Demand Driven Material Requirements Planning (DDMRP), Version 2. Carol Ptak
Souder’s law states that “repetition does not establish validity.” Simply continuing to do something that has always been done does not define whether it is or ever has been the appropriate thing to do. Worse yet, the longer the repetition, the more invalid or inappropriate the rule may be.
There are three areas that point to major issues with the rules and tools of conventional planning featuring MRP.
Return on Asset Performance Degradation
As described above, the United States led the adoption of manufacturing information systems starting with MRP in the 1960s. These systems are expensive to purchase, to implement, and to maintain. The value of these formal planning systems has always been based on the ability to better leverage the assets of a business. Did the widespread adoption of MRP and subsequent information systems enable the U.S. economy to better manage assets?
In late 2013 Deloitte University Press released a report by John Hagel III, John Seely Brown, Tamara Samoylova, and Michael Lui that is quite eye-opening when considered against the progression and adoption rates of information systems.2 Figure 1-1 is a chart from the report that depicts the return on asset performance of the United States economy since 1965.
There is a steady decrease in return on assets for the U.S. economy from 1965 to 2012. Furthermore, during this time period the same report shows that labor productivity (as measured by Tornqvist aggregation) more than doubled! What is most interesting about this graphic in relation to information systems is that by 1965 we had the modern acronym MRP, but massive proliferation of information systems did not occur until after 1975 and, in particular, after 1980 with MRPII.
Obviously there are many factors at play with this decrease in return on assets, but this report would certainly lead one to realize that the impact of the widespread adoption of MRP, MRP II, and ERP systems (at least in the United States) has not significantly helped companies manage themselves to better returns on asset performance. Indeed, when this decline is taken in combination with the increase in labor productivity, it actually suggests that companies are accelerating their mistakes.
But this is just one point of data, a high-level view with many unrelated factors contributing to these effects. What additional evidence indicts the efficacy of the conventional planning approach?
FIGURE 1-1 Return on asset peformance for the U.S. economy
In addition to examining the performance of an entire economy over a period of time, next examine the day-to-day actions of the people charged with making decisions about how to utilize assets. One hallmark of supply chains is the presence of supply orders. Supply orders are the purchase orders, stock transfer orders, and manufacturing orders that dictate the flow and activities of any supply chain.
The very purpose of a planning system is to ultimately determine the timing, quantity, and collective synchronization of the supply orders up, down, and across the levels of the network. Inside most manufacturers there are tiers within the planning system where stock transfer orders could prompt manufacturing orders that in turn would prompt purchase orders. Additionally, within most supply chains there are tiers of different planning systems at each organization linked together by these orders and communicating through these supply order signals. For example, purchase orders from a customer can prompt stock transfers or manufacturing orders at suppliers.
Perhaps the biggest indictment of just how inappropriate modern planning rules and tools are can be observed in how frequently people choose to work around them. The typical workaround involves the use of spreadsheets. Data are extracted out of the planning system and put into a spreadsheet. The data are then organized and manipulated within the spreadsheet until a personal comfort level is established. Recommendations and orders are then put back into the planning system, essentially overriding many of the original recommendations.
Consider polling on this subject by the Demand Driven Institute from 2011 to 2014. With over 500 companies responding, 95 percent claim to be augmenting their planning systems with spreadsheets. Nearly 70 percent claim these spreadsheets are used to a heavy or moderate degree. The results of this polling are consistent with other surveys by analyst firms such as Aberdeen Group. This reliance on spreadsheets has often been referred to as “Excel hell.” Validation for this proliferation can be easily provided by simply asking the members of a planning and purchasing team what would happen to their ability to do their job if their access to spreadsheets were taken away.
But why have planners and buyers become so reliant on spreadsheets? Because they know that if they stayed completely within the rules of the formal planning system, approving all recommendations, it would be very career limiting. Tomorrow they would undo or reverse half the things they did today because MRP is constantly and dramatically changing the picture. This phenomenon, known as “nervousness,” is explained in Chapter 3.
So what do they do instead? They work around the system. They each have their own ways of working with tools that they have crafted and honed through their years of experience. These ways of working and tools are highly individualized with extremely limited ability to be utilized by anyone but the originator. This is a different, informal, highly variable, and highly customized set of rules.
Worse yet, there is no oversight or auditing of these side “systems.” There is no “vice president of spreadsheets” in any company the authors have ever worked in or visited. Everyone simply assumes that the people who created these spreadsheets built and maintain them properly. Consider an article in the Wall Street Journal’s “Market Watch” in 2013:
Close to 90% of spreadsheet documents contain errors, a 2008 analysis of multiple studies suggests. “Spreadsheets, even after careful development, contain errors in 1% or more of all formula cells,” writes Ray Panko, a professor of IT management at the University of Hawaii and an authority on bad spreadsheet practices. “In large spreadsheets with thousands of formulas, there will be dozens of undetected errors.” (Jeremy Olshan, April 20, 2013)
As an example of how disastrous spreadsheet errors can be, consider the role a spreadsheet error played in a $6 billion disaster for JP Morgan in 2012. The following is an excerpt from the zerohedge.com article “How a Rookie Excel Error Led JPMorgan to Misreport Its VaR for Years”3:
Just under a year ago, when JPMorgan’s London Whale trading fiasco was exposed as much more than just the proverbial “tempest in a teapot,” Morgan watchers were left scratching their heads over another very curious development: the dramatic surge in the company’s reported VaR, which as we showed last June nearly doubled, rising by some 93% year over year, a glaring contrast to what the other banks were reporting to be doing.
Specifically, we said that “in the 10-Q filing, the bank reported a VaR of $170 million for the three months ending March 31, 2012. This compared to a tiny $88 million for the previous year.” JPM, which was desperate to cover up this modelling snafu, kept mum and shed as little light on the issue as possible. In its own words from the Q1 2012 10-Q filing: “the increase in average VaR was primarily driven by an increase in CIO VaR and a decrease in diversification benefit across the Firm.” And furthermore: “CIO