The Big Data Era has changed how organizations conduct planning. Although companies have always attempted to forecast what the future would bring and how it would impact supply chain operations, they didn’t have the advanced analytics available today. In some ways, they were plotting their course to future steering by their wake (i.e., using primarily historical data). Even worse, many organizational departments maintained their own databases and planned future activities in isolation. Lora Cecere (@lcecere), founder and CEO of Supply Chain Insights, insists, “The siloed organization is insular. It cannot sense, and is slow to adapt.”[1] By breaking down silos, companies can think more horizontally and more outside-in. Cecere explains, “Horizontal processes within the supply chain organization help to align and orchestrate demand and supply to deliver on the business strategy. Effective design of horizontal processes reduces silo friction and improves organizational cross-functional alignment. It enables growth.” She also recommends that organizations transform their planning from inside-out to outside-in. “An outside-in process design,” she explains, “starts at the market and uses channel data to sense and then shape demand opportunities while mitigating risks through cross-functional, horizontal processes. It is designed using optimization and simulation tools, and the network design processes are iterative on a monthly basis to improve the end-to-end set of horizontal processes.” In a more recent article, Cecere notes, “While I am advocating rethinking supply chain planning, for some consultants, the only path forward is the adoption of DDMRP. The recommendation is a broad-brush approach. No matter what I write on demand planning, the response is to blindly deploy DDMRP.”[2]
Demand-Driven Material Requirements Planning is Not the End Game
As Cecere notes Demand-Driven Material Requirements Planning (DDMRP) is gaining traction. In fact, too much traction for her liking. Such attention is not entirely unexpected. A couple of years ago an article by analysts from iCognitive called DDMRP “revolutionary.”[3] The basic concept of DDMRP was introduced in 2011 by Chad Smith (@demanddrivenmrp) and Carol Ptak (@itsallaboutflow), who partnered to create the Demand Driven Institute. In some ways, DDMRP is an amalgamation of concepts. The Demand Driven Institute defines it this way, “Demand Driven Material Requirements Planning is a formal multi-echelon planning and execution method to protect and promote the flow of relevant information through the establishment and management of strategically placed decoupling point stock buffers. DDMRP combines some of the still relevant aspects of Material Requirements Planning (MRP) and Distribution Requirements Planning (DRP) with the pull and visibility emphases found in Lean and the Theory of Constraints and the variability reduction emphasis of Six Sigma.”[4] The following video helps explain the concept.
Analysts from iCognitive assert one reason companies need to adopt DDMRP is because of the complexity found in today’s supply chain. “Today,” they write, “there are more complex and planning scenarios than before. The past is no longer a predictor of the future.” To make their point, they note that in 1965, Colgate and Crest each manufactured one type of toothpaste. In 2012, Colgate produced 17 types of toothpaste and Crest made 42. Whenever “complexity” in the supply chain is raised, I immediately think about how cognitive computing can help organizations deal with it. In the years ahead, I predict cognitive computing will an integral part of outside-in planning solutions. According to the Demand Driven Institute, “Demand Driven Material Requirements Planning has five sequential components. … The first three components essentially define the initial and evolving configuration of a demand driven material requirements planning model. The final two elements define the day-to-day operation of the method.” The following graphic describes each component.
The Demand Driven Institute notes, “DDMRP is most commonly the start of an organization’s transformation to a Demand Driven Adaptive Enterprise.” Most often, a demand driven adaptive enterprise is referred to as a digital enterprise and the Digital Age requires a digital enterprise (and a digital supply chain) to thrive. Stefan de Kok (@wahupaSCM) notes that DDMRP is most useful to companies that source everything locally. Companies with lengthy supply chains can’t do anything to reduce the amount of time it takes a particular resource or part to move from overseas to where it’s needed. He notes transportation time “is a big gap that must be addressed.”[5] He adds, “Whilst a strength of DDMRP is that it can determine WHERE product should be stocked, one of its biggest weaknesses is determining HOW MUCH to stock in such locations. Safety buffers are determined backward looking, using archaic logic with wet-finger parameter values. This is equivalent to using a naive safety stock, worse than using a traditional safety stock and certainly much worse than determining stocking levels through a multi-echelon inventory optimization. This is masked rather than addressed by the use of multiple so-called zones of inventory levels.” Advanced analytics can help with this latter challenge so that an enterprise doesn’t have to rely solely on historical data. De Kok recommends companies objectively evaluate DDMRP for themselves. He concludes, “DDMRP is built on strong core concepts and it has a lot of potential. However, the way it is marketed and promoted today pretends to be more than it is.”
