With the ever growing threat, and reality, of low cost competitors, as well as the need to meet Emerging Market demand, not to mention the never abating requirement to improve the corporate bottom line, the pressure upon Supply Chain, Procurement and Operations leaders to sustainably reduce costs is as great as ever.
Low cost sourcing, outsourcing of non-core activities, consolidation into focussed factories, huge investment in ‘end to end’ planning systems have, and are all being, tried with various levels of success. A key issue is that as supply chains have been getting longer through EM penetration and low cost sourcing, mature market service levels are being negatively impacted, inventory levels are increasing and supply chain alignment and efficiency is getting ever harder – not least because of sku proliferation, demand becoming more volatile and forecast accuracy that much harder to achieve.
In these trying circumstances, is there another credible route, as yet largely untried, which could significantly improve cost, service and inventory performance across Procurement, Operations and Distribution?
Remarkably there is, it’s been around for a very long time and it doesn’t rely upon a ‘black box’ solution or a new and untested “here today, gone tomorrow” management fad. It does, however, need some counter-intuitive thinking to fully understand, some minor spend upon ‘cloud’ software support and a huge focus upon serious change management activities to get it installed and successfully implemented.
Why you shouldn’t use a forecast to drive replenishment
The ‘it’ is the replacement of the long standing and ubiquitous forecast push DRP/MRP replenishment processes with Demand Driven Replenishment (& Planning). The rationale for this change is that ‘forecast push’ simply blows forecast error into your operations which means that you are continuously moving the wrong stock to the wrong places and making/buying the wrong product mix. These activities, usually defended by excessive lead times and time fences, inevitably lead to high and unbalanced stock levels with service issues and, if the schedules are ‘crashed’, as they inevitably are due to service threats, the result is use of unplanned change overs and capacity which generates costs. Schedule crashing, as the queuing theory behind Hopp & Pearson’s “Factory Physics” demonstrates, also has knock on effects within the factory and right across the supply chain causing changeable and longer lead times which generate future service issues (MRP requires stable lead times) and inventory growth (inventory is directly proportional to lead time) – and these negative consequences increase exponentially if you’re trying to work at high levels of capacity utilisation. In addition, across the end to end supply chain (and the longer it is the worse it gets), the forecast error and demand variation is amplified through batching and the unfortunately binary and over compensatory impact of attempts by hard working Planners to ameliorate the problems……………….this, of course, is called ‘bullwhip’ and it hugely exaggerates the negative cost, service and inventory effects of driving replenishment with an inaccurate forecast.
‘Demand Driven Planning & Replenishment’ (DDPR)
How does DDPR avoid these problems? Well for a start, although it uses the forecast for product family capacity, material and financial planning, it always uses a 100% accurate replenishment execution trigger which is that activated by real demand, not the forecast. It is therefore not reliant upon sku level time phased forecast accuracy for effectiveness (in a DDPR environment, such forecast accuracy has no relevance) and it also prevents the inevitable forecast error being amplified up the supply chain. Eradicating the impact of such demand amplification or ‘bullwhip’ has been demonstrated by Metters (1) to improve product profitability by up to 30% while right sizing inventory (always reducing) and meeting desired service levels. Similar improvements have also been reported by those companies which have completely dispensed with ‘forecast push’ execution and adopted DDPR instead.
Broadly, there are three demand driven replenishment techniques available which can be used for different items at different echelons within the supply chain. The following explains how they work and when they are appropriate. The important point to remember is that to achieve stability across the supply chain echelons, the parameters and buffers used in the design of these ‘de-coupled’ replenishment techniques, must be aligned so that they support each other in meeting end customer demand.
- Consumption-based pull or Rhythm – in which inventory buffers are located, as appropriate, in the supply chain to decouple processes and minimize lead times. Supply activities (eg. inventory movements, production and purchase orders) are scheduled according to an efficient time phased cycle and the quantities triggered, rounded as necessary, replace what has been taken from the location immediately downstream. This affords protection against demand uncertainty and minimises amplification by only building to a replenishment signal, not to a forecast. This technique can be used even if demand has trend or is seasonal so long as the replenishment parameters reflect future demand patterns appropriately.
- Rate-based or Level schedule – where demand is high and relatively stable (as it often is for mature products and for upstream items before sku specific customisation takes place), supply can be levelled at a suitable fixed rate, subject to periodic review . In Lean language this is called ‘heijunka’ or ‘mixed model scheduling’ and it works best when it involves high frequency batch production. Ironically, products which are suitable for level schedule are also those which are easiest to forecast – but why use a forecast driven replenishment technique when a far more effective and simple alternative is available? Level schedule, with regular review, is also very useful for product launches as it generates the stability that operations need to enable them to focus upon improving the, possibly, new manufacturing processes.
