Do You Have a Pricing Opportunity?

Understanding if you have a pricing problem

When we engage with retail prospects for our services, the first thing I usually ask is, “Are you setting prices in a spreadsheet?”  If the answer is yes, likely you are experiencing challenges with your ERP or Host Merchandising system that were insurmountable and lead you to externalizing price setting through a spreadsheet.  Often it is not just a single spreadsheet, but many spreadsheets that must work in unison to deliver prices.

Spreadsheets aren’t the only cause of pricing problems.  In one case, we saw a retailer that has an optimization tool, but can’t reliably get prices down to stores.  This is a price execution problem rather than price setting.  Regardless of the reason, prices have to be set and sent to where customers can see them and problems can arise along the way that ultimately impact your profit.

If you suspect you might have pricing problems, how do you find out?  We typically start analyzing where errors are the most obvious.  At the highest level, commercial companies sell products for a price, customers buy the products, and you can check if the price for which you sold the product is the same as what you expected.  The specific points where errors occur differs by industry but for retail, you can typically start with:

  • Store operations. Store operations will typically be notified by sales associates when prices are wrong.  Track how many wrong prices are reported per week and which stores have errors.
  • Take a sample of orders and re-price them.  A random sample will give you an indicator of if there are pricing errors.

You can then quantify the financial impact of the price errors.  Products can be overpriced or underpriced and you can calculate the difference from the actual price.  If it is overpriced you risk losing a sale. You can also undermine customer satisfaction but that is tougher to quantify.  An underpriced product will impact profit and is easily calculated.  Secondly, when you identify pricing errors it takes time and effort to correct the price.  This has a cost in that an employee must analyze the error, correct it and move it through the systems to eCommerce or the POS.  For example, you can review the analysis from the metals company we worked with years ago.  Even though it has been 15 years, many companies still manually set prices in spreadsheets and experience similar error rates.

Once you realize how much price errors cost, you can figure out how to correct them by reviewing the process.  The metals producer mentioned above found they had a 5% error rate on all invoices.  This error rate is not uncommon when there are manual steps involved.  They analyzed the process from start to finish.  We used a similar approach and mapped it to fashion customers where we’ve seen a multi-step process that includes:

  1. Merchant sets initial regular price
  2. Merchant sets a calendar for promotions
  3. Merchant defines promotions and a pricing administrative team executes them
  4. Merchant sets mark down cadence for the season and pricing administrative team executes
  5. Prices are sent to store
  6. Store associate moves items and tags them

Where can this process go wrong?  It’s best to do a thorough review of the process.  What we’ve done in the past is to look at each step, interview the people doing the task and map out the steps and tools.  For fashion, here are typical areas of opportunity:

Merchant sets initial regular price.  When a product is introduced, the merchant sets the regular price.  They typically define price points to target and then assign specific styles to each price point.  A style is broken down into style-color-size combinations.  This explosion of permutations is where the process gets cumbersome.  For the most part, to make it easier, a style is generally priced the same but there are exceptions for size and color.  Then, if you’re dealing with multiple currencies the process expands for each of the countries you’re dealing with.  Price errors can occur when products aren’t mapped correctly or while converting prices for different countries.

Merchant sets calendar for promotions.  The promotion calendar is built for the season but initially specific promotions are only defined at a high level.  I’m calling out this step because merchants use it for planning but they’re not assigning the specific promotion yet.

Merchant sets promotion.  When the promotional event is closer, the merchant will set the promotions.  This can be specific discounts for a category, price points for a set of items, buy X get Y, or anything else a merchant can dream up.  Usually, a merchant will define these in as much detail they can within a spreadsheet.  Then, they hand it off to a pricing administrative team for execution.  The interpretation between what the merchant wants and what the administrator enters can be a source of errors.  For example, the merchant may inadvertently copy a style from a previous promotion or make an error while assigning a given item to a promotion.  The pricing administrator may be able to catch the errors, but some errors will slip through.

Merchant sets mark down cadence.  Depending on how a product is selling and how much inventory is left, merchants will set mark down cadence.  These are hard marks geared towards optimally selling through the inventory by the end of the season.  The use of separate systems for planning and executing these markdowns can lead to errors. Individual styles are marked down based on manual analysis or an optimization algorithm.  If there is no systematic hand off between setting the prices and executing on them, problems can occur.

Prices are sent to the store.  Once prices are set, they must be transmitted to the stores.  Given that each store has their own point of sale system and might have different prices, promotions, or markdowns, most stores get their own set of prices and rules.  For a large retailer, there can be 400 or more individual systems.  Each system is a potential failure point given that the POS must receive the prices, load them, and pull in the rules.

Store personnel moves items and tags them.  In parallel, promotional sheets are provided to the stores for product placement, promotional signage, and prices.  Any number of issues can occur here. Tight coordination is required at each store to insure the prices are correct on the tags and the products are in the right spot for the given promotion.

Reviewing this process can reveal areas of opportunity to plug the holes.  In this example, an up front system that allows the merchant to directly enter promotions or price changes directly would eliminate the opportunity for confusion with the price administrator.  A system to quickly and reliably transmit the calculated prices to the POS might be needed.  Alternatively, the POS could call out to a central pricing system which would eliminate the need to transmit the price data.  The promotional and placement sheets that store personnel use could be generated out of the price execution system rather than being created manually.  In some cases, electronic tags could be used.  Regardless, once we’ve identified the most egregious spots we tackle them first, then move to the next ones.  Typically, the solution relies on systems that can keep all the relationships in sync.  It usually includes better processes as well.  We look forward to learning about your specific processes and how we can help improve them.

Pricing errors leak profit

Pricing errors leak profits and they could be dramatically reduced with some effort.  Companies that have straightforward list prices are much easier to manage then when companies negotiate complex contracts.  Pricing errors are common place when companies negotiate regularly because there are so many exceptions to prices and conditions that sales people agree to which must first be put into a contract and second either executed by sales reps taking orders or automated into a system.  This exception process leads to a lot of errors.

Our customers cut across many different industries and these issues are prolific whether you are in metals, high tech, insurance, or other business to business situations.  One of our customers in the metals industry performed an extensive Six Sigma study prior to engaging us.  The study found their sales process and inter-communication caused thousands of problems a year.  The quantifiable errors cost them $1.4M a year and the upside was most likely $5M – $7M a year.

In Six Sigma, it is critical to have a well defined business case that outlines the purpose of the project as well as a goal statement that addresses the business case.  For the customer, the business case was obvious:

  • $750 million in sales, 60 thousand invoices, 3 thousand discrepancies.
  • No system in place to verify accuracy of contract and pricing for orders.
  • Loss of revenue, customer confidence, control of pricing in marketplace.

And the resulting goal statement was defined as:

  • Improve competitive pricing and invoice system for quarterly savings of $350K.

The customer’s first priority was reducing pricing errors in accordance with the business case and goal statement.  Today’s market requires more extensive information on invoices than in prior years including the price and how the final price is derived.  Their price components were base price, alloy surcharges, scrap surcharges, freight, freight equalization, and fuel surcharges.  Each one of these components were either contract values or tied to a monthly index average.  The steps for calculating and looking up values produced significant errors as shown in Table 1.

 Total InvoicesPrice ErrorsInput ErrorsRetro PriceLate Price SheetIncorrect Price Sheet
Plant 156122552193217
Plant 239312006
Plant 3489220231103133
Plant 44112488445106
Plant 5221292746155
Total17217534460247617
% of Reasons 43.80%2.60%3.50%14.40%35.90%
% of Invoices4.20%1.80%0.10%0.10%0.60%1.50%
Avg cost/yr to correct$348,503.00$152,483.00$8,910.00$12,150.00$50,018.00$124,943.00

Table 1

This table looks at invoice discrepancies only when payments received did not match the invoiced amount.  The number of invoice discrepancies were 4.2% of the total 60,000 invoices per year.  Errors were broken down into five categories including:

  • Price Errors, which were mis-calculations or incorrect lookups of price components.
  • Input Errors, which occured when transposing prices from spreadsheet calculations to the invoicing system.
  • Retro Errors, which were caught after the fact and changed.
  • Late Price Sheet, which occured when sales people failed to get price changes in on time before invoices were set out.
  • Incorrect Price Sheets, which were spreadsheets maintained by sales people that had errors or were interpreted incorrectly.

We worked with them to eliminate all of these errors by using a centralized pricing structure and automatically generating price sheets.  Sales people used the tool which had a similar interface as a spreadsheet which they were familiar with and prices were automatically stored in the central system.  So, when invoicing needed prices, they were always up to date and interpretation disappeared.

It was interesting to note in the study that more often than not, customers notified them when they thought they were over-billed, but customer reported under-billing was almost non-existent as shown in Table 2.

 JanuaryFebruary
LocationOverUnderOverUnder
Plant 1$39,867.13$0.00$6,965.65-$232.35
Plant 2$20,895.40-$2,171.66$5,842.22$0.00
Plant 3$29,933.82$0.00$3,282.29$0.00
Plant 4$13,555.53$0.00$8,392.71$0.00
Plant 5$1,700.74$0.00$87.73$0.00
Monthly Total$108,124.28-$2,171.66$24,803.08-$232.35
Running Total$108,124.28-$2,171.66$132,927.36-$2,404.14
Annualized Rate$1,297,491.36-$26,059.92$797,564.16-$14,424.84

Table 2

Statistically, the over and under billing errors should have been similar.  In the customer’s case, only one customer actually called back when he was under-billed.

As mentioned previously, with the new system the prices were correct the first time.  When we piloted the system, they checked 5 customer’s prices for a month period and found over $77,000 in under-billing that would have previously gone unnoticed.

The Six Sigma study also took an in depth look at why errors occurred.  The source of errors fell into the six bins shown in Illustration 2.  The main source of errors was the loose price sheet document, which they called a CPF.  This document was managed by sales people, but with little structure.  Sales is a creative process so sales people needed flexibility in managing customers, but the loose form significantly contributed to errors.

Market EnvironmentCPF DocumentPricing System ErrorsCPF InterpretationTime of Order vs Time of Shipping
1. No time for detailed price agreement with customer1. No requirement for documenting agreement with customer1. Same person not always available to price1. Long learning curve for interpreting CPFs1. Lack of need for documented agreement with customer for specific delivered price
2. Customer’s system is not compatable with CPF2. No time for detailed price agreement2. Input error prone2. Complexity of CPF, agreement, non-standard info sources2. No comparison of pricing to customer PO
3. Information not received in a timely fashion3. CPF constructed several days after agreement3. Illegible, handwritten info3. Inconsistent interpretation of CPF and add-ons3. Price is optional part of order entry
4. Amendments not received4. Pricing using wrong sheet4. Calculation errors4. Same person not always available4. Price at order entry not transferred to pricing
5. CPF not updated5. Information not received in a timely fashion5. Transposition errors5. Didn’t capture info correctly from CPF5. No checks on previous billing history
6. Information not available on time6. Amendments not received6. Employees time constrained6. Missed charging for extras 
7. No verification process with customer7. CPF not updated7. Documents hard to read7. Misread the CPF 
 8. Format of CPF not compatible with pricing needs8. Information not available on time8. Working from the wrong sheet, line, or column 
 9. Information not available on time9. No controls to ensure correct info is being used9. No controls on using wrong infor from CPF 
 10. Price protected orders/shipments hard to ID   
 11. No controls on what ammendments are used for pricing   
 12. No controls on effective dates   

We were able to address the majority of the error sources discussed above without interfering with the creativity of the sales people.  By centralizing pricing, standardizing calculation methods, and tables, we brought structure to the process but allowed sales people to customize price sheets according to individual needs.

