Demand Driven: Agility with Stability!

 

Demand Driven: Agility with Stability! In a supply chain context the expression ‘Agility with Stability’ might appear to be something of an oxymoron. After all, Agility is usually associated with a supply chain’s ability to respond quickly to changing demand patterns through amending its schedules at short notice. It is very different to the common understanding of Stability as being visibility of future firm schedules (often two weeks in CPG but sometimes four or longer in LifeScience) that can be planned for with a high degree of reliability. How then, can ‘Agility’ be achieved with ‘Stability’? To appreciate how, the words’ definitions need to be clarified in terms that are accurate (but often mis-understood) in a supply chain context: Stability - is the various supply chain value add activities working to their own efficient and predictable sequence with minimal unplanned changes and interruptions. The benefits of stability are that costs can be minimized through working to a sequence that maximizes changeover efficiencies and can be relied upon so that, for instance, people, materials, machines can be prepared as necessary (1). To achieve such stability there needs to be a minimum of interruptions, be they due to lack of components, quality issues, machine breakdowns or changes to the schedule to prevent service threats. On the other hand: Agility - is the ability of a supply chain to be autonomously flexible and responsive to real demand, and its variations, with buffers that are of the right size and in the form that best serves both the company and its customers - and is 'designed in' The essence of ‘Agility’ is in the use of the terms: ‘autonomously flexible’ – responding to changing demands immediately and without prior thought and preparation ‘buffers that are of the right size and in the right form’ – buffers are always associated with variability unless the supply chain is so flexible that it is able meet demand exactly while operating at 100% utilization without customers either being kept waiting or supplied ex-stock. The aggregate of these buffers (time, capacity and stock) should be minimised in terms of their cost generation and be in the form that ‘delights’ the customer.

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'designed in' - it is the design of how the supply chain is planned, operates and improved upon that delivers high service with ever decreasing levels of cost and inventory (ie. CI). In most ‘make to stock’ supply chains, efforts to achieve stability are usually through the holding of ‘safety stock’ and use of time fences that freeze schedules over the internal manufacturing lead-time (followed by a limited degree of mix flexibility out to the cumulative lead-time). Despite significant time and effort being put into achieving high forecast accuracy, however, pressure to break these time-fences is often immense. When one considers that world class portfolio mix forecast accuracy is just 80% (ie. 20% wrong and hiding the fact that the majority of sku’s – those with medium to small volumes and medium to high variability – will be achieving errors of 40%+) (2) it is not surprising that finished goods inventories tend to be severely unbalanced and prone to service threats, which generate these schedule crashing ‘hot lists’ via ‘exception messages’ (3). In addition the manufacturing lead-time lengths that the frozen time fence is supposed to protect, are always significantly longer than strictly necessary (ie. the actual manufacturing 'value add' time) due to the need to add significant queue or wait time buffer into the planning parameter. Already it can be seen that the desire for Stability inevitably causes the supply chain to generate buffers due to the variability induced by the inaccurate forecasts. Given this, how can meeting customers’ variable demands be met without creating excessive cost generating buffers (ie. Agility)? One answer, of course, is to implement Lean that, through its tool kit (eg. TQM, TPM, SMED/small batches, Standard Work, DFM etc) continuously drives supply chain reliability, flexibility and responsiveness (4). The other answer is via implementation of the Demand Driven Supply Chain which is, in effect, simply enterprise(s) wide Pull, and can be defined as: "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

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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 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:

1. 2.

High Volume / Low Variability - ‘level schedule’ or ‘rate based’ (1, 5) Low Volume / Low to High Variability – depending upon the circumstances the appropriate technique might be ATO, MTO, 2-bin or poisson

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  3. 4.

Medium Volume / Medium Variability – replenish against consumption, up to a managed stock target (that accommodates trend and seasonality), in a stable and optimal sequence 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 (6). 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 evidence that Demand Driven delivers ‘Agility with Stability’ and significantly minimises the cost generating buffers can be seen from its results. Typical supply chain performance improvements (7) are: 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% The reason that Demand Driven Supply is so effective is that it minimizes queues, and enables the supply chain to Flow by eliminating the forecast error induced variability and minimizing any that is residual – thereby also minimizing the cost generating buffers of stock, time and capacity (8). Demand Driven Supply is a major change in the way most companies manage their supply chain but technical implementation is relatively simple. Robust, functionality rich and globally tested ‘Software as a Service’ that supports the critical processes of positioning, sizing and maintaining the independent inventory positions is now available; they also support the ‘work to’ list generation and simply upload / download data via FTP files on a daily basis with legacy ERP transaction systems. In addition, via system supported forecast driven simulations through the relevant replenishment rules, the Planner can understand future inventory and capacity requirements which, of course, form the basis of S&OP decision making. The SaaS model also facilitates quick simulations of your supply chain’s

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historical performance versus that which Demand Driven Supply would have delivered, as well as low cost, fast pilots prior to a revenue expensed implementation and roll out. A rapidly growing number of CPG and LifeScience companies are quietly piloting and implementing Demand Driven Supply – quietly because they know that ‘Agility with Stability’ will give them a significant competitive advantage! If you would like to learn more about Demand Driven Supply, a great place to start is www.demanddriveninstitute.com Can you afford not to? (9) References 1. Productivity increases of up to a third have been recorded as a consequence of regular and predictable schedules being introduced into Operations through “economies of repetition”. See http://www.repetitiveflexiblesupply.com/pdf/the_magic_of_levelled_scheduling.pdf 2. See https://www.linkedin.com/pulse/forget-forecast-accuracy-kpi-simon-eagle?trk=mpreader-card 3. Exception messages are generated whenever the safety stock is in danger of being used so forecast error tends to be ‘bounced’ up the supply chain from it and through the dependent demand network. Many companies protect their Operations with time fences that are effectively a lead-time extension which represent more stock buffer (and exacerbate the situation by increasing load), but will still eventually suffer service issues unless some schedules are amended. See also https://www.linkedin.com/pulse/20140915142040-2206374-would-you-meet-yourplanned-service-levels-with-100-forecast-accuracy?trk=mp-reader-card 4. Hopp and Spearman (Factory Physics 1995) describe Lean as '...fundamentally about minimising the cost of buffering variability' See also https://www.linkedin.com/pulse/20140905153717-2206374-have-our-efforts-toimplement-lean-all-been-a-waste?trk=mp-reader-card and 5. It is important that level schedules are not used to unnecessarily drive up batch sizes as these generate variability reflected in higher stock levels, service issues and higher costs. See https://www.linkedin.com/pulse/20140923183455-2206374-smaller-batches-reducecosts-yes-really?trk=mp-reader-card 6. See https://www.linkedin.com/pulse/demand-driven-collaboration-key-successful-fightingback-simon-eagle?trk=mp-reader-card 7. See case studies at: https://www.linkedin.com/pulse/some-demand-driven-case-studies-simon-eagle?trk=mpreader-card 8. Flow variability at constraints generates queues according to the Kingman/VUT equation: Q = Variability x Utilization x Time. It quantifies how they increase directly in line with V and exponentially with U, especially at high levels of U. See Factory Physics (Hopp & Spearman) 1995, and any text on Queueing Theory. Also see https://www.linkedin.com/pulse/can-scm-deductive-simon-eagle?trk=mp-reader-card 9. See https://www.linkedin.com/pulse/20141201195743-2206374-the- paradigm-shiftquietly-occurring-in-supply-chain-management?trk=mp-reader-card and https://www.linkedin.com/pulse/20141122203834-2206374-what-the-ceo-really-needsto-know-about-their-supply-chain?trk=mp-reader-card

Simon Eagle 2015  

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