RL Magazine
Technical Trends: Calling Your Shots
by L. Bryant Underwood

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One of the most enduring metaphors from Baseball originates from Babe Ruth in Game 3 of the 1932 World Series. The legend is most generally recounted as Ruth pointed to the center field bleachers as a sign to the fans of his intent to hit a home run. On the next pitch, Ruth indeed hit a home run to center field. While many wax about the accuracy of what really happened that day, the goal is still one of significant importance. In reverse logistics the margins are very slim. A little labor overtime consumed at the wrong time can destroy operational performance. A critical need is a consistent method of performing technical analysis of the gross return trends so that operational performance can be better planned. Thomas Welsh and I have been discussing the dearth of meaningful guidance in predicting inbound trends of defective materials. To that end we wanted to outline some thoughts and suggestions to help you improve your own forecasting methods.

There is a great deal of science around technical analysis for finance and stocks. Much of this is beyond the scope of what we are trying to do here, but nonetheless very interesting. I especially like Elliot wave theory and how it leverages the Fibonacci Series. Very cool techniques for finance and equities just not very relevant to reverse logistics forecasting. The main hinge that all RL forecasting must start with is some understanding of the seasonality of your product line. Each product line will be different, but there will be some trending. For example, the RL trend for navigation products all stems from the two sales peaks they have each year. The first is in the springtime in support of dads-N-grads gifts followed by winter sales for Christmas. There is a similar pattern for consumer notebook computers, while business class machines are more level loaded across the year. As a first step in improving your forecasting you need to take a look at your data and aggregate the gross return numbers over time. From this you can then extract the overall seasonality trend of your product returns. Just this basic metric will help greatly in forecasting your returns accurately.

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