Metals pricing is particularly complicated and if you’re going to tackle your own pricing errors, you don’t necessarily need to do a Six Sigma study before you start.  It certainly helps to unearth the estimated savings, but you probably already have a gut feel for where the leaks are coming from.  The first thing to do is get a handle on all the contracts floating around and the special conditions.  You can segment these, put them in spreadsheets and start from there.  Once you have the the special conditions isolated, you can put together a framework for exceptions that captures the majority of the conditions.  Then, you can offer those exceptions as the only exceptions you’ll allow from sales reps.  Giving reps some freedom within a process gives them some negotiating latitude that you can put into a system to cut down on errors.  It’s not simple, but it can be done and will help increase profit.

Pricing errors take time to fix

One of our customers, a global clothing and accessory retailer, was looking for a more effective way to manage their prices.  Competitive threats precipitated the need to change prices frequently which stressed their existing process.  Their merchandising and pricing teams struggled with correcting price mistakes quickly and identifying where errors occurred.  Their process was caught in a cumbersome coordination between their host merchandising, spreadsheets, eCommerce, and Point of Sale systems.  The system we implemented made the process more effective, improved the speed at which they could respond to price mistakes, and gave them visibility to where the errors were happening.  Below is a review the benefits they received and how we helped them.

Business Case.  It is critical to have a well-defined business case that outlines the purpose of the project as well as a goal statement that addresses the business case.  In this case, the objectives were clear:

  • Ability to react quickly and flexibly to local market conditions
  • Correct mistakes faster through direct integration into downstream systems
  • Identify problems faster with better visibility into where the errors occurred
  • Consolidate pricing activities into a single system of record

Better flexibility in local markets.  As the competitive landscape changed, our customer needed the ability to change prices easily across local markets.  While price changes were possible in their previous process, a lot of manual effort was required.  Through the new tool and process we implemented, merchandisers were given the flexibility to change hard marks, sale, clearance, and promotional prices for any product and store combination.  This laid the foundation to rapidly change prices.  All prices are managed centrally and then individual files are generated for each store or the eCommerce site.  In the future, they may take advantage of real time APIs which would allow systems to immediately receive price updates without any delay.

Correct mistakes faster.  Correcting mistakes faster was a top priority in accordance with their business case.  Today’s retailers must have accurate pricing and be able to react quickly to errors.  The previous process would take about 2 to 2.5 hours to update mistakes or simply send out midday updates.  With the new solution the time was slashed to 20 minutes.  The previous process went through several steps with intermediary systems.  Now, they are able to generate the price change directly for the given stores and distribute the files immediately which are then transferred to the POS.

Gaining visibility to pricing outcomes.  Prices were buried in spreadsheets and often it was difficult to determine the actual effective price given overlapping hard marks, promotions, and stackable coupons.  In many organizations different people are responsible for merchandising and marketing and the ultimate margin is estimated until sales data is returned.  With the new tool, users are able to see how the prices were built, who created the promotion or coupon, and when it is effective.  The price administrators are able to search across the time horizon to see if a future price change will affect their expected margins.  Prior to the new process when a store recognized a price was wrong, they would notify the business who would then go through a flurry of emails to figure out where the error occurred.  Now, the pricing team is able to look up the item, find out exactly which promotions are applied, and correct the error quickly.

Consolidating pricing activities.  In the previous pricing process, activities were split between the host merchandising system, spreadsheets, and a separate system for multi-item deals.  Our customer wanted to consolidate those functions to have a single system for hard marks, clearance, sale, promotions, and deals.  They were able to do that through the new system which allows them to manage their prices and then distribute to their various channels.

Future considerations.  Looking into the future, our customer will be able to move price entry into the hands of the merchants rather than having a dedicated team for price entry.  This will allow the pricing team to focus on more strategic initiatives.  The next area of focus is store communications.  They manually create a document for store managers that tells them price changes and product placement.  With the addition of product placement information, the new solution will automatically generate this document.  This will streamline the process the merchandisers do to get information to the stores.  Finally, they are considering an Asia Pacific rollout and real time connections to systems to cut the response time further.

We were able to address the issues discussed above working with a cross functional team of merchants, eCommerce, IT staff, and pricing managers.  Working with these teams, we identified the critical issues with the process and implemented new capabilities that ultimately saved them time and money.

Preparing for an optimization opportunity

Pricing optimization is one of the best tools you have at your disposal to increase your profit.  Studies have shown base price optimization can yield an increase of 2% – 5% in margin, promotional optimization can yield 5% – 20% and mark down can lead to a 6% – 10% improvement.  That is too great of an opportunity to ignore.  If you are not using science and have a good amount of transaction data, then you could almost certainly benefit from using optimization.  If you are considering optimization, you can take steps to make sure you are fully prepared to take advantage of the solutions.

First, a brief explanation.  Products go through different lifecycles which closely tracks with what types of algorithms you can use to optimize prices.  Many products adhere to a lifecycle where the product is introduced, then sales increase, eventually even out, and finally decreases at the end of life as inventory is sold through.  Each stage in the product lifecycle requires different optimization techniques.  The initial and day-to-day price is established at introduction and you monitor performance for a period of time before using promotions to increase sales and profit.  The initial price can be optimized but is typically bounded by constraints and business rules you have which limits optimization.  Promotions allow more freedom in using elasticity to understand what the best price is and mark down optimizes your sell through.

The general barometer mentioned above is valid in most cases and you can do some high level analysis to determine what benefits you can achieve, but truthfully if there is an opportunity you won’t be able to realize the benefit unless you can actually do the optimization.  So, instead of discussing a process for estimating the opportunity, we’ll discuss how you can figure out if you can unlock the opportunity.

For optimization to work, you must have enough data and price variation.  The data elements needed depends on the type of optimization you are doing but you always need base data like products, location, and sales history.  You might also need things like inventory positions, marketing instruments, cost, and promotions.  Below we explain what each element is and how it relates to the specific optimization:

Sale price.  It is important to have the price the customer sees when they make a purchase.  As simple as this sounds, its sometimes difficult for companies to get this price.  For example, if you’re a manufacturer, distributor or any other entity that does not have control over the final price the customer sees, it may be difficult to get it.  Retailers have the transaction data, but in many cases the data isn’t clean and needs to be fixed.

Number of units sold.  The transaction data will also include the number of units sold per location.  Number of units sold and the sale price are the foundation of your historical data which is used in the forecast.  If you can’t get the number of units sold, you can possibly get the number of units shipped to a given location.  This isn’t ideal, but it’s better than nothing.

Price variation.  Sometimes it is difficult to get enough data to build an accurate demand curve.  But you can get it good enough then use basic analysis to set your price.  Price variation can come from many different sources such as discounts, coupons, and price errors.  It is essential to know the regular price, the promotional price, and the date range when the price was in effect.

Cost.  When optimizing for profit, you’ll need to know how much you paid for it.

Marketing instruments.  The marketing instrument used can influence the effectiveness of the promotion significantly.  When capturing the price variation, it is important to know exactly what instrument was used because not all instruments are created equal.

Competitive prices.  If you are in competitive markets, you’ll need the prices these competitors and the proximity to your stores.  The same is true for your eCommerce channel.  This data is tied to business rules which drive day to day pricing.

Inventory.  If you’re trying to do mark down optimization, you’ll need inventory positions at each location including stores or distribution centers.  Inventory would also include any future buys that have been made already.

Not all of this data is necessary to get started with optimization and you can add new data streams after your initial dip into the optimization pool.  The basis for optimization is the forecast.  If you don’t have enough price variation or data, you may need to substitute similar products, aggregate at a higher level, or use other forecasting techniques to get an accurate picture of demand.  When you are trying to evaluate whether or not you can do optimization, the data is analyzed to see if there is enough to feel statistically confident.

When you’ve verified you can do optimization, what is all this data used for?  For day-to-day pricing, a lot of the prices are dictated by business rules.  These typically restrict the prices in a narrow band based on competitive products, target price points, and other factors.  After that there is a small amount of room to maneuver using price elasticity.  Promotions have more latitude in using price elasticity and also consider cannibalization and halo effect from other products.  Finally, mark downs are constrained by available inventory and try to maximize your sell throughs based on your business goals.

If you pass the litmus test for having the data, you have an opportunity.  The next step is to go through the process of collecting, cleansing, and preparing the data for an optimization tool so that you can unlock the potential benefits.  In a future article, we will discuss how you use this data in each of the different types of optimization.

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How Do You Get a Pricing Project Started?

Navigating a Pricing Project

If you’ve done your homework and are confident you have a pricing opportunity, it’s time to start thinking about a project.  Depending on what you’re doing, the project could be extensive or it could be a quick hit.  We’ll cover scoping the project in the next articles, but first some things to avoid.  Pricing projects, like any enterprise project, are subject to similar pitfalls that can be avoided or mitigated to ensure a successful project.  In our experience, here are some of them:

  • No executive sponsorship. Pricing projects are resource intensive and touch a lot of parts in an organization.  Without executive sponsorship, these projects rarely have a chance.  Clear leadership helps align key resources that need to contribute on the project and ensures you have their attention.
  • Competing priorities. In one company, the leadership had made a decision to go with one software solution but the project team didn’t support the decision.  The project team worked with the product and halfheartedly attempted to get it working but ultimately opted to abandon the solution for their preference of a custom-built solution in contrast to the leadership’s direction.
  • Too many cooks in the kitchen. Without executive sponsorship and a clear direction, different factions in the organization align towards competing objectives.  Then, they’re compelled to ‘right the ship’ in accordance to their own objectives.
  • Underestimating the scope. In another case, the level of effort for a pricing project was way under estimated because the scope had not been defined.  The inexperienced leadership made knee jerk decisions on timelines in contrast to the advice of the more experienced team members.  Pricing projects need well defined requirements and typically require a lot of integration which takes time.
  • Data availability. Pricing projects require data.  Ultimately a pricing solution is a calculator – it brings data in, calculates and computes, then sends data downstream.  If you’re not prepared to get the data out of your systems, don’t start the pricing project.  As part of a readiness exercise, you might want to consider a master data management project or something similar to ensure data is available.
  • Limited business experience from implementers or no technical expertise in business owners. Someone on the team needs to bridge the gap between the business side and technical side.  These are two different languages and unless someone translates, you won’t end up with what you want.  There are often tradeoffs in implementations and if the technical folks don’t understand the business benefits or the business folks don’t understand the difficulty of implementing features then you can end up with an end result that misses the mark or cost overruns.
  • Ill-defined benefits. This is the big one.  When benefits are clearly defined, everything else follows.  Leaders support strong business benefits and competing priorities fade away.

