Reducing Inventory by Simplifying Forecasting and Using Point of Sale Data

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1 Executive Summary - Atul Agarwal and Gregory Holt MLOG 2005 Thesis 1 Reducing Inventory by Simplifying Forecasting and Using Point of Sale Data Introduction This thesis assesses the value to vendors of using point of sale data (POS) to predict what retailers will order from them. In particular, we look at how The Gillette Company can use POS generated by two of their customers, (Wal-Mart and Target), to predict the orders of all of Gillette s customers combined. The thesis also examines the impact on forecasts of shortening and simplifying the demand planning process. By improving the forecast of orders from their customers, vendors like Gillette can reduce safety stock inventory which is held as protection against unpredictable demand. Our findings indicate that for Gillette, there is very little value in using POS. In general, vendors selling regular use products to a large number of customers may have little use for POS. Shortening the demand planning process, by contrast, has great potential to improve forecasts and reduce safety stock. Gillette currently uses an elaborate forecasting process which it updates monthly. Simply by switching to a basic forecasting technique such as a moving average updated weekly, Gillette can reduce its safety stock by a third. Simplifying and Shortening the Demand Planning Process Gillette currently produces a forecast each month of what its customers will order. Much of the data used in this forecast is collected early in the month. This forecast is then used over the next month to determine how much product to produce. Thus, by the end of any given month, production is being determined by data which is from 4 to 8 weeks old. Since forecasts tend to deteriorate with time, it s easy to see that the forecast made by Gillette suffers from inaccuracy caused simply by the passing of time. We tried to evaluate the impact of reducing this planning cycle so that the resulting forecast would not suffer from being 4 to 8 weeks out of date. We tested the forecast accuracy of several simple techniques over two years of historical data across 11 Gillette SKUs. The method was simply to compare what the forecast accuracy would have been for each technique had they used that technique over the last two years. The best performing technique was a 12-period moving average of historical customer orders. The results below compare Gillette s current ability to forecast the orders of all of its customers combined each week (As-Is) with what Gillette s forecasts would have been, had they been using a simple moving average for the last two years. Daily CV represents the coefficient of variation of forecast errors. This is the metric which Gillette uses to determine safety stock. Reducing this causes a proportional reduction in the safety stock required to maintain the same service level.

2 Executive Summary - Atul Agarwal and Gregory Holt MLOG 2005 Thesis 2 Method Forecast Accuracy Daily CV of error of 4 week forecast Resulting Safety Stock As-Is 74.6% days 12-period moving average of Historical Orders 77.9% days Thus, simply by switching to a simple moving average updated weekly, Gillette can save 6 days of safety stock. By adding in various other simple improvements to the forecast such as correcting for bias, we can reduce the required days of safety stock to 14, for a total of a 33% reduction in safety stock. The point of this section is not to demonstrate the power of moving averages or forecasting techniques which can be performed on a spreadsheet. The point is to show the benefit to Gillette of switching to a forecasting technique which can be updated weekly. In all likelihood, the forecasting tools which Gillette uses now, (complex software, collaboration with customers, and a 30-person demand planning team) could deliver a more accurate forecast if they were updated weekly than that gained by using a 12 period moving average. The problem is that they are only updated once a month. Using POS to Forecast Wal-Mart Orders The well documented bullwhip effect results in an increase in the variability of orders as you move up a supply chain. As the following chart shows, Gillette s supply chain with respect to Wal-Mart is no exception: Wall Mart Bullwhip POS W*M Store Orders Customer Orders from W*M Coefficient of Variation A B C D E F G H I J SKU Thus, the ordering pattern which Gillette sees from Wal-Mart is far more variable than the variability of consumer demand which is represented by POS. This variability is

