A lessor mentioned function in inventory and demand management is segmentation, yet I believe it is the bedrock of creating an effective plan. By classifying your products into distinct categories, you can tailor your forecasting, policy setting, and replenishment strategies to suit each group's unique characteristics.
Segmentation Groups I Normally Consider
In my view, there are seven essential groups that form the basis of any segmentation exercise I undertake.
New Items: These are newly introduced products that lack historical data. They require a different approach, often relying on market analysis and similar product comparisons rather than traditional forecasting methods.
End-of-Life Items: As products near the end of their lifecycle, demand can become unpredictable. Planning for these items involves making last-time buy decisions and managing stock levels to minimize excess while avoiding stockouts.
Items with Insufficient Data Points: Some products are so slow-moving that they lack enough historical data to generate reliable forecasts. Alternative methods, such as qualitative forecasting or using analogous products, can be more effective.
Erratic Items: These items exhibit unpredictable demand with no clear pattern. They often require a focus on responsive replenishment rather than forecasting.
Intermittent Items: Demand for these parts occurs at irregular intervals. Specialized forecasting techniques like Croston’s method are needed to handle sporadic demand.
Lumpy Items: Characterized by large, infrequent spikes in demand, lumpy items are challenging to forecast. Inventory buffers and flexible supply chain strategies are essential for managing these products.
Normal Items: Finally, these items have stable and predictable demand patterns. Standard forecasting methods, such as moving averages or exponential smoothing, are typically effective and are most likely to be auto-replenished.
One Size Does Not Fit All
Each of these groups necessitates a different forecasting and policy approach. Applying a one-size-fits-all method across these diverse categories can lead to significant issues. For example, trying to forecast erratic items in the same manner as normal items can result in poor inventory decisions and service level failures. I
AI: Enhancing Segmentation and Adaptability
Artificial Intelligence (AI) is a powerful tool for refining segmentation. AI can analyze vast amounts of data and accurately classify items into the appropriate groups. It can also monitor ongoing data to identify when an item's characteristics have changed, suggesting that it might perform better in a different group. This adaptability ensures that the segmentation remains relevant and effective over time, enabling more precise forecasting and inventory management.
Beware Improper Data Cleansing
Moreover, improper data cleansing processes before forecasting can strip away crucial information. For instance, smoothing out historical data for erratic items might eliminate the very spikes that are critical to understanding their demand patterns. Therefore, applying the right data cleansing and preparation techniques specific to each segment is vital.
In Summary
Segmentation is the foundation of successful planning in inventory and demand management. By recognizing the unique characteristics of each item group and tailoring your approach accordingly, you can improve forecasting accuracy, optimize inventory levels, and enhance overall supply chain performance. With the added power of AI, you can ensure that your segmentation remains dynamic and responsive to changing item characteristics.
Think flow,
Kevin Boake
