Can SCM be Deductive?

Can SCM be Deductive? It is well known in science that nothing can actually be proven. The means by which science makes progress is through the process of falsifiability and, unless a theory is falsifiable (ie. can be shown to be false by observation or experiment), it is not regarded as scientific. As a result, “knowledge” claimed through induction cannot be regarded as scientific. For instance, the fact that the sun has always risen in the morning is not a logical basis upon which to claim it will rise every morning (1). Or, as Karl Popper put it in “The Logic of Scientific Discovery” (1934): “...no matter how many instances of white swans we may have observed, this does not justify the conclusion that all swans are white” On the other hand, mathematics progresses through a process of deductive reasoning in which absolute proof can be achieved through use of ‘already known to be true’ statements, or premises, that mean, if the rules of deductive logic are followed, the conclusion is also necessarily true. A simple example is that if 1 + 1 = 2 then we can also say that 2 - 1 = 1. (2) Why is this difference between induction and deduction important to the “science” that is supply chain management? It is important because it can be deductively proved that the currently most common form of supply chain replenishment process is significantly inferior to the Demand Driven process that is, with the availability of appropriate SaaS systems, now beginning to be adopted in place of ‘forecast push DRP / MRP’. In line with scientific principles we cannot, of course, claim that Demand Driven is more effective than ‘forecast push’ just because its typical performance improvements are (3): 1. Achievement of consistently high planned service levels, with 2. Reductions in average inventory of between 30% to 50%, and 3. Reduction in unplanned over-time, or increase in capacity utilization, enabling cost reductions of c20%, with 4. Lead-time reductions of up to 85% But we can legitimately claim that Demand Driven is better if we are prepared to accept that supply chains are effectively a flow of materials though various value add processes on their way to consumers. If we accept that supply chains are about Flow then we also have to accept that flow in the presence of flow variability at ‘value add’ constraints (eg. machines, warehouses etc) causes queues as described by the, simplified, mathematical Kingman Equation (also known as the VUT equation) (4):

Queue = Variability x capacity Utilisation x average processing Time Average waiting time in

Flow Variability relates to the rate of arrivals at constraints and the constraints’ rates of processing. The average queue time grows proportionately in line with the variability and, as capacity utilization increases, the queue time increases exponentially as demonstrated below:

 

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In a supply chain environment queues develop, despite the existence of adequate average capacity (if there was a real capacity deficit the queue would grow indefinitely), because of arrival spikes temporarily above capacity (eg. big batches) and periodic capacity losses due to non-arrivals and processing rate dips (eg. quality rejections, run rates, unplanned schedule interruptions and changeovers). What VUT tells us simply is that, for a given level of throughput and assuming no change in average processing time, supply chain queues of WIP inventory can only be reduced if Flow variability is reduced or capacity is increased. And when one considers that, in most companies, the Process Cycle Efficiency (Value Add Time / Lead-time %) is rarely above 7% it becomes clear that supply chains are plagued with significant levels of variability. Clearly, if it can be demonstrated that use of Demand Driven results in less variability than 'forecast push', then we can be certain that Demand Driven is the superior process. And if it is found that 'forecast push' actually generates variability we could also say that it is actually perverse and harmful to use it. Of course many companies achieve high service levels despite this variability because they work with significant levels of spare capacity, tolerate long leadtimes and hold high levels of finished goods buffer inventory. But in all such companies there is continuous pressure to reduce inventory levels, reduce leadtimes and reduce costs (eg. through achieving higher levels of capacity utilisation) while maintaining, or increasing, service levels. With the aid of “deductive reasoning” we now know that we can do all of these together by reducing supply chain variability, and many companies have been busy trying to do so for many years in the form of Lean / CI initiatives. When you think of the various Lean tools (SMED/smaller batches, TQM, TPM, Standard Work, Poka Yoke, DFM etc) they can all be recognised as being: “…fundamentally about reducing the cost of buffering variability” (5) Unfortunately much of the effort put into Lean to date has been something of a waste. Although kaizens etc achieve local efficiencies their benefits rarely fully drop through to the bottom line because they are swamped by the cost generating buffers (time, capacity and inventory) created by forecast error induced variability which is propagated and amplified through the supply chain by MRP’s use of dependant demand. Forecast Error Induced Variability, Propagation and Amplification It is well known that all forecasts are incorrect and 80% portfolio accuracy (ie.

 

