We regularly receive S&OP and Forecasting questions from the IBF Membership base that are answered in the Journal of Business Forecasting (JBF). I have identified 5 common questions with my responses below.

In your judgment, should we centralize the forecasting function?

I am more inclined toward centralization. If it is centralized, the department will be independent, and thus forecasts will be unbiased. There will be a single point of contact; if anyone needs a forecast, he or she will know where to go. Further, it will be easier to develop consensus, and the forecasting department will be highly accountable. If the forecasting function is decentralized, everyone will do their own thing. It will be difficult to get input from others, which is essential in preparing forecasts. There will be multiple forecasts, making it difficult to align supply with demand. In addition, in a decentralized environment, if resources are needed somewhere, there is a temptation to drop some of the people from the forecasting staff. Further, if someone leaves, there won’t be anyone to train the newcomer.

How much inventory should we hold so that we have the right items, at the right place, and at the right time?

It all depends on whether the product is high in value or low in value, whether the product is highly predictable or difficult to predict, what its production cost is, and what its lead time is. You may like to hold more inventory on products that are high in value, but difficult to forecast. On high-value products, you don’t want to lose any orders, so it may be better to hold a little more inventory for them. On low-value products, if the cost of production goes down significantly when produced in larger lots, you may like to hold more inventory. Lead time also makes a difference. You may like to hold a little more inventory of products with longer lead times. There are some formulas available that are used to determine safety stocks. Calculation is generally based on things such as forecast errors, customer service, and lead time. Based on the 2015 survey by the Institute of Business Forecasting, when all industries are combined, businesses hold 37 days of inventory of finished goods, and 27 days of raw material.

What are the pros and cons of forecasting at an aggregate level (total U.S.) versus DC level? Which is more common and does forecasting at a DC level require additional headcount?

At which level we should forecast depends on the objective. If the objective is to determine how much the total sales would be for the whole year, we should be forecasting at an aggregate level. This is good for strategic planning, but not for operational planning, where most of the forecasts are used. For production planning, you would need forecasts at a most granular level. You would need forecasts not only at a DC level but also at a SKU level. How many forecasters required would depend on the number of things, including how many forecasts have to be prepared and what level, and how difficult is to forecast those products. All products are not equally forecastable.

New products are difficult to forecast, but their number is increasing every year. How should we handle them in the S&OP process?

There is no question that the role of new products is on the rise. According to the recent survey by the Institute of Business Forecasting, about 22% of sales revenue now comes from new products. Therefore, they need special attention. The best way is to split new products into two sets: one group may include products that are relatively easy to forecast. These include new products resulting from line extension and product improvement. The low-value new products can also be lumped in that group. The second group includes products that are new to the company and to the world, and the ones that result from market extension. The products of first group can be handled as a part of the regular S&OP process. However, products of second group require special attention. For that, we need a separate team that generates forecasts and reviews their performance on a weekly cycle, and then develops a course of action. The team should have people who have the power to make decisions so that decisions can be implemented right away.

In measuring forecast accuracy and bias, should we keep the same denominator?

We should have the same denominator, which is actual, whether we measure forecast error or bias. Otherwise, we will be looking at the forecast error from one perspective, and bias from another. We use actual as a denominator in measuring forecast accuracy because we want to see how the forecast deviates from the actual, not how the actual deviates from the forecast. The same is true with bias.

If you have an S&OP, Demand Planning, and Forecasting question you would like answered, please send your questions to jainc@stjohns.edu. Your comments on all are welcome.

Meet Dr. Jain at IBF Academy 2016 in Las Vegas, where he will be leading the session on Forecasting Accuracy and Metrics.