Keep Your Demand Forecasts Up-to-Date

The reorder point world for a moment, it’s easy to understand why keeping an up-to-date forecast wasn’t of paramount importance. After all, once the order has been created, the forecast has done its job. If things start to change quickly, there’s no point in changing the forecast because you’re within the lead-time. Your only option is to change the order quantity or attempt to expedite. If things do go according to plan, you won’t need to take any action until the item comes up for order review again, at which time you’ll just be accepting or rejecting the resulting order quantities anyway. Contrast this with a time-phased planning environment. Ideally, there is no order review. The system is constantly re-calibrating itself and making necessary adjustments on a daily basis. Put another way, the system automatically conducts an order review for every item every day. In addition, the demand forecast is used to plan activities well beyond the lead-time – changes that are happening today will affect the entire planning horizon. Because the forecast has a greater purpose than just making order suggestions, the ordering lead-time is irrelevant in the demand planning process. And while it may be too late for a forecast change within the lead-time to impact purchase orders that are in transit, the entire forecast horizon should be managed the same way to protect the integrity of the entire plan.

2. Use the Operational Forecast to Validate the Sales Plan

Few people will dispute the value of having a sales plan. It’s a great tool for
focusing and motivating marketing efforts to achieve a goal. Although a lot of
work and analysis goes into formulating this plan, it must be recognized that the assumptions that went into its creation become more outdated with each passing week. By contrast, the 52-week operational forecast is constantly updated with new information from the market at item level. This gives the marketing department an unprecedented ability to directly compare the operational forecast (rather than just history) to the budget. Variances can be continually tracked to identify root causes and take action to get the plan back on track: Which of the original budget assumptions aren’t coming true? Do I need to schedule more promotions for a category or beef up the assortment in another? The decisions made would feed back into the operational demand planning process (in the form of promotional or new item forecasts, for example) and cycle through again. While it may be tempting to want to stick with the original plan, it’s always a wise practice to defer to the most recent information from the market. Hoping for the plan to come true is not really a strategy. By always using up-to-date information to make course corrections, the sales plan has a greater chance of becoming a
reality.

3. Measure the Process, Not the People

While it’s important to measure the output of any process, how you use these measures is even more important. A common misconception is that the best path to forecast accuracy is to make demand planners accountable for it. While accountability is a key ingredient for improving any process, you have to make sure that people are only held accountable for things that are within their scope of control. If you think about most supply chain activities, with some concerted effort, near perfection is an attainable goal. Think of inventory record accuracy, data accuracy, on-time delivery – with the exception of unusual circumstances, these things are largely controlled within the process. The only failures that can’t be weeded out through root cause analysis and procedural change are those things that are completely unavoidable. As a result, the processes that produce these outputs can reach for specific goals (98% bin-level accuracy, 95% perfect orders,
95% on-time delivery) and failures can easily be traced back to find the root
cause. Contrast this to the demand planning process – to achieve a perfect result would require predicting the future with 100% accuracy by item by week. While several factors that influence demand can be internally controlled (e.g. pricing, promotion strategy, market penetration), there are countless others that can’t (e.g. weather, geopolitical events, consumer sentiment and plain ol’ randomness!). With so many uncontrollable influences, successful demand planning is not so much about trying to achieve a particular goal for accuracy as it is about:

• Understanding and managing the things you can control to try to
produce a better result and;

• Reacting quickly when one of the uncontrollable influences takes you
by surprise Perhaps a better way to think of measuring (and, by extension, improving) the demand planning process is within the scope of the methods of statistical process control, pioneered by management guru W. Edwards Deming.