These projects are typically transformational and affect a large part of your sales organization.  Because of that, they really need strategic sponsors at the executive level in a company.  They aren’t easy and the process changes that permeate after implementing these solutions are as complicated as the technical challenges.  Ignoring this reality just puts the project at risk.

All parties from the top down need to be in alignment.  If any link in the chain isn’t on board, it will again jeopardize the project.  This does not mean suppress critical thinking or challenges to the majority opinion, but there needs to be a set of strategic goals that everyone agrees with so everyone is marching in the same direction.  Here are some steps to take to mitigate the above risks prior to starting the project:

  • Clearly define the business benefits. This is one of most important things to do when starting a project.  The business benefits guide the project and ensure when you have disagreement you can balance the discussion against the benefits you are trying to deliver.  In addition, as the project progresses you should measure the business benefits achieved and evangelize the results with business owners and executives.  On the flip side, if it is not achieving the expected business benefits, realign towards them, revaluate, or cut your losses.
  • Align the business leadership. Once the business benefits are defined, it is easier to get an executive sponsor.  The executive sponsor should support the business case whole heartedly.  If the executive sponsor has a lukewarm feeling towards the business case, he or she is less likely to be the evangelist you will need with the other company leaders.
  • Always listen to the end users. Success lives and dies with the users.  If they don’t accept the solution or it is too difficult to use, they won’t adopt it.  In our projects we rapidly prototype and regularly demonstrate the results to the end users to solicit feedback.  You run the risk of getting additional scope, but this can be managed by putting it in the queue and aligning to the business priorities.

Keeping these risks and mitigation points in mind when embarking on a project is important.  In the next articles, we will walk through project planning and scoping.

Planning a Pricing Project

Structurally, pricing projects aren’t much different than most enterprise software projects. Planning is the key to success.  The first step is to work with the client team to develop an implementation strategy that works for all parties. The plan would have a firm scope for the first phase and potential scope for subsequent phases.  This allows you to be agile as new information and situations come to light.

An implementation strategy will help guide the project and should address the following:

  • Document the expected drivers of benefit and the changes that enable and sustain those benefits
  • Define the business functions each application will cover during each phase
  • Define the data flows and integration methods between the applications for each phase
  • Assess the risks and plan mitigations to keep the project on track

In addition, implementation success factors should be discussed.  Some of the factors that might be included are:

  • Involve key business users throughout the project. Users aid in defining requirements, setting scope, reviewing prototypes, performing acceptance testing, training other team members.
  • Keep implementation phases small. Phases should focus on a “minimally viable product” approach to manage risk and maximize the opportunity to learn and adjust as you go.
  • Maintain a consistent team from start to finish. This allows you to maintain institutional knowledge, minimize handoffs, more effectively support live issues and modify earlier work when needed.

Next, what are the pricing pain points you are trying to fix?  When we identify the pain points, we will also estimate the financial benefit or productivity gain expected from addressing the issue.  If you went through the process of identifying your pricing errors and analyzed the causes, you will have a good idea of what your pain points are.  Here are some typical pricing pain points that we have seen in implementations:

  • Prices aren’t making it to stores quickly and reliably
  • Customers are demanding instant access to their loyalty points and stored coupons
  • Network connections are unreliable to the stores and often go down
  • There are potential price conflicts or margin leaks between price changes, promotions, markdowns, and coupons
  • Prices are managed in complex spreadsheets and there is an associated risk of making pricing errors often with the processes
  • The need to keep prices and promotions across channels aligned

These are some example of pain points that might exist in an organization.  As mentioned, this list can be distilled from the analysis exercise of determining where your pricing errors are coming from.  The list might also contain tangential pain points that aren’t directly causing pricing errors, but do cause frustration with the team that manages prices.

Rather than a big bang approach we subscribe to many different business releases that focus on key functionality.  But where do you start?  For our projects, we initially use a broad brush to identify how complex the different features are and balance that against the pain points.  A business release would have well defined benefits that are attributed to specific feature requests.  There are typically many different business areas you could start with for example a specific set of features, brand, geography, set of stores, or channel.  We typically look at the following criteria in deciding where to start:

  • Start with a quick win. Often one of the best places to start is something that would address one or more key pain points and keep the timeline short.  If you sized the complexity of the features and cataloged the pain points, you should be able to gauge what would be a quick win.
  • Understand the quality of the current data sources. Poor data quality is an issue with most projects and can bog down any timeline.  A quick assessment of where and how to get the data, what kind of holes exist, and what kind of transformation should feed into your assessment of complexity.
  • Supportive business group. Another factor is what business groups are supportive of change and have the bandwidth to help drive the early implementations.  If key personnel aren’t available such as business owners, users, or IT staff then your timeline could be in jeopardy.
  • Participation. Determine what level of participation the business can provide during the project.
  • Competing projects. Determine if there are other active projects that would impact the same resources or systems.
  • Other priorities. Are there any business priorities that would need to take into account?  In retail, we often deal with back to school or black Friday and have to plan around those events to ensure we aren’t impacting those critical times.

All of these factors should be taken into consideration when determining where to start.  Once you nail down the initial business release, you can plan it in detail to determine the expected timeline and cost.  At the same time, you should identify the future business releases you expect to follow.  You don’t have to plan the future releases in depth but should have a rough idea of complexity and cost which will drive your overall resource allocation and budget.

Pricing project structure

We find that taking bit sized chunks is best when deploying pricing projects.  As discussed previously, we have a program planning phase where we determine what functionality would go in each business release.  When we know what is in the first business release, we start the project.  We typically use the following framework for a business release:

  • Requirements and blueprinting. Good requirements are essential to the success of the project.  The first step is to comprehensively document the business user requests.  This is followed by detailed user workflows, high-level data flows, and user acceptance tests.  After this is documented, the technical architect and/or developers are consulted to get estimated timelines and balance technical feasibility against business goals.
  • Iterative config and development. Configuration and development are when the business features get codified.  With enterprise software applications there is typically configuration work that tweaks the application to enable the features the business owners would like to see.  There might be data modeling or price modeling that conforms to the needs of the input and output data.  In addition, development work might be needed to fully realize the business owners vision.  In either event, iterative discussions will take place between the architect and the technical implementation team.
  • Integration testing. After configurations or code is released, integration testing is done to finalize the data inputs and outputs and the interfaces that are used.  The bulk of the independent work is done in the previous step and then end to end testing is done here.  This could be flat files, real-time calls, or a bus.  The data could be coming from multiple sources and be sent to multiple destinations.  The complexity of this step could extend the timeline.
  • User acceptance testing. After the integration testing, user acceptance testing is done on the final functionality.  In the config and development step we typically show the business users what will be coming, so they have a preview, then in user acceptance testing they work through the defined test cases.
  • Deployment and post move to production support. The last step is to deploy the functionality to production.  The setup and installation is done prior to this step and here we would move data, code, and configuration to the production systems.

These steps are put into a more detailed plan with a business release taking a few months or longer.  A typical schedule for a pricing project might be as follows:

In conjunction with the plan we develop a resource plan with the mix of team members necessary to deploy the project.  In our projects we use consultants who are experts in the products we implement mixed with part time and full-time resources from the customer.  We typically see the following roles are needed in varying degrees.  On smaller projects, people can wear multiple hats to keep costs down, but larger projects typically require more dedicated roles:

RoleResponsibilities
Project ManagerManaging the project and playing an SA role, coordinating with AAP
Solution ArchitectSolution architect would define and oversee the overall requirements, network architecture, solution approach, and integration architecture
Senior Integration DeveloperIntegration architect would define and develop the necessary integration functionality
Modelling ArchitectManage, define, and document the pricing model
Test ManagerContinuous testing and documentation would be performed throughout the project
Business AnalystAnalyzing requirements, working with modeling architect and integration development to deploy the solution
DeveloperAdditional development to support integration

In addition, the customer typically provides following roles:

RoleResponsibilities
Project ManagerDedicated to the project
Subject Matter ExpertSignificant involvement during program planning, blue printing, and testing phases. Need coverage of all the pricing processes
Legacy Data OwnersNeed resources that understand the format and context of the data from the legacy systems that will drive the pricing process
IntegrationNeed resources to provide sample feeds from legacy systems, participate in the design of interfaces, support integration testing, and operationalize the new feeds
IT OperationsParticipate in the design of the operational processes as the new systems come on line. Batch windows for feeds, SLA’s, backup / restore processes, initial loads, net change feeds, etc
UsersParticipate heavily in UAT and as needed during blueprint and config / test phase
Steering CommitteeProvide guidance during program planning and a monthly cadence to review progress and resolve management level issues

The plan is an union of required business functionality, success factors, resources, budget, and timelines.  Once this is done, it is time to start implementing.

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How Do You Implement a Pricing Service

Centralized pricing service

In our consulting practice, we speak to many different retailers about their pricing needs.  Recently, the requests we have fielded are trending towards what I would term a centralized pricing service.  For many years across all industries the trend has been towards specialized services and we seem to have hit that point with pricing in retail.

In retail, we often see three systems that work in conjunction to deliver prices to customers: ERP (Host Merchandising), POS and eCommerce.  Prices come from downstream and are aggregated or augmented in the ERP system and are sent out to POS and eCommerce separately.  What retailers have found is that the ERP isn’t a very effective tool for managing prices, so they end up externalizing the pricing process in spreadsheets or custom systems.

This is because pricing sits in the void between eCommerce, POS, and ERP / Host Merchandising.  Many off the shelf and homegrown eCommerce solutions struggle to handle the volume of data associated with the permutations between channel and location.  POS is typically segmented for a single store and ERPs struggle to handle the transaction speed necessary for supporting real time or mass calculations in a timely manner.  This leaves enterprise retail pricing out in the cold with a hodgepodge of spreadsheets and custom solutions.

What is driving the need for a centralized pricing service?

  • Consistent pricing. Customers are demanding that retailers give them consistent prices on the internet and the store.
  • Amazon.  Grocery and fashion retailers watched as Amazon decimated other retailers and realize they have to make a change.
  • Hyper personalized offers. What used to work as location based offers don’t make sense with multiple channels, so retailers are starting to tie offers to specific customers to inspire loyalty.

