Why DDMRP Is A Necessary Condition For Industry 4.0 To Deliver On The Promise

 

Introduction

Industry 4.0 is going to change the way the manufacturing industry will design, manufacture and sell their products. The digitalization of the industry will generate considerable improvements in productivity and speed to market for both new and existing product offerings. However, will this revolution in industrial integration really deliver to its full potential and convert these productivity gains into real top and bottom line returns?

So far very little attention has been paid to the one vital element necessary to ensure that the supply chain is focused on delivering

  • the right products
  • to the right places
  • at the right time

This vital element is the use of the Demand Driven Operating Model and the related planning methodology Demand Driven MRP (DDMRP). This is currently the only approach that allows to effectively synchronize supply and demand across complex and volatile supply networks. We see a shift from the traditional Forecast-MPS-MRP driven world to a Demand-Driven-Pull world as a necessary condition to capitalize on the productivity improvements that will be generated by the digitalization of the industry.

Industry 4.0 background

The fourth industrial revolution has started and in all likelihood will reshape the manufacturing industry in the next twenty years. This revolution is generally referred as industry 4.0 in most of Europe, while In the US it is sometimes referred to as The Internet of Things or The Internet of Everything (5). What exactly is Industry 4.0 is still not yet fully defined and a consensus on its definition and on all the elements that it will encompass is still being shaped.

However, there does seem to be common agreement that some of its ingredients are the widespread application of:

  • information and communication technology (ICT)
  • cyber-physical systems
  • network communication
  • big data and cloud computing
  • human-system interfaces like augmented reality (1-5).

The vision for the future is that of factories working mostly autonomously, where all relevant data is available in real time, where machines – mostly robots – communicate to one another and the products themselves communicate their status to the machines. Sensors will monitor the entire manufacturing process. Equipment will autonomously determine the optimal maintenance intervals. System integration will allow the customers to direct both the design and the manufacturing of their products. Thanks to 3D printing, additive manufacturing and highly flexible equipment, it will be practically possible to run lot sizes of one.

Substantial benefits are expected as a result: better maintenance and data integration will allow an increase equipment utilization; shorter production cycle times will allow a faster response to demand signals. Transactions will be performed autonomously without any human interaction. The expectation is a consistent gain in productivity, profitability and an overall Returns On Asset (ROA) for industry of 15% and beyond (1). This return would then allow re-investment in the manufacturing sector, thus reversing the current trend of erosion in manufacturing assets observed in most European countries.

When we take a look at the literature on industry 4.0 we find a strong emphasis on productivity increase, increasing of equipment utilization and decreasing unit cost. The assumption is that these benefits will translate into an increase in profitability and ROA.

It seems to us, that most authors and commentators have ignored a key element necessary for these benefits to become real. Current Industry 4.0 literature does not investigate in any depth the supply chain management element and the generation and propagation of the demand signal to the different players in the supply network. It is assumed that the machines will get the correct demand signal – probably as a natural result of the digitalization itself – without questioning the validity of this assumption.

The reality is that today most companies are suffering due to the lack of synchronization of supply with demand. Despite the use of ever more sophisticated planning tools and analytics this process is very badly managed and it is the source of much waste. Many industries are in a constant fire-fight against the bullwhip effect and ever changing customer requirements. This is mostly a result of the way we plan our supply chains using MRP techniques and sales forecast as the source of demand data. These traditional planning techniques break down in global and complex supply chain networks subjected to volatile demand and extended end-to-end lead times.

According to some authors (3), as a result of the 4th industrial revolution we should expect an additional increase in supply chain network complexity, more product customization and shorter customer tolerance time. “The complexity of production and supplier networks will grow enormously” (Sigfried Dais, Partner of the Robert Bosch Industrietreuhand KG)(10). If all this is true, then this does note bode well for supply chain management and will only exacerbate the current disruption to the smooth running of the supply chain driven by an inherently inaccurate demand signal.

The fallacy of asset utilization

One of the underlying assumptions of the Industry 4.0 transformation is that a decrease of unit cost will translate into a positive ROA. We believe that this assumption is fallacious. For once, this has not happened in the last 45 years despite the use of better and better technology and exponential increase in computer power (6).