3 Executive Summary - Atul Agarwal and Gregory Holt MLOG 2005 Thesis 3 usually unpredictable and therefore results in the need for Gillette to carry more safety stock. A common solution to this problem is to use the forecasts of POS made by the customer, in this case Wal-Mart. We considered this option, but a test of the forecast generated by Wal-Mart showed that their forecast was biased extremely high and had very poor accuracy. We therefore elected to ignore the Wal-Mart forecast and create our own based on the POS data itself. Our basic method was to develop a forecast of future Wal-Mart POS based on historical Wal-Mart POS, then use this same number as the forecast of Wal-Mart orders. In the long run, the total orders made by a customer should roughly equal the total sales or POS. Therefore, no matter how variable the Wal-Mart orders, they should always center around the underlying POS. We tested numerous forecasting techniques and were surprised to find that the best forecast of future POS is the naïve forecast. The naïve forecast simply says that our forecast of next week s POS is whatever POS was for the prior week. For most data sets, the naïve is far from the best method due to random jumps in data. But the data set we considered was regular use products (shaving cream) distributed through the largest retailer in the world using every day low pricing. It makes sense, then, that this demand has little variation from week to week and a forecasting technique which places 100% of the weight on the most recent piece of data would be the best. Theory and intuition aside, the naïve forecast was empirically superior and we used it not only as our forecast of future POS, but also as our forecast of future orders. This simplistic method gave the following results: Forecast of Wal-Mart Forecast Accuracy Daily CV Orders As-Is 59.0% period moving average 63.4% 2.29 Naive POS 70.1% 1.98 Consistent with the results of the first section, a 12-period moving average was better than Gillette s current process. More important, however, was that our naïve POS based forecasting method was far more accurate than either of the historical orders based methods. This supports the theory that vendors can mitigate the effect of the bullwhip and achieve better forecasts by basing their forecasts on POS. If Wal-Mart were Gillette s only customer, our work here would be done, and Gillette could enjoy the benefits of lower safety stock resulting from a POS based forecast. Unfortunately, Gillette has another 1,800 customers. Using POS to Forecast Orders From All of Gillette s Customers Combined Like most vendors, Gillette gets the benefits of risk pooling by aggregating the demand of all of its customers in centralized DCs. There is essentially one large safety stock to cover the unpredictability in the demand of all of Gillette s customers. When one customer s order is unusually high, another customer s order may be unusually low and the two will cancel each other out. The safety stock which Gillette maintains is

4 Executive Summary - Atul Agarwal and Gregory Holt MLOG 2005 Thesis 4 related to its ability to predict the total orders from all of its customers. Thus, improving the forecast of one of its customers in isolation (even one as large as Wal-Mart) won t necessarily have a significant impact on safety stock. We tested the benefit to Gillette of forecasting Wal-Mart s orders based on POS by adding this forecast to a moving average based forecast of all non-wal-mart customers. We also tested a combination of a POS based forecast and the As-Is method. The results are shown with the results of the other techniques already discussed. Method Forecast accuracy of national customer orders Daily CV of error of 4-week forecast Resulting Safety Stock As-Is 74.6% days 12-period moving average of Historical Orders 77.9% days Wal-Mart POS added to As-Is Wal-Mart POS added to 12- period moving average of Orders 76.3% days 78.0% days Using a combination of a POS based forecast for Wal-Mart and a moving average of orders yields a forecast accuracy and CV which are roughly comparable to those of a 12-period moving average. These techniques are both significantly better than the As-Is method. It is important to note that the combination technique is also updated weekly and this is most likely the reason for its success compared to the As-Is rather than anything to do with POS. Wouldn t it be nice if we had POS from all of Gillette s customers? To try to get the benefits of this hypothetical situation, we attempted to estimate what national POS would be if we had POS from all customers. Gillette gets POS from Target as well as Wal-Mart and together these two customers comprise about 47% of the volume of the SKUs in question. By assuming that national POS (that is, retail sales for all customers) is proportional to Wal-Mart and Target POS, we were able to estimate a national POS. We then forecasted total customer orders using the same method which we had used for Wal-Mart. This method worked better than the As-Is but not as well as a 12-period moving average of orders. Our assumption that national POS and Wal-Mart/Target POS are proportional is probably not entirely true. We also tried taking a moving average of the last 12 National POS estimates. This method gave better results than just using the standard POS based forecast. At first, we thought this was a promising sign for the idea of estimating national POS. But we realized later that there is very little difference between the forecast generated by a 12-period moving average of historical orders and that made by a 12-period moving average of national POS estimates. This is because over long periods of time (such as 12 weeks), total POS and total orders approximate each other. In fact, we tried each possible number of periods (naïve, 2-period, 3- period. Up to 20-period) and found that the closer the period number was to 12, the