Cecere indicates there are things to admire about DDMRP. “For the record,” she writes, “I like the fact that DDMRP calculates buffers across the enterprise. (I call this demand translation, and the market has been slow to see the need for this.) The recognition of this need is a great gift to the industry.” One of things De Kok likes about DDMRP is that it’s process focused. “DDMRP does some things traditional MRP and many other traditional approaches did not focus on or ignored completely,” he explains. “Notably, a lot of approaches have historically been inspired by innovative system capabilities, not so much by innovative processes.” He continues:
“Taking inspiration from queuing theory, LEAN, JIT and other schools it enables companies to determine where product should be stocked and in what form. Its focus on flow and reduction of queues around and between each stocking location, allows for effective lead times to be reduced more than would otherwise be possible. A main objective is to reduce the maximum effective lead time between any two sequential stocking locations across all the steps a product traverses from supplier to customer. As soon as a sales order comes in for an item, every stocking location in its path independently and simultaneously determines if it needs to replenish or not. This behavior is why they dubbed the approach ‘demand-driven’, the DD in DDMRP.”
De Kok reports that SAP has now adopted the DDMRP methodology. As a result, he writes, he expects “this activity to get much stronger.” Even though SAP has jumped aboard the DDMRP bandwagon, the company is a fairly new advocate. A little over a year ago, SAP’s Rui Pedro Dantas lamented, “In the SAP world, there is still a deafening silence about this subject.”[6] Cecere and Shaun Snapp (@ShaunSnapp) worry that SAP’s promotion of DDMRP could force many companies to use planning methods ill-suited to the task at hand. Like Cecere, Snapp thinks DDMRP is overrated. He notes, “MRP is not the most sophisticated method of matching supply and demand. Inventory optimization and multi-echelon as a planning method is far more advanced than MRP. Unlike MRP it has the math to compare stocking locations across the network and set stocking positions while cognizant of the stocking locations around the stocking location.”[7] This kind of contention leaves planners in a quandary. Is there a best outside-in method? Snapp writes, “The matter is rather simple. Some items can be reliably forecasted — and for those, it makes sense to use a forecast-based supply planning method. MRP is one these available in the software. For items that cannot be forecasted, it makes sense to use consumption-based methods. All of this can be set up in supply planning systems.” Cecere adds, “Can we have a discussion like a group of college math majors that realize that most of today’s supply planning implementations are less than ideal.” As noted earlier, I believe cognitive computing has a role to play in outside-in planning. Cognitive computing platforms are adaptable and their advanced analytics provide the most advanced mathematics available. Cecere concludes, “If your boss asks you for three use cases of machine learning and open source technologies in the industry today, connect them to Lokad, PINC and Think IQ. I also like the work in cognitive computing at Aera, Enterra Solutions, and Transvoyant. These are all very different solutions but promising. I strongly believe the future of the supply chain lies at the intersection of outside-in processes, better math, cloud, open source, and machine learning.”
Footnotes
[1] Lora Cecere, “Go Horizontal!” Supply Chain Shaman, 21 June 2016.
[2] Lora Cecere, “Three Cool Technologies,” Supply Chain Shaman, 12 October 2017.
[3] Staff, “Demand Driven Material Requirements Planning (DDMRP),” LinkedIn, 21 July 2015.
[4]Staff, “What is DDMRP?” Demand Driven Institute.
[5] Stefan de Kok, “DDMRP: The Good, the Bad, and the Ugly,” LinkedIn, 9 October 2017. For an updated version of de Kok’s article, see “DDMRP: A Fistful of Considerations,” LinkedIn, 19 October 2017.
[6] Rui Pedro Dantas, “Demand-Driven MRP – Part I: Introduction,” SAP, 20 June 2016.
[7] Shaun Snapp, “How to Understand DDMRP as Yet Another Repackaging of JIT and Lean,” Brightwork Research & Analysis, 19 September 2017.