The reason for that is the following: you can speed up your manufacturing and reduce your unit cost as much as you want, but if your industrial robots are not getting the correct demand signal then it will be mostly wasted on making the wrong products at the wrong time and the productivity gain will not translate into a positive ROA. This aspect seems to have gone forgotten in the current Industry 4.0 discussion. While from a manufacturing technology perspective we are steering well into the 21st century, from a supply chain perspective we are still stuck in the 60’s. In our analysis of Industry 4.0 articles we did not find any indication that this is about to change. McKinsey (3) predicts that the application of advanced analytics should increase forecast accuracy to 85% and they include this projection as one of the Industry 4.0 benefits. While we believe that there is certainly room to improve forecast accuracy to a degree, we need to recognize that good forecasting will tend to become harder due to an increase in volatility, product customization and shorter product life cycles.

Most importantly, the mere fact that analysts are still thinking that focusing on forecast accuracy improvements will solve the problem, clearly indicates that we are still collectively trapped in the past when thinking about supply chains. We need to fundamentally change our thinking of what supply chains are and how they work. We need to accept that a forecast, however meticulously generated, will always be wrong to some extent and using this to drive a highly autonomous supply chain will never deliver the full potential of the Industry 4.0 world.

Rethinking the supply chain

All of our traditional supply chain thinking and our established management rules are based on the wrong assumption that supply chains are linear systems. According to this view you can break your supply chains in subsystems, then optimize the performance of each subsystem and you would get as a result an optimized supply chain.

Based on this linear view you would expect supply chains to be stable and predictable. The outputs should be a linear function of the inputs: you can optimize them.

Unfortunately, this is not an accurate description of the real world.

A much better description of supply chains is provided by the new science of Complex Adaptive Systems (CAS). According to this new science, supply chains are complex, non-linear and self-organizing. They must be described not by the sum of their parts, but rather by the interactions between the parts.

  • Complex means that a higher order emerges as a result of the interactions between the subsystems.
  • They are dynamic: they don’t stay in any stable state very long.
  • They behave in a non-linear fashion: a small change in initial conditions may results in major shifts elsewhere. The bullwhip effect, as seen in almost all supply chains, is a typical example of this non-linear phenomenon.
  • In addition they are adaptive and self-organizing, which means that they evolve and learn as a result of agents interacting within the system.

A more in depth discussion of supply chains as CAS can be found in Smith and Smith (7).

The first necessary step to move forward is to understand the reality of supply chains as CAS and accept the consequences implied by this fact. We need to start applying systemic thinking in our approach to design, plan and execute in our supply networks. The ability of managers at all levels to think systemically is a key factor for success in this environment. Luckily, from here onward things will get much easier: these non-linear systems can be effectively controlled by acting on a few lever points. Once the right environment has been created and the lever points have been identified with the application of what we call the Demand Driven Operating Model it then gets quite simple to manage and to keep the supply chain under control.

Demand Driven Planning

The application of the Demand Driven Operating Model and planning methodologies, like Demand Driven MRP (DDMRP), create the conditions to manage an end-to-end supply chain according to the insights provided by CAS theory(7,8).

  • Under this methodology the supply chain is decoupled into independent planning horizons.
  • Supply orders are generated by customer sales orders or internal pull signals from decoupling points. This ensures that real customer demand signals pass along the end-to-end supply chain.
  • Forecast plays no part in the generation of these signals and therefore, forecast error do not create nervousness and erode performance.
  • Stock buffers acting as decoupling points prevent the remaining variability from being transferred between the planning horizons and effectively stop the propagation of the bullwhip effect.
  • This creates a very stable demand signal and protects both supplier and customer from variability (demand variability and supply continuity variability).
  • The buffers contain and pass all the relevant Information necessary to steer the supply chain.
  • A few critical control points are used to monitor and ensure that the system is performing as planned.
  • The result is an extremely agile, but stable and resilient supply chain.

Companies that have deployed demand driven approaches like DDMRP have reported dramatic improvements in operational performance:

  • very high service levels
  • shorter lead times
  • increased responsiveness
  • while decreasing overall inventories – sometimes even by 50% and more
  • and almost completely eliminating expediting expenses.

For instance, one of our clients recently reported to us that from the moment they have changed their distribution planning using DDMRP they completely eliminated shipments between distribution centers. This used to be a major supply chain expense before, due to inventory being in the wrong place. During the same period inventories went down by 20% and service levels improved. Meanwhile, order stability achieved perfection: not a single supply order has been changed once placed to the sourcing plant.

The increased stability generated by the correct demand signal being propagated impacts positively equipment utilization: manufacturing plans and schedules are stable and do not change. All these improvements combined translate directly in

  • less inventory
  • shorter cash cycles
  • lower total supply chain cost
  • and thus higher ROA.

