Category: Replenishment

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.

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.

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

In this case study, we explore a replenishment planning project undertaken for a distributor managing an extensive inventory across a vast network. The distributor handles hundreds of thousands of items, distributed through approximately 20 distribution centers (DCs). The scale and complexity of the network presented unique challenges, particularly concerning circular sourcing issues, demand allocation, and the need for real-time optimization. The company wished to replace a hybrid ERP plus manual process that consumed considerable time from employees at the cutoff window every day. By implementing the new process, the company was able to free time from employees saving hundreds of thousands a year and get a more consistent approach to allocating inventory to customer orders.

Key Challenges and Solutions

1. Addressing Circularity Issues: The distributor’s network faced circularity challenges where sourcing was limited to only one level deep. For instance, Site A could source from Site B, and Site B could source from Site A, creating potential loops that could disrupt efficient replenishment. 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 solution effectively minimized circular sourcing conflicts.

2. Optimizing Demand-Supply Matching: To allocate products efficiently to customers, multiple tiers of demand classification were implemented. The project leveraged a linear programming 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.

3. Achieving Order-Level Transfer Visibility: Given the complexity of the network, achieving order-level transfer visibility was crucial. 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. 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 integrated 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.

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.