In an earlier post I discussed the opportunities for using richer data to support supply chain planning in e-commerce retail. One topic I didn’t touch –purposefully – is returns as I wanted to deal with the topic in a separate entry.
As almost every etailer and retailer with an online operations knows all too well, product returns are a costly headache that simply have to be taken care of. When customers can’t see, touch or try goods before buying them, the return rates tend to be significantly higher and, when they can’t just take them back to a store but have to mail them, the cost of returns is also relatively higher. Tracking this data alongside sales is very useful as it shows where problems are occurring – you can easily check your data to see if there are items, categories or brands that are more problematic than others, and then work to lower those rates.
A common question is whether forecasting should be based on sales with or without returns. Instead of trying to forecast sales including returns, I would suggest forecasting them separately. With sales forecasts you want to maximise availability – i.e. you need to know what will be shipped and when, irrespective of whether it will be returned or not. Forecasting returns separately gives you a clear overview of the associated costs and requirements such as logistics capacity, and therefore helps both operational and financial planning.
Instead of trying to forecast sales including returns, I would suggest forecasting them separately.
If you want to take your returns forecasting to the next level, you could calculate it in two ways: first, as a separate independent time series, and then as a percentage of your sales – monitoring the difference between the two would provide you with early indicators if the rate of returns is increasing too much: if the independent forecast clearly starts to exceed the percentage-driven forecast it means the trend is pointing to the wrong direction and that some action should be taken. Bringing in a returns budget can also act as an alert threshold and help inform decisions about what levels of returns are acceptable within the company’s overall financial plans and business goals.
Finally, to really take advantage of the return forecast, one could use it to test different what-if scenarios. For instance: “So if my sales keep increasing with the rate of X and returns keep increasing with a rate of Y, what would that mean in terms of warehouse operations in total, end-of-season stock, markdown costs and/or total margin”. Having a system that manages all this information, and keeps it at hand, helps operations to be managed more smoothly as well as informing strategic decisions concerning the retail offering and returns policy.
To really take advantage of the return forecast, one could use it to test different what-if scenarios.
These principles are effective whether one is running a standalone e-commerce operation or has an online retail presence as part of a multi- or omni-channel business. In my next post I’ll focus more on the latter and discuss how to set up forecasting and inventory management policies for situations when there is more than one channel.
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