Consistent pricing.  In many retailers, the eCommerce and POS are typically two disparate systems often with different functionality.  Maintaining consistency becomes an exercise in custom code or manual processes that break down.  Customers don’t really care what your internal issues are, they just want to be able to go online, see a price, then go into a store and get the same price.  And if they have a special deal because they’re a loyal customer, they want to get that same price in the store that they would receive online.  It’s not a new or unusual request and it’s been an issue in retail for years.  Customers are now getting frustrated and expect it.

Amazon.  Amazon has been dabbling in grocery and fashion for years now.  Then, they bought Whole Foods and are smack dab in the middle of grocery.  At the 2018 SXSW I was in a session where the CTO of Amazon Fashion stood up and questioned a leading fashion retailer.  They’re watching, learning, and getting better.  It’s inevitable that they will figure it out and retailers need to be prepared.  Of course, it’s not just Amazon, grocery and fashion have always had competitive threats.  It’s just more pronounced with Amazon encroaching.  Competitive and consistent pricing is one way to combat this threat.

Hyper personalized offers.  As competition closes in, another tool to entice customers to continue shopping with you is personalized offers.  In the past, retailers could offer location based or general coupons for customers.  Entrepreneurial affiliates on the internet have rendered general coupons a shared secret that serve to simply lower margins rather than inspire loyalty.  Retailers have since turned to coupons or offers that are tied to a particular customer.  On top of that, hyper personalized offers push existing systems to their breaking point.

These are just three of the most prominent complexities that are difficult to address with current solutions.  So, what would you need from centralized pricing service?

  • Fast. If you’re generating files for POS then it has to calculate potentially millions of price changes quickly and if you’re servicing internet requests, it needs to have fast response time.
  • Real time and batch interfaces. To serve different channel needs, the system needs to allow real time or batch interfaces.  In some cases, some retailers are seriously considering real time interfaces from the POS which would negate the need for batch.
  • Pricing system of record. A centralized pricing service needs to be the pricing system of record including day to day pricing, mark downs, promotions, coupons, contracts, and all the history.

Fast.  Whether you are enabling real time connections to your POS or generating files that will be distributed to your POS, a centralized pricing solution needs to be fast.  Retailers that have hundreds of stores with localized prices can easily scale to millions of calculations.  The system needs to be fast so you’re not waiting hours to get your prices out.  Without a pricing service, ERP typically shoulders that burden and given the number of calculations needed would take hours to process rendering the ERP unusable during that time.

Real time interfaces.  A centralized pricing service would be used for POS, eCommerce, and funneling prices back into your ERP or Host Merchandising system for financial calculations.  If your infrastructure can handle it, real time interfaces are the best way to go because then you have the right price from your pricing system of record.

Pricing system of record.  If you have a centralized pricing service, it needs to handle all pricing requirements.  This includes day to day pricing in grocery, regular price in fashion, hard marks, promotions and coupons.  Each of these are different events that change the price. They need to be tracked and historical records kept so that you can reconstruct the price at any point in time.  In addition, some retailers have b2b contracts with customers, so the system needs to handle customer pricing for individual products or groups of products.

In conclusion, if you’re finding pricing is spread across several different systems, you’re having to piece it together and you aren’t sure if your POS prices match your eCommerce prices it might be time to consider a centralized pricing service.  Leading retailers are trending in this direction and the flexibility a pricing service offers is tantamount to their success.

Pricing service deployment approach

Retail customers have different requirements on how their pricing execution service should behave.  Some need it to feed their POS and eCommerce systems whereas others require real-time connections to improve functionality in limited POS systems.  The approaches we use are centralized and distributed which translates to real-time execution or batch processing respectively.  The decision on which to use depends on many factors including the customer’s intended use of the service, the levels of maturity of the IT infrastructure, and availability of staff.  Both approaches have merits and customers must weigh the trade-offs before committing to an approach.

What is the difference between the two?  Distributed is when you pre-process most of the prices, store them in a repository such as files or a database table, and then send them to the remote system.  This allows central calculation of most of the prices with minimal additional calculations at the local level.  Real-time is a service where the function of calculations is offloaded to the external system and accessed through callouts.  The advantages and disadvantages of each approach are enumerated below.

 AdvantagesDisadvantages
Distributed·       Utilize existing price distribution infrastructure·       Known file interfaces·       Utilize existing operational procedures·       Price authoring infrastructure simpler ·       Less flexibility in promotions·       No centralized validation for coupons or loyalty discounts·       Potentially longer deployment time for prices 
Centralized·       Easier consistency across channels·       Quicker deployment time for prices·       Real-time validation of coupons and other promotions ·       More complicated infrastructure·       More difficult integration 

Often, it might not be a one approach fits all and you can employ both approaches.  For example, in hardlines a distributed file would need to be sent to the price tagging system and real-time could be used online for pick up at the curb whereas the POS would also require a price file.  What are the reasons to use one approach versus the other?  We help customers make the decision by asking pointed questions such as:

  • How complicated are your promotions?
  • Do you want promotions that are more complicated than your POS can handle?
  • How much latency does your network have?
  • Can your POS handle real-time callouts?
  • How reliable is your network?

For example, you might use a decision tree like this:

This is just one example of how you arrive at the decision.  Other factors might be budget, availability of staff, and any number of competing projects that would have an impact on a pricing service implementation.

We have seen an important motivation for using batch processing is that many retailers don’t trust their network.  They feel they would like to use real time but their network isn’t reliable enough across all stores, so they need a local presence that can calculate the prices.

Here are some other reasons that would tip the scales one direction or the other:

  • Complicated promotions: Complexity of promotions is a factor that could lead a company down the real-time path.  For example, if promotions are simple, then it might make sense to pre-process them and send them to the stores in batch.  If they are complicated, you might be better off calling real-time because the work involved updating the POS system would be better spent integrating to a centralized pricing system.
  • Network reliability: Many retailers are reluctant to use real time connections because they trust their network 99% of the time, but not 100% and the slim chance that the network might go down precludes them from deciding on real time connections.  There are hybrid approaches where most of the pricing information is sent to the POS and in case of a network disruption the POS can fail over to the local price files.  However, as more and more critical services require reliable connections, the decision to migrate to real-time becomes easier for retailers.
  • Validated coupons: Another point of consideration is any promotion that requires validation.  For example, single use or customer tied coupons need a centralized system to ensure the coupon hasn’t been used before in another store or a different channel.  This can be coupled with a distributed approach for standard promotions and prices so that a real-time callout is only necessary when validating.
  • Utilizing existing infrastructure: When determining an approach, sometimes it is easier to get started by using the existing infrastructure and replacing the back end price authoring environment.  This might lead customers to choose batch processing where they would be able to duplicate the existing files and limit changes to the distribution framework.  By doing this, they would limit the impact on IT given the required code changes, testing, and system upgrades.

In both cases when implementing a pricing service, customers gain centralized control of pricing which leads to reduced errors, better traceability, and quicker execution.  Ultimately, a customer will evaluate the objectives of their pricing service which will dictate the approach.  Once that decision is made then we focus on the final network topology which we will discuss in the next article.

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Why is Pricing Excellence Important?

The Need for Better Pricing

One of our customers eloquently said “If it ain’t broke, fix it anyway.”  She was talking about pricing.  She had inherited a pricing process from a former colleague that had to leave suddenly and had to get up to speed quickly.  The process worked, but it was terribly inefficient and error prone.

What she was getting at was that she saw an internal opportunity to fix her pricing process and she recognized there where competitive threats on the horizon they were going to need to deal with.  She needed to do something.  The company’s margins were doing alright.  They were consolidating and adjusting the mix of their stores, but overall doing ok.  A new CEO, however, wanted more flexibility with pricing at the store level and she knew they would crack under the weight of the internal processes needed to support that level of pricing.

What precipitated the CEO’s desire for more local pricing?  I didn’t talk to him about it and don’t have telepathy, so I can only reason that the conversation in his head went something like this:

  • My margins are falling. Why?
    • There is a slowdown in retail
    • We also have a new competitor in the market
  • OK, what do I do?
    • I need to innovate or make myself more efficient
  • OK, how?
    • I have more inventory than I need at some stores, what if I allocate products better or improve my distribution?
    • Do I need to forecast better so that I have a better understanding of what inventory I need?
    • What about more flexibility with pricing at the store level? Localized pricing would help my margins.
  • What now?
    • How about starting with pricing? It would give us a quick win for improving margins and being able to compete
    • What if I shift more sales online? It would help my distribution and inventory, plus my margins are better online since I don’t have full store operations supporting the sales
    • How can I better forecast? A better process and science is the short answer.
    • . . .

You can see where this simplified chain of reasoning is going.

Why were his margins falling?  The dialogue alluded to a few of the possibilities.  It might be an assortment issue or could be too much inventory.  It could have been changing demographics at their store locations leading to under-performance or it could have been the state of the retail industry in general.  The other big possibility was the competitive threat.  Once stores are trimmed the next logical lever to pull is pricing.  Localized pricing is a quick way to improve margins.

What is the competitive threat?  In this case, other brands have always been around, but also Amazon is a major driver in almost every retail industry.  Leading companies know if they don’t adjust, Amazon will just steamroll them.  I saw a video recently that alluded to Amazon researching robots that would make to order any clothes you wanted.  At SXSW a few years ago I listened to a fashion panel where 3 leading retailers discussed the state of the industry.  Their feeling was that Amazon was a marketplace and apparel was an emotional buy.  A member of the audience stood up and started asking really pointed questions, then followed with “full disclosure, I’m the CTO of Amazon Fashion.”  The panelist’s faces when white.  Amazon is watching and learning.

If you wait until you have to do something, it’s probably too late.

Pricing excellence can improve profit

What is pricing excellence?  In our experience it’s a collection of processes, data science, and automation that ensures you have the right price at the right time for you customer.  Is the right price the best price for everyone?  No, it’s the price that balances your business goals against market conditions like when you want to maximize profit using price elasticity.  The right time means that your customers can get a relevant price in any channel they choose whether in a store, on-line, or through a partner eTailer.  Pricing excellence encompasses the ability to optimize your price and ensure that you can deliver those prices to your customers.

For me, my education about pricing excellence started many years ago in one of my first jobs at Trilogy in Austin, TX.  Trilogy was an energetic company with big ideas.  One of which was a ‘pricing engine’ that would dynamically price products, such as computers or automobiles, when a user selected options for the product.  It worked by providing a modeling environment where an administrator could construct the pricing calculations and conditions by which the product was priced without writing software code.

That certainly doesn’t sound particularly innovative today because it is common practice now, but back then many sales people would price in a spreadsheet or worse with a calculator and paper.  The new method did it automatically.  In working with different companies across industries, though, it highlighted that sales people and price administrators made mistakes when they manually calculated prices.  These pricing errors cost the companies money.  Interacting with these clients drove home how important it was to consistently give customers the right price and how important it was to institute a process to achieve pricing excellence.