When this is combined with the automation and productivity improvements promised by Industry 4.0 concepts, then a truly transformational change in top and bottom line business performance is within reach.

On the other side, we believe that companies that will move to Industry 4.0 without transforming their supply chain into a demand driven operating model risk to become very efficient at manufacturing the wrong thing. It doesn’t matter how cost efficient you are producing something: if there is no customer demand for it then you will end up with the wrong inventory in the wrong place and you will not generate returns for the company.

Fully mature technology solutions for DDMRP already exist and some have several characteristics typical of Industry 4.0(9). One of these tools for instance has existed as software as a service solution since its inception in 2002. This software plugs on any ERP system through standardized interfaces and takes over the planning part. It can be deployed fast and can easily be configured to link different supply chain players independently of their backbone ERP system, thus providing an immediate opportunity to enhance customer supplier integration and collaboration.

Conclusions

We believe that and important element of the Industry 4.0 landscape has not been considered with due weight until this moment. This element is the generation and propagation of the proper demand signal through the supply chain and it represents the key to Industry 4.0 becoming the “game changer”. In order to include this element we need to evolve our understanding of supply chains and accept that a continuing reliance on forecast is not the way of the future. The Demand Driven Operating Model / DDMRP is the perfect methodology to include this element in the Industry 4.0 equation. It seems that so far no one so far seems to have recognized this gap in Industry 4.0 thinking. We believe that failing to recognize the importance of a clear and accurate demand signal linked directly to true customer demand, and taking action now to rectify this, will nullify a major portion of the potential benefits that Industry 4.0 will have on the ROA performance of many businesses.

Sources

Reports:

  1. Roland Berger. Industry 4.0: The new industrial revolution. How Europe will succeed (March 2014)
  2. Strategy& (PWC). Industry 4.0: Opportunities and challenges of the industrial internet (2014)
  3. Industry 4.0: How to navigate digitization of the manufacturing sector (2015)
  4. Industry 4.0: Digitalisation for productivity and growth (September 2015)
  5. Industry 4.0 Challenges and solutions for the digital transformation and use of exponential technologies
  6. Deloitte University Press. Success or struggle: ROA as a true measure of business performance (2013)

Books:

  1. Debra Smith, Chad Smith. Demand Driven Performance: using smart metrics (2013)
  2. Carol Ptak, Chad Smith. Orlicky’s Material Requirement Planning, 3rd Edition (2011)

Links:

  1. http://demanddriveninstitute.com/software_compliance.html
  2. https://en.wikipedia.org/wiki/Industry_4.0

 

On Industry 4.0, Complex Adaptive Systems and DDMRP

This interview will be published in the presentation booklet distributed to the participant to the Logistik Technologie- und Innovationspark (TIP) fair in Zurich on April 6th and 7th, 2016. Please visit http://www.vnl.ch/en-us/events/logistik-tip.aspx

 

Where do you see the mid and long term supply chain challenges in for Switzerland?

Switzerland must become price competitive without disrupting quality and service. The major challenge is to change our thinking of how supply chains work: companies want to reduce their unit cost and they see Industry 4.0 as a way to achieve it. But: reducing unit cost is not the same as improving your company returns: in fact, often exactly the opposite happens. Without understanding how supply chains work you may well end up digging your own grave with Industry 4.0.

 

Switzerland uses innovation to differentiate from its competitors. How do you see the situation for an innovative logistic?

I believe that from a technology standpoint we will be leading. Switzerland has a tradition of leading technologically. But here again, I don’t see the major challenge as a technological one, but in our mental ability to understand and accept some fundamental changes in how to think about supply chains. Otherwise we will end up at becoming very efficient at doing the wrong thing: this would be a catastrophy.

 

Where do you see your contribution in addressing these future challenges?

We help companies to get their supply chain under control. To do this you need first to understand that supply chains are not linear system but Complex Adaptive Systems (CAS). CAS behave with different rules that are sometimes counterintuitive and the opposite of generally accepted rules used todays. Unfortunately some of todays rules, which are more than 60 years old, are firmly embedded in the algorithms at the core of our ERP systems. These rules are outdated and are not fit for today’s highly volatile and complex supply chains. Then we help companies designing and implementing a demand driven operating model and use Demand Driven MRP (DDMRP) to plan and execute their supply chain. When you do that you truly get amazing results. Generally inventories go down by 30-50%, service levels are maximised and most expediting activities, which account for a major portion of supply chain cost, disappear. We have seen companies where their returns on capital employed, and hence their profitability, has increased by several times.