- Time buffered – in general, the more volatile is demand the more important it is that replenishment is not driven with a forecast due to the inevitably high levels of consequent forecast inaccuracy and ‘bullwhip’. When volatility is very significant, and ex-stock service is not an option, time buffered techniques can be selected. Depending on the required service strategy, the technique chosen might be ‘assemble to order’ (ATO) in association with postponement, or ‘make to order’ (MTO). These techniques can be used when customer demand is genuinely volatile (eg. response to tenders and price promotions) as well as forming part of an ‘abnormal demand’ management process. Postponement, if supported by appropriate ‘design for manufacture’ and asset configuration can deliver stable demand on capital intensive or bottleneck production assets, which is highly cost effective and yet very responsive to demand volatility – ‘Agility with Stability’. Other low volume/highly variable products should, of course, simply be infrequently slotted around the schedules as required and with maximum notice.
These techniques don’t just apply to a company’s internal supply chain of course, they can also be the basis for collaboration with suppliers and customers.…… clearly the benefits from DDPR increase in line with the share of the supply and demand network that is managed using its principles.
DDPR allows the supply chain to autonomously respond to genuine demand variations and enables Planners to concentrate properly upon Supply Chain Conditioning. Supply Chain Conditioning is how Planners ensure that end to end capacities and material availabilities are accurately planned (S&OP) and the inventory parameters and buffers are calculated and aligned . Despite the criticality of effective parameter management, however, the replenishment buffers and triggers should not be changed every month (if they were you may as well use the forecast!); they should certainly be reviewed regularly but only c5% will actually need amending at any one time.
There is a significant additional benefit to S&OP from implementing DDPR. Reductions in operational variability actually makes the S&OP process more accurate as there is far less likelihood of the operational plans being confounded by variability induced lost capacity. In addition, companies that use DDPR also experience vastly improved S&OP collaboration between the Commercial, Supply Chain, Operations and Procurement functions. Not only is this due to the generally better relations that might be expected from a more successful process, but also because there is less unreasonable ‘blaming’ of the forecast for service misses (and the bad feeling this inevitably creates) and less short term schedule changes which reduces stress, pressure and the need to ‘achieve the impossible!’
Why isn’t DDPR more common?
If DDPR is so effective, it’s reasonable to ask why it isn’t practiced more widely? ‘Bullwhip’ was first written about by Forrester and Burbidge in 1961 which was the same year Kingman mathematically formalised the relationship between average queue times with process time, variability and capacity utilisation. ‘Pull’ has been part of the Lean toolbox since at least the early 1980s and, in fact, is increasingly common within the factory walls and, in some industries, with suppliers (eg. grocery multiples and automotive). ‘Pull’ is rarely seen, however, all the way across companies’ distribution channels and linked into their factories through to suppliers. This might explain why many companies often find that all their Lean Factory and Supplier Engineering efforts haven’t translated into the significant performance benefits they expected. By now the reader will know why.
The reasons for the relative scarcity of “end to end” DDPR can only be speculative but might include the fact that it is counter intuitive, neither widely or fully understood and difficult to implement successfully without appropriate software support.
Clearly Factory Managers, and Procurement have an interest in how their company’s supply chain is managed if it has a significant impact upon their performance and their ability to contribute to the bottom line, let alone if it could prevent material shortages and service issues. In some companies the CEO might also be interested?
Fortunately, the outlook for DDPR is positive with the impact of a Lean education upon the new generation of supply chain leaders and the emergence of specialist “software as a service” (SaaS) DDPR vendors such as Orchestr8(UK), Ultriva and Demand Driven Technologies (both US). Recent adopters of DDPR tend to be ‘blue chips’ working with very powerful customers, volatile demand and for whom cost effective service is an absolute key to success and survival – CPG and ‘own brand’ manufacturers (including consumer LifeScience).
DDPR implementation guidelines
It is important not to assume that DDPR is an easy fix. Successful implementation requires a clear understanding of its rationale and benefits (a successful pilot is often an important element here), adequately robust, user friendly and functionality rich planning systems (don’t use MS Excel for the critical Supply Chain Conditioning, it doesn’t have the credibility with users, let alone the functionality; you’ll be taking a service risk and the full potential benefits won’t come through), senior cross functional leadership support and a capable and interested supply chain team that includes Order Management through to factory Schedulers and Supply Management.
Successful implementation of DDPR can sometimes cause issues as production often has to be significantly ramped down for a time, due to previously excessive inventory levels, so misleading but adverse standard cost variances may have to be planned for.
Significant and noticeable benefits from DDPR will be that warehouses, factories and suppliers will all find that their daily ‘work to’ lists become far more predictable and stable. In consequence, potential throughput goes up, unplanned overtime and adverse cost variances diminish and more quality time can be spent on CI initiatives that minimise supply variability and cost instead of chasing the latest backorder. Lead times will also come down and, due to reduced levels of Operator stress and error, quality will improve and reductions in rates of absenteeism have also been reported. Planners will be able to do a far more value add role (ie. S&OP and SC Conditioning etc) instead of continuous expediting and Materials Management should be able to share their now relatively stable requirements with suppliers and allow them to reliably schedule their operations accordingly and, perhaps, even manage their own replenishment schedules if working with a collaboration platform. Perhaps, very importantly, suppliers will be able to operate far more efficiently and some of the consequent cost savings should accrue to their customers!
1 – Metters (1997) “Quantifying the bullwhip effect in supply chains”. Journal of Operations Management 15(2) p89-100