Sales forecasting in times of crises at DSM

Responding to the Lehman Wave

Sales forecasting and Supply Management during the Credit Crisis

Concise summary of this best practice
A system dynamics model has been developed in order predict demand development throughout the supply chain in times of crises.
Good insights by using this type of modeling enable managers to make the right decisions and to gain competitive advantage out of the crisis.
Using a system dynamics model described in this best practice, DSM was able to predict its sales with astonishing accuracy, and came stronger out of the crisis.

Key terms
Cumulative de-stocking, synchronized de-stocking, bullwhip, improvement of sales forecast to enhance S&OP process.

Relevant for
Companies with a desire to improve supply chain visibility; Companies who want to understand the impact of their end markets;
Companies with a desire to understand their supply chain dynamics.


Panic takes over
Shortly after the bankruptcy of Lehman Brothers, in October 2008, a strong dip in the manufacturing industry was visible.
The crisis in the US housing market crash had turned into a credit crisis, and the bankruptcy of Lehman caused a shockwave
of panic throughout the international business community. Both consumers and producers lost confidence and the sales of
large purchases dropped sharply. Many companies focused on cash by cutting costs, by reducing working capital in inventories
and investments, and by delaying their purchases. This makes forecasting an extremely difficult job. Often, companies use Sales
or Sales and Operation Planning (S&OP) departments to make accurate forecast based on historical data, current sales and customer
information. However, historical data do not provide any solution, customers also do not know what to expect, and S&OP managers
basically remain clueless. In the rampant times of panic after the collapse of Lehman, traditional forecast processes become totally
unreliable. The panic will not disappear but accumulate, unless more sophisticated methods are developed to understand the real
situation. This is exactly what the management of DSM NeoResins+ did. Together with Eindhoven University of Technology, DSM
focused on modeling the dynamics within the supply chain to forecast the future sales upstream. This is the story of DSM’s research
on the bullwhip effect.

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Key terms: 
credit crisis

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  • Process industry (chemical and other)
  • Pharmaceutical industry / Health Care
  • Capital goods industry, especially service parts logistics
  • Retail & Internet / E-commerce
  • Consumer packaged goods
  • Mass assembly
  • High-tech (consumer electronics, semiconductors, contract manufacturing, communications)
  • Logistics service providers



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