Variability, inaccuracy, surprises, pains, … Covid-19 crisis has highly impacted the entire supply chain. I can now see myself again, 19 years ago, as a sales planner. What would have been possible for me to do in front of my planning system to plan the future? We should not consider year 2020, and probably H1 2021, as a relevant set of data for forecasting calculation. So, should I pick 2019 as the new reference, and delete 2020 from my data? My next S&OP run is coming, and I must find a solution to deal with sales forecasting after Covid.
Making your process demand driven? It’s a given!
As far as I know, some advanced practices show that it is possible to get rid of those inaccurate forecasts that sales or demand planning teams were used to do. In that case, Demand-driven MRP is the best example. Based on the real demand, I can operate my supply planning without using precisely wrong forecasts.
Anyway, I will always require a rough-cut forecast, per product family, for long-term capacity anticipation as well as critical raw material purchases or for “really” new products. This new methodology has proven benefits and gives the chance to put effort only where we need it. If you do so, you won’t waste time anymore and increase the accuracy of your Sales forecasting after Covid!
Sales forecasting contribution in the S&OP process
Also, the best practice we always recommend at Citwell is the collaborative forecast that involves all the teams, investigates many possible alternatives and points out the sources of deviations. Where are the stable sales segments? What are the sourcing alternatives? Do I have a real supplier risk assessment? May I anticipate ground transportation rather than flight? … so many questions we must answer before making a good S&OP decision. Therefore, you’ll see some decision loops that will enrich the process.
At the end, we must make sure that the plan is connected with the financial forecast: after Covid, the S&OP restricted to demand vs supply balance is not sufficient anymore.
In that way, you should probably check if you well included those factors in your plans:
- Analyze the variations of change rate (the euro/dollar rate was close to 1.1 in march 2020, it’s now 1.2)
- Check raw material price variations, as well as electronic components global shortage, if it impacts your procurement plan
- Anticipate transportation cost changes, and also consider the shipping container global shortage
- And finally, making regular gap analysis with the initial budget will be necessary to reconciliate your figures
Will AI replace forecasting?
Fortunately, big data provides us with advanced solutions (AI, Machine Learning..) that can help solve the issue. First, they will increase our knowledge of our customer’s behavior. According to a Gartner’s survey, 45% of companies are already using machine learning and 43% of them are planning to use AI-powered demand forecasting within two years. Let’s hope those solutions will help supply chain teams make better decisions, and will not replace human’s fine analysis !