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Over the decade, the profession of demand forecasting has been renamed many times to reflect the broader designation of the role. Demand management has been a term that has often been deployed. The term suggests that this is not just about forecasting, but also includes the shaping of demand, for instance by pricing and management of promotions. Als the term Demand planning has been frequently deployed as well. This term suggests that the role also involves the allocation of supplies to the demand, or maybe even actively planning or shaping demand to meet supply.

In reality, however, many companies that have deployed alternative names for the forecasting role have not done more than actual demand forecasting, i.e. the estimation of future demand – often based on some algorithm that uses past sales information augmented with some human judgment. The current Coronacrisis however presents both a need and an opportunity to really develop this function.

With drastic demand changes, algorithms need to be shut off

Demand forecasting algorithms that are deployed are based on time series of previous sales. Most of these are using relatively simple statistical methods that extend and smoothen previous time series. In the last few years, there has been much promise of machine-learning (sometimes dubbed as „Artificial Intelligence”) that takes additional external information, such as the weather or the pricing of competitors, into account when determining the forecast. With any of these algorithms, the basic presumption is that the underlying system does not change: relationships between independent variables (previous sales, assortment, weather, etc) and dependent variables (future demand) remain unchanged. Obviously, this is not the case in the current circumstances. Hence, it makes perfect sense to switch off your automated forecasting support tools, especially if they are „hands-of-the-wheel” linked to orders being placed by your replenishment system without any human interference.

When will demand go back to normal?

This is of course the million-dollar question that everyone is facing. In all cases, it is important to first estimate when the situation may go back to more normal patterns of demand. This time estimate is difficult, but there are some basic elements that you can work with:

  1. Remaining time of the governmental measures that cause you drastic demand change. From the China situation, we know that this may likely be at least 3 months; maybe longer if the hospital capacity is really stressed.
  2. Lead time between you and the consumer market: the is the cumulative lead time that a molecule, part, or product leaves your plant and is being consumed by a final consumer. For a retailer, this is a just a few days, while for a chemical producer this can easily be half a year.

Demand changes at the consumer level will roughly take the sum of these two times to reach you. However, they are affected by inventory adjustments:

3. Cumulative surplus or shortage of inventory in the supply chain: if your customers or consumers have a shortage of inventory now compared to how much they would stock normally, this will need to be brought back to „normal”. This could imply additional orders in case of shortages, or reduced orders in case of surpluses. For instance, consumers will likely not buy toilet paper for a while in many countries, while the shortage of electronic parts in the automotive supply chain will need to be replenished. Such inventory replenishment requests will reach you more or less instantaneously and will not face the delay above. We have learnt this from the recovery after the credit crisis.

Finally, all of this will be exacerbated by the infamous bullwhip effect. Again, from the credit crisis we may reasonably estimate that the order inflation may easily be in the tens of percentage points, with this inflation being larger if you are further upstream from the consumer.

Real demand planning requires strategic choices and subsequent analytics

Current demand planning requires both strategic choices and subsequent operational analytics. The strategic choices depend on whether your market is currently facing (huge) drops in demand or (huge) increases in demand, or whether there are significant demand shifts between products or channels.

Currently facing drops in demand

Of course you are currently looking to serve alternative markets or trying to make alternative products for which there may be demand. But in many cases this may not be feasible. Then what remain is trying to estimate when demand will go back up, which I have discussed above. It makes sense then to decide whether you want to build up inventory in advance. If you have the financial means to do so and your products are not persihable, this may be a very sensible strategy. Demand will go up, and more than you expect, as discussed above, but the exact timing is hard to tell. What is critical is to involve your sales force in this plan. They need to be aware of the constraints of supply.

Currently facing demand increases

You are currently scrambling to make supply meet demand. Several companies have reduced assortment size in order to keep capacity up (by saving on changeover times). At some point, demand will go back down. In order to avoid a bullwhip, it is really important to keep a very clear picture of the actual consumer demand and the accumulated inventories between you and the consumer. Thinking cumulatively rather than incrementally makes a lot of sense. From a planning perspective, you will need to ask from your sales force to do something they don’t like to do: sensing in the market how demand will go down eventually. Your sales force needs to understand that if they are too late with their sensing, they will be causing potentially large amounts of unsold inventory somewhere in the supply chain.

Currently facing demand shifts

In particular channel shift have been happening: from out-of-home to supermarkets and from in-store to online. The first question of course is how much of that remains after the crisis eases. I am reading many reports that this is the definite breakthrough of online grocery. I seriously doubt this. I am definitely not a marketing expert but the current online experience is poor with extensive delays in delivery and many out of stocks. ALso, after having been locked down for months, I can imagine that going out to a store will be a great experience for many. Hence, the argument could just as well be made that online sales will drastically decrease after the crisis eases. Hence, I think it makes sense for any supplier to hedge their bets: a bit of additional inventory makes sense, and building the ability to shift demand between channels or products is a worthwhile investment.

I serious doubt the many reports that this is the final breakthrough of online grocery shopping

In conclusion

All of the above implies that companies will need to set up true interdisciplinary demand planning teams that actually have the ability to plan. Such teams should be able to make (or prepare) strategic choices and be able to conduct analytics of the consequences of such choices. This requires different information than just prior demand; it requires knowledge of the full state of the supply chain. And much of this requires humans to do the job.

Disclaimer: This is not the direct result of any specific academic study. The above is my current analysis and interpretation based on prior research conducted by me and others in the area of supply chain management, inventory management, and demand forecasting during crises. It is not an advise for anyone specifically. Anyone that feels this is of use is welcome to let me know in the comments below. If you have additions or corrections to make, I would really welcome them in the comments below.

Jan Fransoo is a professor of Operations Management and Logistics and Dean of Research at Kuehne Logistics University in Hamburg, Germany. Has published over 130 papers in academic journals and books on topics ranging from production planning and supply chain management to inventory management and transportation optimization. 

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