Precision in the Periphery - Forecasting and Simulation at Verizon
While I was at Verizon, I was in the supply chain division of the wireless group, tasked by my manager with helping the team reduce accessory inventory. This was becoming a bigger and bigger problem as time went on, as accessories shifted from phone cases and screen protectors to high-end bluetooth devices and even drones. These are big ticket items that we need in store for merchandising, but with very intermittent demand and larger costs, this represented a challenge in managing inventory. If we ordered too much inventory and sent it to the stores, this inventory risked being stranded, with no one to purchase it until we either pay to ship it back to the warehouse or heavily discount the accessory to free up space in the store. If we did not order enough, customers would arrive to purchase a phone and not be able to have the accessories they want, which is not the experience we wanted the customer to have. Combined with large new phone launches driving a large initial spike in demand for associated accessories that might not last, we needed to get smarter about forecasting demand and allocating units.
Traditional supply chain techniques for the stocking problem involve predicting the future volume of sales over a time period and shipping that quantity to arrive at the beginning of the period, with a margin of error added on top to buffer against the uncertainty in the predicted demand. When the demand signal is very low and intermittent, the usual time-series forecasting models were not effective, as the assumptions of these models are seldom satisfied for these SKUs (e.g. residuals are not normally distributed). We also need to do this for a very large number of SKUs, as each phone model has a large amount of possible accessories, including multiple phone cases and screen protectors for each size for each vendor selling with us, for both new models and older models. The demand can also be very volatile, as phone-associated SKUs are highly correlated with the phone sales, which are offered on promotion or discount often, causing a spike in the accessory demand. We needed a custom method to deal with this situation.
The method we developed included a two prong approach. For the initial launch of the phone, we used the marketing forecast of the phone itself and used an attach rate derived from similar launches and products to try and capture the first wave of demand. For the rest of the phone's life, however, sales are not as well correlated to the phone's launch. In this method, we find that the main driver of replenishment to the stores was not sales, but keeping minimum inventory on the shelves for merchandising (i.e. to keep the shelves full). We turned our attention to trying to figure out what the shelf space we needed for each phone model, or in other words, what the minimum inventory we needed. The solution we landed on was store-level simulation using historical data.
Once we have either historical time series data, either from the SKU in question or a similar SKU, we can simulate sales at different minimum values, calculating the maximum forecast error over the lead time of the SKU. (i.e. how far off our prediction is by the time the order from the warehouse makes it to the store). Once this is calculated, we can talk to the merchandising department and recommend minimum values that strike an appropriate balance between service level (or approximately the probability that the SKU is out of stock when you want it) and stranded inventory at the store.
This was a major contributor to hitting our inventory valuation goal, with the total share of accessory on hand staying relatively constant while making up a larger proportion of our revenue year over year. Key takeaways for me from this is that when you can simulate a system, you are one step closer to optimizing it. Being able to show our stakeholders what would have happened if we had some something differently is very vital to getting buy in both beforehand and during the project, giving them confidence that we have looked for the best solution that balances all business interests.
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