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20% wrong) is considered ‘world class’. Due to the 20:80 rule, such performance means that most medium and low volume sku’s (usually the majority) actually achieve accuracies that are significantly worse, not least because with lower volumes, variability is higher and so is the level of forecast inaccuracy. Manufacturing or purchasing schedules based upon inaccurate sku forecasts lead to the production of unbalanced stocks with potential service issues, and expediting inevitably follows as Planners respond to exception messages. Service saving production schedule changes cause expensive unplanned machine change overs, schedule congestion and increased lead times with knock on effects upon other schedules up and down the factory routings. In consequence, average lead times increase and become volatile (contrary to the DRP/MRP assumption of fixed lead times, therefore causing a further service risk) and stock becomes both excessive and unbalanced with service issues often continuing to occur. The further up the supply chain, and away from end customer demand one goes, the variability is propagated by MRP’s dependant demand relationships (the assumption that every supply chain activity has a known and fixed lead-time with no buffer) and is amplified by batching and latency (6). To enable supply chains to fully benefit from Lean, and to eliminate the performance eroding impact of forecast error induced variability, we now know that we shouldn't be using 'forecast push MRP/DRP/APS'. How does the Demand Driven process compare? "a segmented multi-echelon supply chain re-order process characterised by multiple deliberately planned, but independent, inventory positions that are autonomously replenished, in an efficient and stable sequence, to a calculated stock target in line with real demand – not the forecast" The key definitions are: Segmented – using the most appropriate replenishment technique for each item / echelon's demand profile (volume and variability – see below) and by never using the inaccurate forecast (or netted forecast) to directly drive replenishment Multiple deliberately planned, but independent, inventory positions – these recognise that all value add process lead-times inevitably suffer natural variation as well as responding to load, especially when capacity utilisation is high. The inventories both absorb and prevent the variation being propagated / amplified up and down the supply chain through MRP’s ‘dependent demand’ Autonomously replenished in line with real demand to a calculated stock target – each inventory position is sized, and maintained, according to average demand over the local replenishment lead-time, plus something for variability, with appropriate adjustments for trend and seasonality. Inventory at every echelon is replaced as it is consumed by down-stream demand and the supply chain thereby autonomously responds to real demand, and its relatively low level of variation, without propagating / amplifying variability and creating the cost generating buffers of capacity, time & inventory Efficient and stable sequence – replenishment has an optimal sequence / cycle which is always followed, respecting MOQs, and never interrupted so Operations can be level loaded to extremely high levels of capacity utilisation without ‘stress’. The appropriate replenishment techniques and when they should be used are as illustrated below:

 

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1. High Volume / Low Variability - ‘level schedule’ or ‘rate based’ 2. Low Volume / Low to High Variability – depending upon the circumstances the appropriate technique might be ATO, MTO, 2-bin or poisson 3. Medium Volume / Medium Variability – replenish against consumption, up to a managed stock target (that accommodates trend and seasonality), in a stable and optimal sequence 4. High Volume / High Variability – use of 1 or 3 but with either ATO response to spikes or their anticipation, and advance stock build using a forecast, for Events. But real Events, that need planning for, or reacting to, are very much less common than might be expected in, say, a highly promotions intensive market place, because the autonomous demand driven response to stock targets is surprisingly resilient. . These item level techniques can be different at different levels within the supply chain, depending upon the local demand volume / variability. And they can just as easily apply across the extended collaborative supply chain as within a single company's (7). For instance, a supplier might respond to a customer via ATO or, assuming it has visibility of downstream stock / demand, replenish against consumption, while using level schedule for upstream material supply. The reason that Demand Driven can be legitimately claimed to be superior to ‘forecast push DRP/MRP’ is that it eliminates the forecast error induced variability and minimizes any that is residual, thereby also minimizing the cost generating supply chain buffers of stock, time and capacity. As Demand Driven can be demonstrated to be genuinely superior to “forecast push” in all supply chain configurations (8), it is reasonable to ask why it is currently so rarely seen. The answer can be attributed to the low data processing capability of the early supply chain IT systems. These early MRP systems were designed to simply support JIT purchase of materials to support a known MPS. They then evolved into MRPII / APS but were too unsophisticated to support simultaneous ‘end to end’ replenishment of multiple independent echelons. And that can still be said of today’s ERPs, APOs etc but the recent emergence of functionality rich and robust Demand Driven ‘Software as a Service’ systems (9) (that share data with, and trigger transactions in ERP via simple flat file upload / downloads) now support this SCM process both across a single company’s various echelons and the extended supply chain. An added benefit of SaaS is that it can be used to quickly and inexpensively simulate and pilot Demand Driven before a revenue expensed wider implementation.

 

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A fast growing number of companies are now quietly piloting and implementing Demand Driven – quietly because they know it will give them a competitive advantage. If you would like to learn more about how YOU can guarantee to significantly improve YOUR company’s supply chain and operations performance, a good place to start is www.demanddriveninstitute.com

References 1. This example goes back to the 18th century Scottish philosopher David Hume of whom Popper said ""I approached the problem of induction through Hume. Hume, I felt, was perfectly right in pointing out that induction cannot be logically justified." (Popper, Conjectures and Refutations, p. 55. 1963) 2. This statement is subject to the caveat that even after the over 300 pages it reputedly took Russell and Whitehead to prove that 1+1=2 in their opus “Principia Mathematica”, Kurt Godel then showed with his ‘incompleteness theorems’ (1931) that even within mathematics there is an inherent limit to what can be known 3. See case studies at https://www.linkedin.com/pulse/some-demand-driven-casestudies-simon-eagle?trk=mp-reader-card 4. The full Kingman or VUT equation, valid for a single server queue, is Average waiting time in queue =

  where T is the mean processing time, p is the utilization (ie. mean processing rate/mean arrival rate), Ca is coefficient of variation for arrivals and Cs is coefficient of variation for service/process times 5. Hopp & Spearman. ‘To pull or not to pull: what is the question’ M&SOM 6.2. Spring 2004 p133-148 6. Latency is when changes in the rate of downstream demand fail to be matched further upstream leading to shortages, in the case of accelerations, of materials and the late and unexpected need for catch up activities which are often then exaggerated leading to the well known bullwhip phenomenon. 7. See https://www.linkedin.com/pulse/demand-driven-collaboration-key-successfulfighting-back-simon-eagle?trk=mp-reader-card 8. Given that all physical supply chains involve a flow of materials, all such supply chains can be managed with Demand Driven principles using active selection and management of the three buffers - time, capacity and inventory.   9. See  Gartner press release 25/11/2014 ‘Gartner Survey Reveals that SaaS Deployments are Now Mission Critical’  http://www.gartner.com/newsroom/id/2923217

Simon Eagle. 2015.

 

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