To Fill, or Not to Fill (pallets). That is the Question.
How RSI Helped a Multi-Brand Company to Better Balance Operational and Inventory Efficiencies
Balancing operational and inventory efficiencies is a critical challenge faced by businesses across various industries. While operational efficiency focuses on optimizing processes and resources to maximize productivity and reduce costs, inventory efficiency aims to ensure that the right amount of inventory is available to meet customer demand while minimizing carrying costs and obsolescence. To address these challenges, businesses need to understand how the decisions they make impact the balance between different, and often competing, imperatives. Leveraging advanced analytics and scenario planning techniques are excellent methods for enterprises to test the consequences that business decisions will have on an entire system. Employing these techniques helps businesses to increase the probability that optimized, balanced decisions will be made.
Right Sized Inventory (RSI) recently worked with a multi-brand company to address the business challenge of appropriately balancing operational and inventory efficiencies. The company’s decision makers had a critical question in front of them: should they prioritize operational efficiency by utilizing full pallet orders or should they prioritize inventory efficiency by using economic order sizes. Variability and uncertainty are constant conditions in the real-world. Scenario planning is a method of preparing for uncertainty by modeling multiple sets of assumptions and parameters. RSI’s patented technology is ideally suited for scenario planning because our simulation-based methodology directly accounts for variability across multiple factors and produces outcomes that are probabilistic in nature.
In this case, RSI’s Inventory Optimization as a Service (IOaaS) team was able to leverage our technology to help answer the operational vs inventory efficiency question for this client. The first step was to establish a baseline analysis for the nearly 7,000 target items. These baseline results included inventory target recommendations for each item + location combination. The next step in this scenario planning process involved introducing inventory per pallet data. RSI’s IOaaS team broke down the data by days of inventory per pallet as well as the cost of inventory per pallet. From there, the team identified several categories of items, including those items that had a Minimum Order Quantity (MOQ) already equal to or greater than a full pallet quantity. Once the target items were fully categorized, RSI evaluated how changing MOQ’s to equal full pallet sizes would impact inventory quantities and cost.
Several operating recommendations emerged from RSI’s scenario planning analysis, including the following highlights:
For items with <1 month of material on a pallet, use a full pallet order quantity.
RSI scenario planning revealed that inventory increases for items that fell into this category were negligible when rounding values were adjusted to equal a full pallet quantity. For items falling within this policy category, operational efficiency is improved without risking higher inventory costs.
For items with <$500 of inventory value on a pallet, use a full pallet order quantity.
Similarly, RSI scenario planning revealed that items in this category could also be rounded up to a full pallet quantity without increasing inventory in a meaningful way. Once again, operational efficiency is improved without harming inventory cost objectives.
If the current MOQ is > a full pallet quantity, use a full pallet order quantity.
RSI’s analysis showed that items with MOQ’s above a full pallet order quantity could be adjusted to the full pallet quantity without sacrificing service levels or increasing inventory value. Because RSI uses actual demand history, we’re able to simulate future inventory behavior under a variety of circumstances. We can easily test out the impact on inventory cost and customer service level of potential changes in order quantity, frequency, and batch size.
For items that don’t meet policies 1, 2, or 3 do not move to a full pallet order quantity.
In some cases, it does not make sense to force full pallet order quantities. Our client is able to apply these four policies moving forward and can also rely on RSI’s technology and expert IOaaS team to help adjust them as conditions change in the future.
In addition to the above operating policy recommendations, RSI’s analysis indicated that, overall, the client had a great opportunity to reduce inventory without jeopardizing the desired level of customer service across their product portfolio. This outcome was not part of the specific scope the client was focused on however, it is consistent with the typical results RSI’s analytics reveal.
Optimizing inventory for individual items is very valuable to any enterprise that makes, moves, stores, or sells physical goods. Developing operating policies that can be applied to large numbers of items across distinct categories is even more valuable because those policies enable better decision making on a consistent basis. The powerful combination of RSI’s simulation-based technology and IOaaS expertise enables sound, repeatable decision making.
