With clients, we regularly encounter S&OP meetings where demand planners are challenged by the Sales director: “you present me a growth factor in the demand plan that is based on an analysis of mere historical data. So, how is the growth that will come from realizing the sales strategy incorporated?” And the ultimate consequence of this feedback is that the demand plan is not accepted by the commercial management team.
Of course, there is still a demand plan that can be used in the S&OP meetings, but it is more the demand plan of the Supply Chain department than a plan that is endorsed by the whole organization. For sure, this is not the approach that paves the way to a real integrated business planning process as S&OP is intended to be. With the consequence that the relevant decisions that really steer the business, still are taken outside of the S&OP process.
Can another approach be taken here? An approach in which the demand planner is helping the commercial team as much as possible in realizing their commercial targets? An approach that helps the commercial team in defining and executing the most effective commercial activities to drive sales and margin, instead of just accepting a single figure made up by Supply Chain?
The basic ideas and techniques behind this new approach, have been present for more than a decade: instead of bringing just a figure, the demand planner should bring insights. Insights in what are the main drivers behind developments in demand. Insights in customer preferences and insights how customer behavior can be influenced. Bringing insights with these sales drivers, enables the sales and marketing teams to select the most effective commercial activities to influence demand volume and value in the aspired direction. Future commercial approaches will be reflected directly in the demand plan, making the endorsement by the commercial teams much easier.
The good news is that the recent developments in big data analytics and powerful cloud computing boost this driver based demand planning approach. Most obvious application area is promotion planning, but sales driver based planning approaches can also be found in price elasticity, localizing propositions and assortments, sales force focus and effectivity, order pattern stabilization, impact of external drivers as weather expectations etc.
Recently, Involvation organized a knowledge sharing and discussion event on Digital Planning with themes like big data analytics and cloud computing, machine learning and artificial intelligence, sensorics, the internet of things, digital twins and so on. To trigger sharing of thoughts and opinions, we challenged the group with some statements around this digitization of supply chain planning. One statement in the debate was: “We could improve planning when moving from historical to driver based forecasting”.
The striking result was that over 80% of the participants agreed with the statement, only 10% fully disagreed. It is great to see that within demand planning departments, awareness is growing that the current traditional process of delivering a demand plan based on the traditional time series analysis has to develop to stay relevant for the commercial department. Awareness is the first step towards implementation. Let’s go for it.
Do you agree or disagree with the statement? Or do you need some more information? Feel free to contact Peter Tielemans by sending an e-mail.