Those who are tasked with forecasting within an organization understand all too well that seasonal items are the biggest supply chain planning pain point. These items are most likely to induce service risks or create burdensome inventory spikes due to the high variability in orders, making it harder to forecast and properly plan for supply.

While these items do typically have repetitive cycles, there is increased pressure to accurately forecast the cyclical spikes. Having been placed in this position before, I would like to use this blog to provide a few tips on how your organization can improve its demand planning efforts for the seasonal items in your portfolio.

Attach a proper baseline statistical model

Using a seasonal forecasting model for these items will allow your team to accurately capture year over year volume uplifts at the correct time each year. A variety of seasonal models are available to help your organization create an appropriate forecast. One popular model is known as Holt-Winters method. Using this approach, a planner can adjust three different parameters — alpha, beta, and gamma — to smooth the base volume, trend, and seasonality. Unfortunately, the time and research required to pick the right parameter values for your model can be labor intensive and may require additional resources.

Coordinate with your retail partners

A robust S&OP process is important for all SKUs. However, this process is especially important for highly seasonal items since retailers typically only promote seasonal products during the corresponding season. Your baseline statistical forecast may not capture these promotions if they are not repeated every year. To avoid this costly oversight, work closely with your retail partners to understand what advertisements and promotions they are running that vary from previous events and plan accordingly.

Measure your FVA impact

After each shipment period, be sure a Forecast Value-Add (FVA) analysis is performed to measure your impact. This statistic captures the difference between the accuracy of your baseline forecast and your final consensus forecast, and will typically show significant changes by week or month for seasonal items. While your baseline stat model can capture all demand needed in off months, it may be necessary to add marketing and sales intelligence for in-season forecasts. Tracking your organization’s FVA monthly over time allows you to understand trends and when it is useful to overlay volumes.

Seasonal items, if not forecasted properly, can create significant service risks and waste valuable materials and labor. Establishing the proper baseline statistical model for these high variable items with cross-functional insight from within your organization as well as your retail partners will allow to layer demand accordingly. However, the most critical piece for any organization involves checking your FVA analysis to determine when the baseline model or consensus forecast is more accurate. This is especially important when monitoring in-season versus out of season demand. Depending on your product portfolio, you may have items that can be sufficiently planned for out of season using a baseline stat model; however, if internal resources are limited, consider employing a Statistical Forecast as a Service (SFaaS) approach to ensure your time and effort are well spent.

Alannah Fenerty is a Sr. Consultant in Chainalytics’ Integrated Demand & Supply Planning practice where she helps clients forecast sales, balance supply and demand, improve product availability and improve cross-functional efficiency within their supply chains.

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