Efficient inventory management is crucial for any business. While inventory is needed to meet customer service levels and mitigate uncertainties, organizations often struggle to balance customer service levels, inventory costs, and cash tied up in inventory.

This challenge becomes even more complex when dealing with multi-echelon supply chains, where decisions are interrelated, and material availability downstream depends on material and resource availability upstream. A methodology that can be applied to tackle these challenges is termed Multi-Echelon Inventory Optimization (MEIO). In this article, we explore the concept of MEIO and its driving forces, its benefits, and how to successfully implement it in your organization to achieve higher levels of efficiency and profitability.

What is Multi-Echelon Inventory Optimization?

MEIO takes a holistic view of inventory management across multiple echelons (levels) within a supply chain. Traditionally, organizations have focused on optimizing inventories at each location, leading to suboptimal results and excess inventory in the system as a whole. Common reasons for excess inventory include lack of data visibility across the supply chain, lack of policy parameter revisions in organically-grown supply chains, and lack of expertise in right-sizing inventories in an integrated way.

MEIO takes a more integrated and comprehensive approach, optimizing inventory decisions across the entire supply chain rather than in siloes. Such a holistic view avoids inventory buffer duplication while ensuring high customer service levels. This is achieved by setting the goal to meet the target service level of the end customer, modeling interactions between different echelons and locations, and accounting for various effects such as demand and lead time uncertainties.

What Key Forces Drive the Trade-Offs in MEIO?

When it comes to the positioning of safety stocks across a multi-echelon supply chain, a significant trade-off is holding safety stock buffers upstream (supply chain entry point) vs. downstream (close to the end customer). The four primary effects which impact the positioning are:

  • Demand pooling effect: centralizing safety stocks upstream to mitigate uncertainties for multiple products or at multiple locations with the same safety stock.
  • Value effect: inventory carrying costs are usually lower upstream.
  • Lead time pooling effect: consolidation of safety stock at a downstream location that also covers for lead time and variability of its upstream location(s).
  • Service level effect: downstream buffering is preferred if service levels are different per customer.

What are the Benefits of MEIO?

MEIO offers significant benefits to your business. It helps increase customer service levels and optimize or rebalance inventory across the entire supply chain while reducing working capital. Running a MEIO project also helps to understand your supply chain dynamics, and it is not uncommon that it triggers discussions on strategic topics such as customer service offerings or network (re)design.

Why Do MEIO Implementations Often Fail?

MEIO is not new and despite the significant savings that can be achieved, not many multinationals have implemented it. Implementation projects are too often driven by management; the key factor is to take the inventory planners along in the journey. Implementing MEIO is not only about implementing software. A key indicator for a successful implementation is the planner’s acceptance rate of the proposed (safety) stocks. The main reasons a high acceptance rate is not achieved are as follows:

  • Each supply chain has specific modelling requirements that most software solutions do not support, resulting in low(er) quality safety stock proposals. Consider full container/truck shipments, seasonal demand, finite production capacity, or a long tail portfolio with slow moving items.
  • Planners have not been trained to understand the key drivers in MEIO, and are too often still thinking in their own silo. Planners that don’t understand the results of the MEIO-models will likely reject the safety stock proposals.
  • MEIO often proposes a large shift of inventories across the supply chain, so carefully managing the rebalancing of stock is important.

Key Ingredients of Successful MEIO Implementation

It is important to take small steps and go back to revisit data and models if new insights emerge. In short, the key ingredients to a successful MEIO-implementation are:

  1. Start with the right mindset: Focus on inventory right-sizing rather than inventory reduction.
  2. Simulation-based inventory optimization: This allows us to model real-world complexity, to gradually increase modelling complexity (which fosters buy-in from Planners), and to explain the results in an end-to-end manner (avoiding silo thinking). Make sure to run multiple scenarios to really understand the supply chain dynamics and the main drivers of stock rebalancing.
  3. Review and implement results at segment level: Review at segment level rather than item level, which can be time consuming if portfolios have tens or even hundreds of thousands of items. Machine learning has proven to be a powerful tool to segment portfolios with similar stock drivers, and to build a decision tree to review the safety stock proposals per segment rather than by item.
  4. Implement results in small steps: If MEIO proposes to reduce a safety stock from 500 to 100, don’t slash stock to 100 units straight away. Rather, reduce it by 100 units per month until the recommended level is reached.

MEIO Case Study in the Fragrances Industry 

Let’s take a look at a real life example of MEIO implementation. I worked with a Europe-based global market leader of flavors and fragrances which had an established inventory management process using SAP. Despite this, they considered their inventory position to be too high. The Inventory Planners optimized safety stocks by item and location, in isolation. My team was brought in to optimize the inventory settings in their supply chain which had more than 60 locations and 13 echelons. The project to right-size inventory resulted in a EUR 20 million saving and would not have been achieved without:

  1. Identifying and realizing quick wins: If project stakeholders don’t see tangible benefits already during the project, their involvement and willingness to support decreases quickly and changes into resistance.
  2. Increasing model sophistication as Planners’ expertise grows: The advantages of simulation over closed-form algorithms is that modelling complexity can be gradually increased according to Planners’ expertise as their understanding evolves. Moving from inventory optimization in siloes towards MEIO requires not only a step up in the Planners’ understanding of the models and stock drivers, but also significant change management for the company as a whole (e.g. different incentives and KPIs).
  3. Applying simulation techniques: This allowed for modelling real-world complexity like full truck load and stock rationing policies (if stock is insufficient, how to split available stock across requesting locations). Further, simulation results are a great facilitator in explaining the interactions between the different driving forces in MEIO.
  4. Facilitating a simple, yet sustainable implementation: MEIO is not a one-off exercise and safety stock parameters require a review every month or quarter. Building a decision tree that groups items with similar characteristics was a great solution. Each item and location combination is assigned to one of the roughly 300 different groups and this drastically reduced the workload of the Planners. They now only review each group’s safety stock factor instead of the safety stock for all 200,000 individual item/location combinations.

Strong collaboration between my team and the Planners led to a step up in the client’s knowledge and capability. After this one-off project, we supported the company on a yearly basis to review and update their decision tree and corresponding safety stock factors.

MEIO is not a one-off exercise. Safety stock parameters need periodic (monthly to quarterly) review and revisions. Every company and supply chain is unique and requires its own approach. MEIO is a powerful tool for businesses looking to right-size their inventory across multiple echelons within the supply chain and go beyond basic textbook modeling and siloed thinking. By taking a holistic approach, leveraging data-driven insights, and increasing your Planners’ understanding of the unique supply chain dynamics, organizations can improve their customer service, inventory costs, and cash.

This article first appeared in the Winter 2023/2024 issue of the Journal of Business Forecasting. To get the Journal delivered to your door quarterly, become an IBF member. Membership benefits include access to every Journal ever published, research and benchmarking reports, exclusive workshops and tutorials, and discounted entry to every IBF conference and training boot camp.