 

Could you describe the problem that you are helping your customers to overcome?

You procure raw materials (with long lead time) and you create your manufacturing plan based on forecast figures provided by your sales department. You have optimised your production schedule to achieve high utilization and lowest cost. Unfortunately the forecast turns out to be wrong. Priorities have changed and what you produced doesn’t seem to be needed anymore. Worst still, management is asking you to disrupt the schedule on your bottleneck equipment to fit the new priorities. You need to call your suppliers and ask them to expedite and increase – or delay and decrease – some of the orders already placed. Express shipments, reworks and inventory relocations are necessary to achieve service level. Everybody is constantly under stress and the customer is unhappy. Finally you will sell at discount prices products that you produced but nobody wants…

 

How does your solution to this problem look like?

Demand Driven planning is fully aligned with the new CAS theory of supply chains. When you know what the relevant information is and where the leverage points are in your supply chain you can get it under control and it works. The good news is: it is much easier to implement and operate compared to traditional planning. The two fundamental changes are: a) the placemement at very specific positions of buffer stocks that act as decoupling point and stop the transference of variability (Bullwhip) across the supply chain and b) the use of sales order and pull signals to generate supply and manufacturing order recommendations. Forecast can be used for sizing and adjusting the decoupling buffers, but are not used to generate supply or manufacturing orders.

 

Could you summarise the core concepts in one sentence?

The flow of relevant materials and information drives company returns, not unit cost reduction.

DDMRP vs. MEIO

Time and again we are asked about how DDMRP is different than Multi Echelon Inventory Optimisation (MEIO). At first sight it may seem that the two are aiming for the same objectives, namely to achieve an optimal inventory distribution in the end-to-end supply network. But this is not so. In this contribution I would like to highlight the differences between the two and try to convince you that there is almost nothing in common between them.

Objectives

Despite the fact that the implementation of DDMRP typically results in overall inventory reduction of 30% to 50% across the entire supply chain, this is not the primary objective of DDMRP. We can almost say that the reduction in inventory in DDMRP is a positive side effect. In fact, DDMRP’s main objective is to stop the propagation of variability and increase responsiveness to market changes by decoupling the supply chain. DDMRP addresses the supply chain performance as a whole, while MEIO by contrast is only focusing on inventory optimisation.

Operating model and inventory definitions

MEIO is aiming at optimising safety stock levels. DDMRP is aiming at moving away from a broken operating model, which we will call “MRP forecast push” and replacing it with a new operating model: Position and Pull. In this new operating model the notion of safety stocks disappear and is replaced by the notion of strategic inventory buffers, which have completely different functions. Safety stocks are supplementary positions designed to compensate for the differences between planned orders, actual demand and supply orders. Due to the mechanics of MRP, safety stocks are contributing to increase the bullwhip effect. The DDMRP strategic inventory are primary and dynamic inventory positions designed to decouple the supply chain, compress lead times, stop the propagation of variability and act as a trigger for supply order generation (Pull).

Planning and operating the supply network

DDMRP looks at all levels of planning and execution (define inventory positions and levels, generate supply orders and managing open orders) in a coherent system. MEIO is looking primarily at defining and sizing the safety stocks. But once the stock levels are set and it comes to operating the supply chain, the traditional MRP forecast push model is applied with all the negative consequences associated to it. Given that MEIO is now moving the goalpost constantly, the impact on the bullwhip will be amplified. These facts should account for the differences in inventory performance between the two approaches. When defining the optimal inventory sizing both systems are likely to give the same results. But when operating the supply chain, one system (MEIO) operates under high variability and increased bullwhip while the other (DDMRP) operates under low variability and no bullwhip. The result is that MEIO claims planned safety stock benefits of 20%-30%, while DDMRP consistently achieves 30%-50% aggregate inventory reductions.

Final conclusion

DDMRP addresses the root causes of poor inventory performance and solves them by redesigning the operating model. MEIO is trying to fighting the symptoms by throwing money and computer power at the problem and trying to optimise a broken process.

A Letter to Swiss Manufacturers

Swiss manufacturing companies are currently under extreme pressure from unfavourable exchange rates with the Euro. Their supply chains have huge potential for profitability increase, but unfortunately, the traditional planning tools embedded in their ERP system do not allow them to convert these potentials in real benefits. Demand Driven MRP is a new planning, inventory management and execution framework that has demonstrated impressive results and is revolutionizing supply chain management around the world. The survival of many companies may depend on their ability to innovate their supply chain operating model and become demand driven.