I was describing pricing errors above, but when talking about pricing excellence many people automatically assume you mean optimization.  And, yes, optimization is a big part of pricing excellence, but the other component is reducing pricing errors and providing a foundation to be able to accept recommendations from an optimization process.  It’s like when I wanted solar panels and thought I could generate all the energy I needed.  Austin Energy would pay for a portion of it but they said they wouldn’t approve funding until I fixed the energy leaks in my house first.  I quickly found I was leaking more energy than I could ever generate, so set down the path of plugging those leaks.  It’s the old metaphor walk before you run and it holds true with pricing excellence as well.

After Trilogy, I continued down the pricing path at i2 (later acquired by JDA, now BlueYonder), wrote a pricing application myself and implemented it for metals companies, then worked as a consultant on a Lenovo eCommerce project.  Through it all, I saw that better pricing provided big benefits to customers.  Now, as the managing partner of K3, I know pricing is an area we can deliver significant value and set out to evangelize the message.  It is an area that many of our customers have yet to exploit.  Some are still using spreadsheets to manage prices and others have unreliable price execution.  Here are some examples of the opportunities that exist:

  • Pricing errors. A steel company I worked with found a 5% error rate in pricing on their invoices which translated to hundreds of thousands of dollars in lost profit.
  • Pricing efficiency. A leading retailer struggled to get prices out the door, often taking 4-8 hours and requiring hours of lost productivity to research the cause of the error.  Here is another example on how pricing efficiency affected a retailer.
  • Liquidating inventory. A customer of one of our technology partners found they could achieve their company goals of liquidating inventory while maximizing profit through better pricing  (will publish details later).
  • Increasing profit. Many years ago, a Harvard business review article Managing Price, Gaining Profit stated a 1% increase in price can yield an 11% gain in profit.  Of course there are a lot of dependencies such as your margin and demand but the point is – good pricing is important.
  • Driving revenue. AMR published a study that found promotions could drive a 1-12% improvement in revenue and a 5-20% improvement in margins.

These are just some examples of why pricing excellence is important.  Bottom line is that pricing errors leak profit and better pricing can improve profit.  Those two statements guide us when we work to deliver value to our customers.  In this blog, we will explore the techniques we use to determine if a company has a pricing problem, how to address the problems in a project, and finally how to lay the foundation for optimization.  I will draw from personal experience, our practice leaders experience, and partners to define the concepts and tease out the details.

Pricing Landscape as we see it

As a consulting company, we generally work with enterprise customers in the $500M to many billion-dollar range.  It’s not that we won’t work with smaller companies, this is just where the majority of our contacts lie given our collective backgrounds in enterprise software and consulting.  We operate mostly on the sell side of enterprise companies implementing eCommerce, master data management, configurators, pricing systems, CRM, and content management solutions and integrating them into our customer’s business processes.  We consistently see that better pricing practices stand out as the area where we can provide the most value to the companies we service.  Specifically, when we talk about Enterprise Pricing we are referring to large companies with complex pricing processes.  Fixing pricing errors and providing the foundation for optimization yields an enormous benefit.

In our work, we span both B2B and B2C pricing. We approach the pricing process differently for these channels.  This is how we view the difference:

B2B.  Business to business commerce is usually done through relationships and typically requires a sales person to negotiate contracts or deals.  A target price or deal envelope can be used to drive sales people to the final price that is in line with a company’s goals.

B2C.  Business to consumer commerce might have floor sales people but typically no negotiation.  B2C pricing is usually done in a back office where a marketing or merchandising team determines the price.  The price is then sent down to stores and the eCommerce site.

Supply chain management commonly refers to the planning funnel which flows from strategy to planning and then execution.  Strategy is focused on activities for the next few years. Planning horizons deal with the next 6-12 months.  Execution focuses on immediate actions taken over the next few weeks.  I like to borrow this terminology when discussing pricing.  In this blog, we will focus on the medium and short-term processes of planning and execution:

Planning.  Planning, aka analysis and optimization, work in conjunction with each other to determine what the right price should be at a given location or for a particular channel.  In B2C, the systems typically employ a forecast that shows both base demand and promotional lift. Price elasticity can be used to analyze secondary impacts of price changes such as cannibalization and halo effects to determine what the right price should be.  B2B planning uses the same techniques, but also provides guidelines in the quoting process that direct the sales people towards the company goals.  These tools also measure performance against those goals.

Execution.  Execution takes the price from planning and gets the price to the place your customer will see it on an eCommerce site or to the POS.  Execution can include systematic checks to ensure that actions taken by the different business functions such as marketing and merchandising don’t conflict.  Also, store managers may need the authority to deal with unknowns such as local competitive actions. The optimized price coming from corporate may need to be overridden. In B2B, execution is really the quoting system.  This is where prices are negotiated, contracts are managed, and price commitments are sent to customers.

When we talk to customers about pricing, one of the initial things we establish is where they fall on the spectrum of pricing needs between B2B and B2C.  Companies that do both typically have different business units that handle marketing and pricing functions for the separate units.  Sometimes the lines can be blurred but in general we see these business functions at the intersection of these categories:

 B2CB2B
PlanningLifecycle analysis and planning including promos, mark downs, and initial pricing.  In general, optimization is based on a forecast which drives a price-elasticity curve to determine the best price.Setting guidelines for sales people and targets that are in line with business goals.  Measuring sales people against those goals.  Setting tier discounts and negotiation parameters.
ExecutionExecution is rules based pricing and verification.  There may be different systems affecting price such as mark downs from merchandising and coupons from marketing.  Execution is where it comes together.Creating contracts, customer specific pricing, enforcing deal envelopes, creating quotes, approval processes, and spot quotes.

In B2C we’ve seen that the execution system is usually different than the planning system, but in B2B we’ve seen the planning and execution systems can be a single system.  I can only venture to guess why this is the case.  B2B systems typically have a lot of interactive users and workflow whereas B2C systems seem to focus more heavily on transaction speed.  I assume there’s enough of a market for these separate business problems that vendors have specialized in one or the other.

For analysis, the dividing line seems to be how much transaction data you have.  When you have enough data, then you can apply science to determine the optimal price and be statistically confident in the recommendations.  If you don’t have enough data, then you employ boundaries and reports to aid negotiation and rely on the sales person to ultimately make the decision.

The typical path we suggest to achieve pricing excellence is to first identify if you have a pricing problem and where you can improve on the process.  For many companies the starting point could be a price execution system to stem the price errors and put controls around how the price is calculated.  If this foundation is in place, you can progress to optimization.

In the next topics, we will cover the process we use to identify pricing problems and how big the opportunity is.  Then, we will discuss how you go about fixing the process.

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Microsoft Fabric: A Good Tool for S&OP

sop microsoft fabric

As discussed in previous articles, Sales and Operations Planning (S&OP) is a critical process for aligning supply chain operations with business goals. Many companies rely on third party S&OP solutions like Blue Yonder, Kinaxis, or o9 Solutions. However, as data pipeline tools become simpler to build and use, organizations may benefit from a custom-built approach.

The reality of S&OP tools is that they are largely OLAP cubes and reports which are virtually customized for every client and follow a traditional data pipeline process as outlined below in the diagram.  That being the case, why would an organization spend thousands on costly licenses when they can utilize a tool like Microsoft Fabric?  Fabric is a unified data and analytics platform that offers an ideal foundation for implementing an S&OP process that is scalable, flexible, and tailored to specific business needs. Microsoft Fabric is well suited for S&OP and organizations can leverage its capabilities to drive better planning, forecasting, and decision-making.  

sop microsoft fabric

What is Microsoft Fabric?

Microsoft Fabric is an end-to-end data platform that integrates multiple Microsoft services, including Azure Data Lake, Power BI, Synapse, and AI-driven analytics, into a single environment. It is designed to break down data silos, enable real-time analytics, and support AI-driven decision-making—all essential for an effective S&OP process.

Key features of Microsoft Fabric include:

  • Lakehouse Architecture: Combines the scalability of a data lake with the structure of a data warehouse.
  • Data Integration & ETL: Supports seamless data ingestion from multiple sources (ERP, CRM, IoT, supply chain systems).
  • AI & Machine Learning: Enables advanced demand forecasting and predictive analytics.
  • Real-Time Analytics: Provides up-to-date insights for agile decision-making.
  • Collaboration & Security: Ensures cross-functional access with enterprise-grade security and governance.

How Microsoft Fabric Supports a Custom S&OP Process

1. Unified Data Lakehouse for S&OP Data Integration

One of the biggest challenges in S&OP is consolidating data from multiple sources—ERP systems (SAP, Oracle, Dynamics 365), CRM, supply chain platforms, and external market data. Microsoft Fabric’s OneLake architecture allows businesses to store and process large datasets efficiently.

  • Centralized Data Storage: Fabric enables companies to create a single source of truth for all S&OP-related data, eliminating silos.
  • Seamless Integration: Native connectors allow real-time data ingestion from various business applications.
  • Structured & Unstructured Data Handling: Whether it’s structured (sales orders, inventory levels) or unstructured (supplier contracts, weather patterns), Fabric can handle both efficiently.

2. Advanced Forecasting & Scenario Planning with AI

Microsoft Fabric also includes AI-powered analytics through Azure Machine Learning and Synapse Data Science. These tools can enhance S&OP by:

  • Demand Forecasting: Using machine learning models to predict demand fluctuations based on historical trends, seasonality, and external market factors.
  • Scenario Analysis: Running multiple “what-if” simulations to assess the impact of supply chain disruptions, pricing changes, or economic shifts.
  • Automated Replenishment & Inventory Optimization: AI-powered insights can help businesses align inventory levels with forecasted demand, reducing excess stock or stockouts.

By leveraging Azure AutoML, companies can develop predictive models without requiring deep data science expertise, making AI-driven S&OP accessible to more organizations.

3. Real-Time Decision-Making with Power BI & Real-Time Analytics

Traditional S&OP processes often rely on static reports, leading to outdated insights. Microsoft Fabric’s real-time analytics capabilities, combined with Power BI, enable continuous monitoring and faster decision-making.

  • Interactive Dashboards: Power BI allows organizations to create real-time dashboards that visualize key S&OP metrics such as demand trends, inventory levels, and production capacity.
  • Automated Alerts: Set up real-time notifications for key events (e.g., supply chain delays, sales spikes, warehouse constraints) to enable proactive adjustments.
  • Collaboration & Accessibility: Power BI reports can be shared across teams, ensuring all stakeholders (sales, finance, operations) are working from the same data.

This real-time visibility helps companies move from reactive to proactive decision-making in their S&OP process.

4. Scalability & Flexibility for Growing Businesses

Unlike pre-packaged S&OP solutions, Microsoft Fabric provides the flexibility to scale and customize the process as business needs evolve.

  • Multi-Tenant Support: Ideal for enterprises managing multiple business units or regions.
  • Custom Workflows: Fabric allows businesses to create S&OP workflows tailored to their specific processes, rather than adapting to rigid software constraints.
  • Hybrid Cloud & On-Prem Support: Organizations can maintain certain workloads on-premises while leveraging Fabric’s cloud-native capabilities.