5 Executive Summary - Atul Agarwal and Gregory Holt MLOG 2005 Thesis 5 better the forecast. So a 12-period was better than a 13 which was better than a 14 etc. In short, the national estimate of POS method only produced good results when it approximated a simple 12-period moving average of historical customer orders. Thus, we recommend not bothering with an estimate of national POS. Method Forecast Accuracy Daily CV of error of 4 week forecast Resulting Safety Stock As-Is 74.6% days 12-period moving average of Historical Orders 77.9% days Wal-Mart POS added to 12- period moving average of Orders National POS Estimate National POS Estimate 12-period 78.0% days 75.3% days 77.1% days We also considered the possibility of more customers giving actual POS. We assumed the unrealistically good case of every customer giving POS and Gillette being able to predict each customer with the same improvement as was seen in the Wal-Mart case. Even with these extreme assumptions, safety stock would only be 6% better than that resulting from using a 12-period moving average of orders. The effort of getting POS from every customer would be massive and it s unrealistic to assume that this will happen in the foreseeable future. Conclusion The vast majority of the potential gain for Gillette comes from reducing the planning cycle from one month to one week. Once this is done, the value of POS to vendors with customer ordering patterns similar to those of Gillette is minimal. There are benefits to switching to a POS based forecasting system but they cannot be significant until the majority of Gillette s customers are sending POS. Even if in the future, 100% of Gillette s demand were recorded in POS, a POS based system would result in only a 6% reduction in safety stock. While a 6% reduction might seem important, it pales in comparison to the savings which Gillette can achieve today by simply using a basic forecasting technique with a bias correction and updating it weekly. By employing a 12-period moving average which is updated weekly, Gillette can reduce its safety stock from 21 days to 14 days. As of this writing Gillette is implementing this for the SKUs we tested. If successful, the program will be expanded to include all SKUs in the Personal Care business unit. Gillette estimates that each day of safety stock reduction for Personal Care saves them $100,000 a year in inventory carrying costs. Thus, they will potentially save $700,000 per year. If the program is

6 Executive Summary - Atul Agarwal and Gregory Holt MLOG 2005 Thesis 6 successful in Personal Care and is expanded to include the rest of Gillette s business units (Personal Care is about 9% of the total) then it could save an estimated $7,600,000 per year. The real figure might be significantly higher or lower since other business units may have different demand patterns and since there are many ways to improve upon the forecasts we used (such as accounting for seasonality and promotions). The point is that for Gillette and other vendors similar to Gillette, there may be huge potential savings which can be gained simply by switching from long, complex demand planning systems to simple forecasting techniques which are updated in short intervals. Our thesis does not prove anything in terms of general conclusions regarding POS and the bullwhip effect since we only looked at one company. However, it demonstrates a case where the bullwhip effect is present in the demand data at a single customer level and using POS significantly improves the forecast of this demand. More importantly however, the thesis shows that when forecasting all customers combined, the value of POS becomes minimal. The accuracy of this last forecast is what really determines safety stock. The reason the value of POS becomes negligible when forecasting all customer orders is that the purpose of using POS is to mitigate the bullwhip effect and the bullwhip effect is not significant at a national level. This is because not all of the customers go into bullwhip cycles at the same time. Thus, even if they exhibit the bullwhip effect at a customer level, these bullwhips are not in phase when all customer demand is combined. Vendors such as Gillette should focus on the reaping the easy to achieve benefits of switching to a shorter, more simple demand planning cycle. These benefits far outweigh the value of using a POS based system since the bullwhip effect is not significant at a national level.