We often get questions or comments about the level of supply chain maturity required for companies to be ready to implement DDMRP and realize the significant operational benefits it brings.
These questions are sometimes paradoxical.
Many companies tell us things like: “DDMRP sounds interesting, and well-suited to our environment, but we must first achieve more maturity in our MRP practice.”
On the other hand, some people say, “DDMRP seems a bit too basic for companies with mature supply chain practices, but for less advanced companies it seems to give good results.”
Mmhh. What level of supply chain maturity is too little or too much to benefit from Demand Driven tactics?
How MRP Fits Into the Picture
For most companies, acquiring complete mastery of an MRP model is a long-term project. Forecast accuracy, reliability of technical data, parameterization of lead times and lot sizes, inventory accuracy, planning discipline, training in MRP2 best practices, sizing and maintenance of safety stocks, etc. This is often a never-ending task, which mobilizes internal teams and support consultants over several years.
The results of these efforts can be significant, but are often disappointing, and they drift over time when key people change roles.
Some of these practices are equally important to DDMRP. If you don’t have the discipline to pass inventory transactions and your teams can’t rely on computerized inventory, your DDMRP model won’t work.
Is it necessary to go through the MRP process?
Clearly not. There are many examples of companies that have directly implemented DDMRP without first implementing MRP.
When implementing DDMRP, problems with technical data, lead time and lot size parameters, stock settings, operating discipline: all these aspects are dealt with in a much simpler, faster and more obvious way than in a traditional MRP approach.
For example, in a recent implementation, the lead times for certain manufacturing steps in the ERP were much longer than the actual processing time. This had been the case in the ERP for a long time and it didn’t shock anyone. As soon as the DDMRP model was fed with these elements, it became clear to everyone involved that these lead times were leading to inventory targets that were far too high, and to the generation of production orders that were far too early. A few days later, the lead times were corrected in the ERP.
The implementation of DDMRP immediately provides increased visibility on the relevance of parameters, and initiates a continuous improvement process that greatly limits the risk of drift over time.
On the other hand, if you are a company that is very mature in MRP practices — if, for example, you are “Class A” according to certain standards — there is a risk that your teams will find it more difficult to transition because they will have to unlearn certain practices… I know it for a fact as I went through that myself!
DDMRP and Supply Chain Maturity
For some, DDMRP seems too basic. For example, the way it sizes inventory and determines red, yellow, and green zones doesn’t seem scientific enough — what math supports the process?
Several voices, for example in academic circles and among non-DDMRP software publishers, express the view that the Demand Driven approach is insufficient, or even obsolete.
Over the years, seemingly more sophisticated practices have been developed and promoted. Safety stock formulas, multilevel optimization, operations research, artificial intelligence: it all seems more scientific, mathematical.
Recently, a skeptical interlocutor told me: “We see many testimonies of companies that progress thanks to DDMRP, but it is probably because they were not very mature. They could have progressed more with the XYZ approach.”
OK. However, it is remarkable to note that many of our customers who testify about the progress they’ve made with DDMRP were already very mature companies, with very good inventory and service performance, a strong forecasting discipline, a global ERP instance with a deployed standard model and trained teams, effective S&OP, etc. Many of them have previously conducted APS deployments, some have tried Multi Echelon Inventory Optimization (MEIO), and some have even tested large scale safety stock optimization with Artificial Intelligence. However, they have embarked on a Demand Driven transformation and are showing significant results.
How is this possible, when other approaches seem more scientific, powerful, and rational?
There are probably a few key factors:
- A supply chain is not simply driven by algorithms. It is driven by men and women who make decisions based on information. The more understandable this information is and the more it leads to continuous improvement, the better the decisions will be.
- However scientific they may be, the majority of optimization approaches are based on a push flow model, not a multi-level pull flow model. The mechanics of pull flow replenishment ensure a constant adaptation in real time, the superiority of which has been proven for decades.
- The more complex our supply chains are, the less they can be optimized: they need to learn and adapt.
- DDMRP does not exclude technology! Data science and artificial intelligence techniques, when relevant, are implemented within our solutions.
Understanding the Demand Driven Journey
Each company is different, in its business model and its level of maturity. The strength of the Demand Driven approach is that, whatever your model and starting point, it will allow you to progress, both during the deployment of the project and over the long term.
