Since statistical forecasting is always inaccurate and even more so when we go down to SKU level, there are businesses which look to increase certainty with actions such as asking their distributors, main customers and commercial networks for their estimations, months in advance, when in reality, they also suffer from market uncertainty. Even so, they base their production on such estimates, further increasing the variability with low quality information that directly affects the operation with constant changes.
Sales forecasts are brilliant for medium and long term planning, but in short term production planning, mixing such forecasts with last minute promotions, customer forecasts and firm orders becomes difficult, complicating not only decision making but causing excess of some products and shortages in others.
We must understand that uncertainty does not only come from demand, but also from the sources of supply and from the very variability of our company, not only in execution but also in decision making, stock limitations, production variability etc.
And the best thing: this will continue to occur because we don’t live in a perfect world. Because we must also increase productivity and profitability, reduce investment in inventory, and manage long delivery times from supply points that in some cases are very far away.
So what can we do? How can I plan production if the accuracy of my forecasts is limited and every small change affects the whole chain planning?
In response to this fundamental problem, everis, in its constant search for best practices, has included in its portfolio a methodology particularly suitable for companies affected by volatility, uncertainty, complexity and ambiguity (VUCA).
The methodology Demand Driven MRP (DDMRP) combines advances such as Lean, Six Sigma, and Theory of Constraints to align production with actual market demand through a structure that:
- Strategically segments the supply chain and establishes "firewalls" that isolate the effects of volatility by means of "buffers" not only for inventory, but for time and capacity.
- Protects the flow by automatically updating the necessary "buffer" levels to cope with the volatility levels.
- Determines when and in what quantity purchase or production orders should be released, using data from actual demand.
- Manages deviations from supply or demand through alerts, i.e. "usual" demand management is automated and only data that falls out of the norm is managed, to determine if it is indeed a one-off problem that needs to be solved, or if demand is changing and parameterization adjustments need to be made.
- Clearly sets and displays priorities for production scheduling.
- Facilitates decision making, not only in execution, but also in planning, providing relevant information, and improving the company's S&OP process.
Published in 2011 by Carol Ptak and Chad Smith after 15 years of research, DDMRP has been trialed in different companies, demonstrating great benefits in both service level improvement and stock reduction.
In May 2019, the Massachusetts Institute of Technology (MIT) conducted a study with the opinions and results of companies that have applied this methodology.
In this study it can be seen that the companies reported improvements of up to 23% in the level of service with reductions of 20% in the inventory level. The delivery time to the client was also reduced considerably, gaining a relevant competitive advantage.
It’s no surprise that Dick Ling, the creator of S&OP supports this methodology.
Dick Ling Creator of the S&OP concept.
It is important to note the support that Demand Driven MRP is receiving from SAP, Dynasys or other emerging tools, which already support this planning philosophy.
After conducting simulations to validate its effectiveness, we can conclude that it is a new planning philosophy that facilitates operations in a practical way and without too much investment, and is accessible to companies, weather old or new.
I am sure that this way of working will create a lot of talk in the future and allow many companies to transform themselves to increase their ROI and at the same time naturally live with the uncertainty which has come to stay.