The Demand Driven Adaptive Enterprise. Carol Ptak

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we have reached the core problem plaguing most organizations today: the inability to generate and use relevant information to effectively manage variability to then protect and promote flow and drive ROI performance. Without addressing this core problem, there can be no systemic solution for flow.15 Figure 1-10 shows the core problem area of the equation versus the area associated with Plossl’s Law as first stated.

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      Having visibility to the right information is a prerequisite to effectively managing variability and ensuring the flow of the right materials at the right time. With this is mind, Plossl’s Law now must be amended to:

      All benefits will be directly related to the speed of flow of relevant information, materials, and services.

      But this core problem is not confined to individual organizations. As discussed previously, complex systems (organizations) interact collectively with other complex systems to create an even larger complex system. What is happening at this higher level?

      There is a phenomenon involving the stated core problem that dominates most supply chains and complex systems. This phenomenon is called the “bullwhip effect.” The fourteenth edition of the APICS dictionary defines the bullwhip effect as:

      “An extreme change in the supply position upstream in a supply chain generated by a small change in demand downstream in the supply chain. Inventory can quickly move from being backordered to being excess. This is caused by the serial nature of communicating orders up the chain with the inherent transportation delays of moving product down the chain. The bullwhip can be eliminated by synchronizing the supply chain.”17

      A massive amount of research and literature has been devoted to the phenomenon known as the bullwhip effect, starting with Jay Forrester in 1961. However, very little, if any, of that body of knowledge has been devoted specifically to its bidirectional nature. Most of the research has been dedicated to understanding how and why demand signal distortion occurs and how to potentially fix it through better forecasting algorithms, tightly synchronizing the supply chain.

      The bullwhip is really the systematic and bidirectional breakdown of information and materials in a supply chain. Figure 1-11 is a graphical depiction of the bullwhip effect. Distortions to relevant information go up the chain, growing in size and causing wider and wider oscillations both in terms of quantity and timing requirements. The wavy arrow moving from right to left represents that distortion to relevant information in the supply chain. The arrow wave grows in amplitude in order to depict that the farther up the chain you go, the more disconnected the information becomes from the origin of the signal as signal distortion is transferred and amplified at each connection point.

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      Distortions to relevant materials come down the chain as delays and shortages accumulate. The more connections that exist, the more pronounced the delays and shortages. The wavy arrow from left to right depicts that distortion. Lead times expand, shortages are more frequent, expedites are common, and flow breaks down.

      Creating visibility to relevant information and managing the risks to coherence and resiliency is no trivial task, but it is the only path to sustainable organizational success as measured by ROI. The more relevant information our organization has, the more immediate and enduring success it will have—it is really that elementary. Intuitively, people in organizations know that they must find and use relevant information for decision making. Yet many of those people recognize that their information systems are not giving them the visibility that they need. What will it take to get more relevant information throughout the organization? This question is explored in Chapter 2.

       The Prerequisites for Relevant Information

      The search for and use of relevant information to control variability, promote flow, and ultimately drive ROI begins with an understanding of four basic prerequisites. The absence of one or more of these prerequisites will dramatically compromise the integrity of the relevance of the information.

      The concept of relevant range comes from economics and management accounting. Relevant range refers to the time period in which assumptions are valid. Trying to force fit assumptions (and metrics derived from those assumptions) into an inappropriate time range directly results in distortions to relevant information, creates variability, and thus causes a breakdown in the flow of relevant materials and services.

      The assumptions and information that are valid and relevant within different time ranges will differ dramatically and these differing ranges are utilized by different personnel. For example:

      

Forecasts are highly relevant in thinking about the future, they are essentially useless with regard to what needs to be done today.

      

Fixed expenses are variable in the longer range, not in the close-in range (that is why they are called “fixed”).

      

Work order delays are relevant for the current schedule, not for an executive trying to determine the company’s strategy for the next year.

      

A machine breakdown is relevant for a maintenance crew’s activity, not what the company thinks it will sell in Q3.

      There are at least three distinct relevant ranges that we must deal with: operational, tactical, and strategic. The specific time value of each relevant range will differ between companies depending on the circumstances involved in that company. Chapter 3 will describe a way to discretely determine these ranges for every organization.

      While the assumptions and information that are relevant for decision making differ between ranges, there is still an absolute need to reconcile these differences on an ongoing and iterative basis; the signals at all levels must align and reconcile. Strategy must be influenced by defined operational capability and performance as well, considering how the operating model might perform under additional predicted scenarios. Operational capability must be influenced by predicted scenarios

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