By adopting Microsoft Fabric, businesses can implement a future-proof S&OP process that grows with them.

Why Microsoft Fabric Over Traditional S&OP Software?

Feature Microsoft Fabric Traditional S&OP Software (e.g., Blue Yonder, Kinaxis)
Data Integration Connects to any data source (ERP, CRM, IoT, market data) Often limited to pre-built ERP integrations
Customizability Fully customizable S&OP workflows Fixed templates with limited flexibility
AI & ML Capabilities Built-in machine learning & AI-powered forecasting AI features may require separate modules
Real-Time Analytics Live dashboards & automated alerts Batch processing with periodic updates
Scalability Scales as business grows Requires costly upgrades for scaling
Cost Structure Pay-as-you-go with cloud-based flexibility Often requires large upfront investment

For businesses that require a custom, scalable, and AI-driven S&OP solution, Microsoft Fabric offers a more flexible and cost-effective alternative compared to traditional software.

How K3 Can Get You Started with Microsoft Fabric for S&OP

If you need to revamp your S&OP process, there is no reason to wait.  Getting started with Microsoft Fabric is straightforward and can be built incrementally.  You can start with a prototype and expand from there.  Here’s how we would get you started:

Step 1: Define Your S&OP RequirementsIdentify key business objectives, data sources, and integration needs.  You would need product data, finance data, supply chain data, and forecast data at a minimum and any other data streams that would help the process.
Step 2: Set Up a Fabric EnvironmentLeverage OneLake for centralized data storage and connect ERP, CRM, and supply chain systems.  Once the data is in, then put data transformation tools in place to curate the data and route information to data cubes.
Step 3: Develop Data Cubes for AnalysisDesign and implement the data cubes based on metrics used in your process like forecast accuracy, inventory turns, days of supply, gross margin return, on time delivery, etc.  
Step 4: Implement Power BI DashboardsCreate real-time S&OP dashboards for visibility across sales, operations, and finance.  The dashboards should support the process you use at your company.  Typical processes cycle through demand, products, supply, and finance integrated at each step, reconciliation, then management review.
Step 5: Optimize & AutomateWith Fabric, you can continuously refine the process with AI-driven recommendations and automation workflows.  Continuous improvement should be integral to the process.

Conclusion

Microsoft Fabric provides a powerful, flexible, and scalable platform for businesses looking to build a custom S&OP process. Its data lakehouse architecture, AI-driven analytics, and real-time capabilities make it an ideal choice for companies that want to move beyond traditional software limitations.

By leveraging Fabric, businesses can gain better visibility, improve forecasting accuracy, and enhance cross-functional collaboration—leading to a more effective and agile S&OP process.

Interested in implementing Microsoft Fabric for your S&OP process? Contact us in the form below to explore how we can help you build a future-proof planning system tailored to your needs.

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Choosing the Right Replenishment Software

replenishment and inventory planning software that focuses on supply chain inventory optimization using BlueYonder (JDA) fulfillment, Kinaxis RapidResponse, and o9 MEIO for the replenishment planning process

Contrasting BlueYonder, Kinaxis, and o9 Solutions for Replenishment Planning Software

Selecting the right software for replenishment planning can significantly impact a company’s supply chain efficiency, cost management, and customer satisfaction. At K3, we focus on 3 leading software providers—BlueYonder, Kinaxis, and o9 Solutions.  Here we will contrast their unique approaches and algorithms to handle various replenishment scenarios. Each of these companies offers powerful tools with different features, catering to different business needs and replenishment complexities.

1. BlueYonder

Overview:

BlueYonder Fulfillment offers a robust and mature suite of supply chain management tools, with a particular focus on replenishment planning and inventory optimization. Their solution is built on a foundation of best-in-class algorithms designed to enhance decision-making capabilities.

Key Features:

  • Demand-Driven Replenishment: BlueYonder’s software uses advanced forecasting algorithms that analyze historical sales data, market trends, and external factors (such as weather patterns and local events) to predict demand more accurately.  Oftentimes BlueYonder’s Demand product is implemented alongside their supply side replenishment, but it does not have to be.
  • Multi-Echelon Inventory Optimization (MEIO): The software supports multi-echelon inventory optimization, balancing stock levels across different locations (warehouses, distribution centers, and stores) to minimize overall costs while maximizing service levels.
  • Multiple Algorithms: BlueYonder offers 4 different algorithms that cater to different replenishment needs including SPARC, DeepTree, MAP, and LP Optimization.
  • Dynamic Safety Stock Calculation: The software dynamically calculates safety stock levels based on real-time demand and supply variability, allowing for more responsive replenishment strategies.

Replenishment Scenarios Supported:

  • Seasonal Demand Fluctuations: Algorithms that adjust inventory based on seasonal changes and promotions.
  • Multi-Location Replenishment: Optimizes inventory across a complex network of locations to ensure balanced stock levels.
  • Demand Uncertainty and Volatility: When integrated with Demand, advanced models predict demand shifts and adjust replenishment strategies accordingly.

2. Kinaxis

Overview:

Kinaxis RapidResponse provides a cloud-based supply chain management platform known for its agility and speed. Its RapidResponse platform is designed to support complex supply chain scenarios, including replenishment planning, by leveraging a single data model that provides real-time visibility and enables concurrent planning.

Key Features:

  • Concurrent Planning: Kinaxis enables concurrent planning, allowing multiple users to collaborate on the same replenishment plan in real time. This approach helps manage complex supply chains by synchronizing plans across different functions.
  • Optimization Algorithms: The software utilizes a mix of heuristic and optimization algorithms to manage replenishment scenarios, such as order quantities, order frequency, and distribution across multiple locations.
  • Scenario Analysis and Simulation: Kinaxis supports “what-if” scenarios to test different replenishment strategies and predict their impact on supply chain performance.
  • Machine Learning Insights: Machine learning models analyze patterns to detect potential disruptions and recommend preventive actions.

Replenishment Scenarios Supported:

  • Highly Volatile Demand: Concurrent planning helps manage sudden demand changes by allowing real-time collaboration and quick decision-making.
  • Multiple Demand Priorities: Algorithms that support prioritizing orders based on demand criticality, customer importance, and profit margins.
  • Capacity and Constraint Management: Models that adjust replenishment strategies based on available production or storage capacity.

3. o9 Solutions

Overview:

o9 MEIO is a newer player in the supply chain management space, known for its integrated planning and decision-making platform. Its AI-powered algorithms provide end-to-end supply chain visibility, from demand forecasting to replenishment planning.

Key Features:

  • Integrated Demand and Supply Planning: The o9 platform combines demand and supply planning in a single interface, allowing for real-time visibility and adjustments.
  • Knowledge Graphs and Machine Learning: o9’s unique approach uses knowledge graphs to map out complex supply chain relationships, supported by machine learning algorithms that enhance forecasting accuracy.
  • Scenario-Based Planning: The platform supports multiple scenarios to simulate different replenishment strategies and their outcomes, helping companies make data-driven decisions.
  • Dynamic Replenishment Planning: Real-time data inputs (such as point-of-sale data, social media trends, and weather forecasts) are used to dynamically adjust replenishment plans.

Replenishment Scenarios Supported:

  • End-to-End Visibility: Provides visibility across the entire supply chain, allowing for precise replenishment planning.
  • Rapid Market Changes: Algorithms that respond quickly to shifts in demand due to market changes, competitor actions, or unforeseen events.
  • Customized Replenishment Rules: Flexible algorithms that can be tailored to specific replenishment rules and business requirements.

Comparing the Algorithms for Replenishment Scenarios

Feature BlueYonder Kinaxis o9 Solutions
Approach Demand-driven with AI/ML and MEIO Concurrent planning with real-time visibility Integrated planning with AI and knowledge graphs
Algorithm Types Machine learning, AI, Multi-Echelon Optimization (MEIO) Heuristic and optimization algorithms Machine learning, Knowledge graphs
Demand Forecasting AI-driven, considers external factors Real-time, collaborative forecasting Real-time, AI-driven forecasting with dynamic inputs
Scenario Planning Focused on demand variability and seasonality Extensive “what-if” scenario analysis Scenario-based planning with multiple replenishment strategies
Replenishment Scenarios Seasonal fluctuations, multi-location, demand uncertainty Highly volatile demand, multiple demand priorities, capacity constraints Rapid market changes, end-to-end visibility, customized rules
Dynamic Adjustments Dynamic safety stock, adjusts to real-time data Concurrent adjustments with real-time collaboration Dynamic planning with real-time data inputs and knowledge graphs

Which Solution is Right for You?

Choosing the right replenishment software depends on your specific business needs and supply chain complexity:

  • BlueYonder Fulfillment is ideal for companies looking for a mature solution that focuses on optimizing inventory across multiple echelons while managing demand variability.
  • Kinaxis RapidResponse is suitable for businesses that require agility and speed in planning, with an emphasis on real-time collaboration and scenario analysis to manage volatile demand.
  • o9 MEIO is a good fit for organizations seeking an integrated approach to planning with deep analytics capabilities, end-to-end visibility, and flexibility to handle complex, dynamic supply chains.

Conclusion

Each of these solutions offers unique capabilities to handle different replenishment scenarios. While we have the most experience with these vendors, there are more solutions that can solve replenishment needs. At K3 Group, we can help you assess your specific needs, choose the right software, and implement or optimize your replenishment planning process. Let us guide you through the selection process to find the best fit for your business.

Ready to optimize your replenishment planning? Contact us today to get started.

About K3 Group

At K3 Group, we specialize in implementing tailored systems that drive efficiency and streamline operations. Our expertise covers a wide range of solutions, including supply chain inventory optimization and inventory planning software to ensure your business is always prepared for demand fluctuations. We support BlueYonder Fulfillment (formerly JDA), Kinaxis RapidResponse, and o9 MEIO systems, integrating them into your overall replenishment planning process to improve accuracy, reduce costs, and enhance service levels across your supply chain. Let us help you optimize your fulfillment and inventory strategies for maximum results.

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Scoping a Replenishment Planning Project

replenishment and inventory planning software that focuses on supply chain inventory optimization using BlueYonder (JDA) fulfillment, Kinaxis RapidResponse, and o9 MEIO for the replenishment planning process

Key Topics Checklist

To ensure a successful replenishment planning project, it’s essential to address several critical topics before starting the implementation during the scoping phase. This is the process we use at K3 to drill into details that will drive an implementation.  Each section below provides a brief description and a list of key points to consider.

1. Objectives

Define the project’s overall objectives and set clear expectations for the project timeline and deliverables.

  • Identify stakeholders.
  • Define project objectives and success criteria.
  • Outline project timeline and milestones.
  • Identify necessary workshops and planning sessions.

