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How do I forecast during Recession?

In a client meeting on Friday – the 13th, I encountered a “scary” statement!! The category manager told me that his gut forecast was more accurate than the one generated by his ‘expensive forecasting system’ for last quarter or so. The symptom was recent and the forecast was going away by as much as 40%!!! Could this be symptom of a recession? How do I forecast during such times? Complex algorithms are far more powerful in finding out hidden patterns and extrapolates them beyond the capacity of human mind. Then why would such powerful models fail to detect a recession which is so obvious?

Forecasting systems have typically 12-36 months data. This works well in identifying patterns during regular times but not during recession. The demand falls dramatically during such times. Even before the system detects the dramatic drop in demand, probably a quarter or two has already passed!! The result is burgeoning inventories in the warehouse.

The key to forecast during recession is to detect it first. The first real indication comes when Forecasting systems miss three consecutive forecasts. During recession they continue to over forecast. The flip side of this rule is that if you forecast only once a month or a quarter, you still lose the plot! The only way to capture is to manually compare last 6 weeks forecast. During recession, demand for consumer products reduces dramatically. Mapping end consumer demand is the best indicator of slowdown. This means, for retailers, they need to look at their weekly sales of baskets and for Consumer Product companies – look at secondary or tertiary sales data, or syndicated sources for detecting the rate of slowdown.

The second step to this is to recalibrate the forecasting system. It could be done through variety of ways. One of the most effective ways to do it is to reduce the history window used in forecasting. Instead of 24 months, 3-6 months data would provide better picture. It is possible that some of the algorithms may not work with this little data. But then, if human mind can forecast better than the complex algorithms during recession, why not use simpler algorithms with less history – bootstrapping, weighted moving average, exponential smoothing etc. These algorithms are not as accurate as ARIMA, Regression or Nerual Networks, but performs better than them with a very short history. Typically, recession lasts anywhere between 6-8 quarters. These models should be deployed during such times. If the system is already using these algorithms, all the coefficients needs to be recalibrated every 2-3 weeks.

Most of the companies do forecast at a distribution center level and at a product level. During recession, customers become more price sensitive and are likely to switch the channels for buying the same product. Channel forecast becomes a balancing factor in manage inventory and satisfying consumer demand at a low cost. Distribution centers serve all channels and they are likely to experience different variability during recession. If the channel level forecast is brought into the picture, it will iron out inventory mismatch between distribution center and end consumer demand resulting into significant cost savings, higher fill rates and improved customer experience. After all, demand forecasting is all about improving customer experience, and there is no better time to do so, when everyone is going wrong in doing so… recession!!


I think this is a challenge for virtually all companies today. As you accurately point out, trying to predict future demand based on historical data is virtually impossible right now.

In addition to your good ideas, I would add a few others:

- Increase customer collaboration. Some companies call this demand co-planning. The basic notion is that you need to move from a handoff process to a collaborative one, where you and your largest, most important customers are collaborating more regularly on their demand requirements. If you don't already do this as a core part of your demand planning process, you need to because latency and intermediaries add more risk to the process.

- Increase your emphasis on demand management. Balancing demand and supply has always been a challenge, but more so in an increasingly volatile environment such as we have today. The traditional handoff process between the demand-side of the business to the supply-side needs to be replaced with a "bridging" process that fosters more collaborative demand and supply planning. These groups need to take a more holistic and real-time view of the inter-dependencies and resulting impacts of changing demand and/or supply. A key part of this process is simulating various possibilities and their associated action alternatives to increase your preparedness for the various scenarios that may unfold.


Thanks for your comments. Collaboration is indeed a very important process during recession - not just with your key customers, but also with the suppliers. One of the most obvious challenges during collaboration process is sharing of "close-to-heart" data with your supply chain partners. The need of the hour is to increase the level of trust and communication. I believe recession cannot be resisted by one player in the supply chain, but needs resilience and discipline by the entire supply chain!

Another key input is the use of Analytics. It's important that organizations / supply chains understand where they are spending the money and how effective it has been. This is the time to cut fat and add muscles - which will not just see the organizations through this recession, but give them a decisive edge when the global economy is up!!

Based on your comments of increasing trust between supply chain partners, it is likely that trust will increase and collaboration will be better in a recession than any other time as the impact is felt by all players and there is effort by all players to weed out the inefficiencies in the supply chain . Also along with recalibration of your forecasting systems which is definitely a good idea - forecast review of system generated forecast is the key - as human and market intelligence can definitely add value in this scenario. User reviewed and modified forecast sent to the replenishment systems for downstream processing would help to effectively control inventory. Along with this, organizations can also relook at safety stock levels at different nodes which also should be recalibrated to make the supply chain more responsive.

Based on the last paragraph above: My company is looking to implement forecasting/demand planning software and we were looking to forecast at a sku level per DC - but we have a request from Finance and Sales to break it down by customer also as they look at it from a customer level - in which case the characteristic combinations each sku per DC per customer run in millions and it may get cumbersome to forecast. What are your thoughts?

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