2. Understanding the Supply Chain Network

Gain a comprehensive understanding of the existing supply chain network, its scope, and parameters to identify areas for optimization.

  • Review current network design and documentation.
  • Define scope of network module (distribution centers, transport modes, lead times).
  • Identify sourcing lanes, multiple sourcing factors, and lot-sizing requirements.
  • Establish minimum order quantities (MOQ) and other key parameters.

3. Defining the Scope of Supply Chain Planning

Clarify which aspects of the supply chain are within scope, including business units, product types, and master data requirements.

  • Determine the business units involved (e.g., domestic, exports).
  • Categorize product types and their characteristics.
  • Review master data needs for plants, DCs, and distributors.
  • Assess current state of master data maintenance.

4. Master Data Requirements

Ensure all master data is complete and accurate to support effective replenishment planning.

  • Identify number and types of locations (plants, DCs, suppliers).
  • Determine number and classification of items/SKUs.
  • Evaluate SKU grouping, prioritization rules, and storable switches.
  • Verify data accuracy and completeness.

5. Resource and Capacity Planning

Plan for resource use and capacity constraints to avoid bottlenecks and ensure smooth operations.

  • List all resources (production, storage, transportation) and their capacities.
  • Identify shared resources and any capacity constraints.
  • Define resource capacities at category and SKU levels.
  • Establish calendars for production, distribution, shipping, and holidays.

6. Demand and Order Types

Clarify the types of demand and order management rules to ensure proper prioritization and fulfillment.

  • Review types of demand (sales orders, forecast orders, backlogs).
  • Define sales order rules (lateness, early fulfillment, horizon).
  • Establish forecast parameters (levels, allocation, proration, netting).
  • Determine planning horizons for different demand types.

7. Demand Prioritization Framework

Develop a framework for prioritizing demand based on various factors to align with business goals.

  • Establish prioritization factors (time, product tier, customer tier, demand types).
  • Define Linear Programming (LP) layering and demand grouping strategies.
  • Set rules for safety stock prioritization.

8. Safety Stock Targets

Define safety stock requirements to maintain inventory reliability and meet customer service levels.

  • Set safety stock levels (absolute quantities or days of coverage).
  • Align safety stock targets with inventory policies.

9. Supply Data Management

Ensure accurate and comprehensive supply data for effective planning and execution.

  • Review inventory data, including locations and sourcing lanes.
  • Verify in-transit data management (statuses, delays).
  • Confirm fixed supply details (production schedules, outsourcing).

10. Planning Parameters and Granularity

Set planning horizons, granularity, and buckets to align with business needs and industry practices.

  • Define planning horizon (e.g., 4-5 months) and granularity (daily, weekly, monthly).
  • Establish planning buckets and frozen or firming periods.

11. Planning Objectives and Optimization Goals

Outline the key objectives and goals for the planning process to ensure alignment with business strategy.

  • Maximize demand satisfaction and minimize lateness.
  • Respect safety stock targets and storage constraints.
  • Minimize costs and excess inventory while ensuring just-in-time planning.

12. Outbound Data Interfaces

Define the data needed for outbound processes and ensure smooth integration with existing systems.

  • Determine requirements for net production plans, vehicle load plans, and inventory reports.
  • Identify exception handling rules and reason codes.
  • Plan for seamless integration with ERP and other systems.

13. Scenario Planning

Prepare for potential risks and develop strategies to handle different “What-if” scenarios.

  • Plan for various “What-if” scenarios to test strategies.
  • Develop contingency plans for unexpected changes in demand or supply.

Conclusion

By thoroughly addressing each of these topics during the scoping phase, you can create a solid foundation for a successful replenishment planning project. At K3 Group, our team is here to help you navigate these complexities and design a solution tailored to your specific needs. Contact us today to learn more.

About K3 Group

At K3 Group, we specialize in implementing tailored systems that drive efficiency and streamline operations. Our expertise covers a wide range of solutions, including supply chain inventory optimization and inventory planning software to ensure your business is always prepared for demand fluctuations. We support BlueYonder Fulfillment (formerly JDA), Kinaxis RapidResponse, and o9 MEIO systems, integrating them into your overall replenishment planning process to improve accuracy, reduce costs, and enhance service levels across your supply chain. Let us help you optimize your fulfillment and inventory strategies for maximum results.

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Case Study: Near Realtime Replenishment Planning

replenishment and inventory planning software that focuses on supply chain inventory optimization using BlueYonder (JDA) fulfillment, Kinaxis RapidResponse, and o9 MEIO for the replenishment planning process

Case Study: Replenishment Planning for a Distributor with a Complex Network

In this case study, we explore how we helped a distributor that manages extensive inventory across a vast network improve their replenishment planning process. While the company was good at filling orders, they knew there was a better approach that could fill orders quicker, optimize inventory, and increase customer fill rates. By implementing a linear programming optimization engine, we helped them streamline operations, reduce costs, and increase efficiency.

Project Goals

The goals of the project were:

  • Automate replenishment planning to free up employees’ time, reducing labor costs by hundreds of thousands of dollars annually.
  • Ensure a more consistent and data-driven approach to allocating inventory to customer orders.
  • Reduce inventory carrying costs by improving stock alignment with customer demand, leading to better cash flow and reduced excess inventory.

Project Background

Large-scale distribution network – The distributor manages hundreds of thousands of unique items across approximately 20 distribution centers (DCs) in the US and Canada. The network consists of primary distribution centers along with smaller cross-docking facilities. While replenishment can be fulfilled from multiple locations, network priorities are established to favor specific sites over others to improve efficiency and optimize inventory flow.

Frequent order fulfillment – With short lead times, orders are released to warehouses multiple times a day, requiring a highly responsive and efficient replenishment process. The network operates on a follow-the-sun model, leveraging 24-hour operations across the country to ensure continuous order fulfillment and inventory availability.

Complex sourcing and demand prioritization – The nimbleness of the operations also introduced complexities such as circular sourcing, near real-time demand requirement, and a complex demand prioritization process.

Reliance on manual processes – The existing system combined ERP custom code with manual updates, requiring employees to augment the daily cutoff process—a high-pressure, time-sensitive window where orders were processed in a frantic rush. While this approach ensured orders were fulfilled, it was inefficient and often led to suboptimal inventory allocation.

Project Solution

The solution uses a linear programming optimization engine to solve the problem. LP optimization engines are fickle but balance constraints, priorities, and objective functions to drive to a near optimal solution which is significantly better than what people can do with a large network.

Data Ingestion – The engine ingests customer orders, forecast, inventory, scheduled receipts, purchase orders, the sourcing network, and other relevant data sources.
Demand Prioritization – After ingestion, customer orders are prioritized according to key business rules such as hard allocations, customer priority, adherence to forecast, and past order cuts.
Optimization & Inventory Matching – Prioritized demand is processed by the LP optimization engine, which allocates inventory, inbound shipments, and purchase orders to fulfill orders. The system also suggests transfer orders when necessary.
Post-Optimization & Continuous Execution – If an order is cut, the system assigns a reason code so sales representatives can explain to customers. The optimization process runs multiple times per day, ensuring warehouses can continuously fill orders efficiently.
replenishment and inventory planning software that focuses on supply chain inventory optimization using BlueYonder (JDA) fulfillment, Kinaxis RapidResponse, and o9 MEIO for the replenishment planning process

Key Challenges and Solutions

Solving this problem was not simple, but the results save millions of dollars a year. Below are some of the main challenges the team faced in the project.

1. Addressing Circularity Issues: The distributor’s network had circularity where sourcing could draw from reciprocal locations. For instance, Site A could source from Site B, and Site B could source from Site A, creating potential loops that would cause issues for automated planning algorithms. To resolve this, the team introduced virtual locations into the network. By assigning a weighted preference to source items from a primary location first and only turning to secondary locations when necessary, the LP optimization engine effectively minimized circular sourcing conflicts.

ITEM SOURCE DEST TRANSMODE PRIORITY 
1234HoustonDallasTruck
1234DallasHoustonTruck

2. Optimizing Demand-Supply Matching: To allocate products efficiently to customers, multiple tiers of demand classification were implemented. The project leveraged the solver to optimize the demand-supply match across the network. This solver accounted for factors such as inventory levels, transportation costs, and customer demand, ensuring that products were distributed in a way that maximized overall efficiency and minimized costs.

ITEM LOCATION CUSTQUANTITY ORDERID SHIPDATE TIER
A1HoustonCUST1100 0001 8/5/2024 TIER 1
A1HoustonCUST215000028/5/2024 TIER 2

3. Achieving Order-Level Transfer Visibility: Given the sophistication of their fulfillment process, inventory transfers could not be lumped into a single transfer and had to maintain order-level transfer visibility. The solution needed to handle cases where transfers between distribution centers were often cross-docked, meaning they were not taken off the truck but directly transferred to fulfill orders. This level of visibility allowed the distributor to manage inventory more effectively, reducing delays and ensuring timely deliveries.

4. Near Real-Time Optimization and Execution: One of the most significant challenges was the need to run the optimization in near real-time. Due to a tight order cutoff window, the team needed to ensure that cuts and orders reached the warehouse within an hour. This requirement necessitated multiple optimization runs per day to accommodate different time zones, a sharp departure from the traditional overnight batch processing typically used in replenishment planning. This new approach aligned closely with the operational needs of many consumer packaged goods (CPG) companies and distributors, who require a more responsive and dynamic planning process.

5. Planning Optimization Process: The replenishment planning run was designed to operate seamlessly, much like an Azure or Google function. The company would send items, networks, sourcing, customer orders, and other data to the optimization system, which would process the information and, within about an hour, return an optimized plan ready for execution. This plan could then be directly imported into the company’s ERP and warehouse management systems, allowing for immediate action. This quick turnaround enabled the distributor to maintain agility and responsiveness in their supply chain operations, critical for meeting tight order deadlines and managing complex inventory needs.

6. Project Duration and Impact: The entire project, from initiation to completion, took 25 weeks. This relatively short timeframe was essential given the project’s complexity and the need for a rapid transformation of the distributor’s replenishment planning capabilities. By the end of the project, the distributor had implemented a more agile and efficient replenishment process, allowing them to respond more effectively to changes in demand and supply conditions.

replenishment and inventory planning software that focuses on supply chain inventory optimization using BlueYonder (JDA) fulfillment, Kinaxis RapidResponse, and o9 MEIO for the replenishment planning process

Conclusion

This case study demonstrates the transformative impact of a carefully designed replenishment planning solution tailored to a distributor’s specific needs. By addressing circularity issues, optimizing demand-supply matching, achieving order-level visibility, and implementing real-time optimization, the project enabled the distributor to enhance operational efficiency, reduce costs, and improve service levels across its network. This approach represents a forward-thinking model for other distributors and CPG companies looking to modernize their replenishment planning strategies.

At K3 Group, we specialize in solving complex replenishment optimization challenges. If you’re looking to enhance your supply chain efficiency and streamline your operations, we’re here to help.

About K3 Group

At K3 Group, we specialize in implementing tailored systems that drive efficiency and streamline operations. Our expertise covers a wide range of solutions, including supply chain inventory optimization and inventory planning software to ensure your business is always prepared for demand fluctuations. We support BlueYonder Fulfillment (formerly JDA), Kinaxis RapidResponse, and o9 MEIO systems, integrating them into your overall replenishment planning process to improve accuracy, reduce costs, and enhance service levels across your supply chain. Let us help you optimize your fulfillment and inventory strategies for maximum results.

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Choosing the Right Software Solution for Your S&OP Process

S&OP

Evaluate the Alternatives and Make the Best Choice Possible for Your Organization

Sales and Operations Planning (S&OP) is an essential business process that aligns demand, supply, and financial planning to achieve a company’s strategic goals. To implement an effective S&OP process, organizations need robust software solutions that provide visibility, collaboration, and advanced analytics capabilities. Several companies offer specialized software solutions tailored to support S&OP, including Blue Yonder, Kinaxis, and o9 Solutions. Additionally, for companies that prefer to build their own custom S&OP processes, platforms like Snowflake and Databricks offer powerful data management and analytics capabilities.

Let’s explore each of these solutions to understand how they can help enhance your S&OP process.

1. Blue Yonder: End-to-End Supply Chain Planning

Blue Yonder (formerly JDA Software) is a leading provider of digital supply chain and omni-channel commerce solutions. Their S&OP solution is designed to integrate demand and supply planning with financial goals, providing end-to-end visibility across the supply chain.

Key Features:

  • Integrated Planning Platform: Blue Yonder offers a unified platform that integrates demand, supply, inventory, and financial planning, allowing for real-time collaboration and decision-making.
  • Scenario Planning: Users can create and evaluate multiple scenarios to understand the potential impact of different market conditions or supply chain disruptions, helping to make informed decisions.
  • Collaboration Tools: Blue Yonder’s solution includes collaboration tools that enable cross-functional teams to work together effectively, ensuring alignment between departments.

Best For:

Companies looking for a comprehensive, end-to-end solution with advanced analytics to drive their S&OP process.

2. Kinaxis: RapidResponse for Agile S&OP

Kinaxis offers its flagship solution, RapidResponse, a cloud-based platform that provides agile and flexible S&OP capabilities. The solution focuses on enabling quick decision-making by bringing together all aspects of planning—demand, supply, and inventory—in a single platform.

Key Features:

  • Concurrent Planning: Kinaxis RapidResponse allows for concurrent planning, meaning multiple scenarios can be evaluated simultaneously, leading to faster and more accurate decision-making.
  • What-If Analysis: The platform provides advanced what-if scenario analysis, enabling companies to simulate different outcomes and choose the best course of action.
  • End-to-End Visibility: The solution offers end-to-end visibility across the supply chain, improving collaboration between stakeholders and ensuring alignment between sales, operations, and finance.
  • Built-in Collaboration: Features such as alerts, notifications, and collaborative workspaces help teams stay connected and aligned.

Best For:

Organizations that need a highly agile and responsive solution for their S&OP process, particularly those in fast-paced industries where speed and flexibility are crucial.

3. o9 Solutions: Integrated Business Planning and Decision-Making

o9 Solutions offers an integrated platform that combines S&OP with Integrated Business Planning (IBP), enabling organizations to make smarter, faster decisions across their entire supply chain. The platform is designed to be highly customizable and providing advanced analytics.

Key Features:

  • Digital Brain Platform: The o9 platform uses a “Digital Brain” to process vast amounts of data, providing real-time insights and recommendations.
  • Scenario Planning and Simulation: Users can create complex scenarios to test the impact of different strategies, market conditions, and supply chain disruptions.
  • User-Friendly Interface: o9 offers an intuitive, user-friendly interface that supports collaboration across functions and promotes quick adoption by users.

Best For:

Companies looking for a highly customizable and scalable S&OP solution.

4. Building Your Own S&OP Process: Snowflake and Databricks

For organizations that prefer a tailored solution and want to build their own S&OP process, Snowflake and Databricks offer powerful platforms for data management and analytics. These solutions provide the flexibility to create a customized S&OP process that aligns with specific business needs.

Snowflake: Cloud Data Platform

Snowflake is a cloud-based data platform that allows businesses to manage and analyze large volumes of data in real time. It is designed for data warehousing, data lakes, and data science applications.

  • Key Benefits:
    • Scalability and Performance: Snowflake’s cloud-native architecture allows for scalable data storage and processing, making it ideal for handling large datasets.
    • Data Sharing and Collaboration: Secure data sharing features enable seamless collaboration between departments and external partners.
    • Real-Time Data Analytics: Supports real-time data analytics, which is critical for S&OP processes that require up-to-date information.

Databricks: Unified Data Analytics Platform

Databricks is a unified analytics platform that integrates data engineering, data science, and machine learning. It is built on top of Apache Spark, providing high-performance data processing and analysis.

  • Key Benefits:
    • Data Lakehouse Architecture: Combines the best of data lakes and data warehouses, allowing for efficient data storage, processing, and analytics in a single platform.
    • Machine Learning and AI: Offers powerful tools for building and deploying machine learning models, enabling advanced forecasting and scenario planning.
    • Collaborative Notebooks: Provides collaborative notebooks that allow teams to work together on data analysis, model development, and reporting.

Best For:

Organizations with specific requirements that prefer a custom-built S&OP solution and have the in-house expertise to leverage platforms like Snowflake or Databricks for data management and analytics.

Conclusion

Choosing the right software solution for your S&OP process depends on your organization’s specific needs, resources, and objectives. Blue Yonder, Kinaxis, and o9 Solutions offer comprehensive, ready-made platforms with advanced capabilities that cater to different levels of complexity and flexibility. On the other hand, Snowflake and Databricks provide the tools needed for companies that prefer to build a tailored S&OP process from the ground up. We at K3 can help you evaluate your needs carefully, consider the strengths of each solution, and choose the one that best aligns with your strategic goals.

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Scenario Planning in S&OP

S&OP

Preparing for Market Uncertainties

In a world where market conditions can change overnight, businesses must be prepared to navigate uncertainty. This is where scenario planning becomes a crucial component of the Sales and Operations Planning (S&OP) process. Scenario planning allows organizations to model different potential futures and develop strategies to respond effectively to unexpected events. By incorporating scenario planning into S&OP, companies can make more informed decisions, minimize risks, and seize opportunities in a dynamic market environment.

Building Scenarios: Modeling Demand Fluctuations and Supply Chain Disruptions

The first step in scenario planning is to build a range of possible scenarios that reflect various market conditions and uncertainties. These scenarios should encompass both demand fluctuations and supply chain disruptions to provide a comprehensive view of potential challenges.

1. Modeling Demand Fluctuations

Demand can be highly unpredictable, influenced by factors such as changing customer preferences, economic conditions, competitor actions, and market trends. To model demand fluctuations, organizations should:

  • Identify Key Variables: Start by identifying the key variables that impact demand, such as sales growth rates, customer segments, seasonal trends, and promotional activities.
  • Develop Multiple Scenarios: Create multiple demand scenarios that capture a range of possibilities. For example, develop a “best-case” scenario with high demand growth, a “worst-case” scenario with a significant demand decline, and a “moderate” scenario that represents stable demand.
  • Use Historical Data and Predictive Analytics: Leverage historical sales data, market research, and predictive analytics tools to model these scenarios accurately. This approach helps anticipate how demand might change under different conditions.

2. Modeling Supply Chain Disruptions

Supply chain disruptions, such as supplier delays, transportation bottlenecks, geopolitical risks, and natural disasters, can significantly impact a company’s ability to meet demand. To model supply chain disruptions, consider:

  • Identify Potential Disruption Points: Map out the entire supply chain and identify potential points of failure, such as critical suppliers, transportation hubs, or manufacturing facilities.
  • Develop Disruption Scenarios: Create scenarios that capture different types of supply chain disruptions. For instance, consider scenarios where a key supplier faces a production halt, or a major port is temporarily closed due to a natural disaster.
  • Assess Resilience: Evaluate the resilience of your supply chain under each scenario. This includes assessing alternative sourcing options, safety stock levels, and lead times to determine how quickly you can recover from disruptions.

By building comprehensive demand and supply chain scenarios, organizations can better understand the range of possible futures and be prepared for unexpected events.

Impact Analysis: Assessing Scenarios’ Effects on Operations and Informing Decision-Making

Once the scenarios are built, the next step is to conduct an impact analysis to understand how each scenario would affect the organization’s operations. This process involves evaluating the potential outcomes of each scenario and using this information to inform strategic decision-making.

1. Evaluating Operational Impact

Impact analysis begins with assessing how each scenario would impact different aspects of the business, such as inventory levels, production schedules, workforce requirements, and financial performance. For example:

  • Inventory Management: Determine how inventory levels would be affected under different demand and supply scenarios. Would a sudden spike in demand deplete stock levels? Would a supply chain disruption create excess inventory?
  • Production Planning: Assess how production schedules would need to be adjusted to respond to each scenario. Can production be ramped up or scaled down quickly? Are there bottlenecks that could hinder flexibility?
  • Financial Implications: Analyze the financial impact of each scenario, including changes in revenue, costs, and profitability. For instance, a demand surge may require additional investment in production capacity, while a supply chain disruption could increase costs due to expedited shipping or alternative sourcing.

2. Informing Decision-Making

The insights gained from impact analysis can be used to inform strategic decisions and prepare for potential challenges. Here’s how scenario planning can drive better decision-making:

  • Develop Contingency Plans: Use the results of the impact analysis to create contingency plans for each scenario. For example, if a key supplier faces a disruption, a contingency plan might include identifying alternative suppliers or increasing safety stock levels.
  • Enhance Agility: Scenario planning enables organizations to be more agile by anticipating potential challenges and having predefined responses ready. This agility is crucial for quickly adapting to changes in demand or supply conditions.
  • Prioritize Investments: Assess which investments are most critical based on the likelihood and impact of each scenario. For example, investing in advanced analytics tools or diversifying the supplier base might be prioritized if these actions mitigate risks across multiple scenarios.
  • Strengthen Cross-Functional Alignment: Share scenario planning insights across departments to ensure alignment and coordinated responses. Regular communication between sales, operations, finance, and supply chain teams ensures that everyone is prepared for the same set of potential futures.

Conclusion

Scenario planning is an essential tool in today’s uncertain business environment. By building scenarios that model demand fluctuations and supply chain disruptions, and conducting thorough impact analysis, organizations can better prepare for the unexpected. Integrating scenario planning into the S&OP process helps companies make informed decisions, reduce risks, and capitalize on opportunities, ultimately enhancing their ability to thrive in a dynamic market landscape.

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