You are in trouble. You are a Swiss manufacturing company and you are under pressure. The recent developments in exchange rates make your goods forbiddingly expensive for international customers. At the end of 2007 the conversion rate was 1.65 CHF for one Euro. In 2012 the Swiss national bank managed to stabilize the rate at 1.20. Now, the rate has stabilized, on its own, at around 1.05. This means that for a European customer your goods in 2012 were 37.5% more expensive and now they are 57% more expensive than they were at the end of 2007.

There are some who would argue that the situation is less bad than it seems. One such argument is that your Swiss products are not competing on price but on quality, reliability and service. All true. But still, a 57% premium on price is a lot. And Switzerland is not the only country renowned for its high quality goods. Germany is, too.

If there ever was an ideal time to take a long, hard look at your supply chain operating model, then this time is now. I am not talking about moving operations to China or some other remote and low cost manufacturing countries. Quite the opposite in fact. I am also not advocating slashing costs everywhere you can find them – again: quite the opposite in fact; some cost cutting programs often damage the substance of the company, are not sustainable or may even have an opposite effect.

What I am calling for, is for you to challenge the most fundamental assumptions of how you manage your operations, and discover that a good part of the solution lies in your hands.

Let’s start by stating what is the goal of every manufacturing company: make money. To achieve that, you buy materials, convert them into finished goods and sell them to your customers. The more efficiently and effectively you can close this loop, the better your returns. Returns are determined by the cash velocity, which is the rate of net cash generation. This is defined as the sales (in Swiss Francs) minus truly variable costs (also known as throughput dollars or contribution margin) minus period operating expense (1). 
Cash velocity, in turn is determined by flow, which is the rate at which a system converts material into products required by a customer. The emphasis on required by a customer is central. Flow is meaningless if nobody wants to buy what is flowing. And here is where your company may be failing and where there is a huge potential to recover cash. In fact, many companies, under the imperative to cut costs take decisions that actually impede flow. All tactical actions aiming at increasing equipment efficiency and utilization, with the objective of decreasing unit cost are in fact resulting in hindering flow and increasing the total supply chain cost. There are multiple reasons for that, and going into depth would require writing an entire book. Here I will just shortly give the two main reasons, that revolve around common fallacies in supply chain management and leave it at that. If you want to know more about the issue you are welcome to read the literature (1). The first big fallacy is that we generally think of supply chains as linear systems. In a linear system, the whole is the sum of the parts. If I want to optimize the total cost in a linear system I just need to optimize the costs of its individual parts. Therefore I cut down my organization into functional silos and I mandate each function to optimize. Wrong! Supply chains do not work that way, because they are not linear systems. They are Complex Adaptive Systems (CAS) and they respond to a complete different set of rules. In fact, using this linear approach will guarantee that the whole system will be sub-optimal. The second fallacy is the unitized fix cost fallacy. Since the advent of MRP II it has become fashionable (yes, fashionable) in many organizations to track standard costs (which are including allocated fix costs and depreciation) and use these cost reports to drive tactical decisions. This drives a whole set of wrong behaviors with the end result of damaging the operations performance. The only costs that your should use for tactical decisions are the ones that are variable in the horizon in question. On a short horizon almost all costs are fixed. Even personnel cost are fix within a three-month horizon (if you exclude overtime and week-end shifts).

Your MRP is definitely not helping you. There is a third obstacle that stands in the way to achieving flow, and it is put in place by the very planning systems that should promote it in the first place. As most other companies, you may have implemented some sort of ERP system. One of the components at the core of each ERP system is a planning module built around an MRP (Material Requirement Planning) engine. This engine was developed in the ’60s and ’70s of the last century to respond to the planning challenges that companies were facing at that time and to leverage the power of these wonderful new machines that were appearing everywhere: the computers. MRP performs well under a set of very specific assumptions. Whether those assumptions were met at the time of its inception is dubious, but in any case they were met sufficiently well for MRP to represent a break-through. What is certain is that these assumptions are not met anymore today; and the results in term of lost operational performance are catastrophic. What is even more certain is the fact that the globalized marketplace is developing in a direction that makes these assumptions more tenuous every day.

The primary assumption for MRP to perform well is that the demand signal is accurate, both in time and quantity. This may well have been the case in 1970, if your company had a full order book and you were operating in a make to order environment, using raw materials procured locally within a relatively short lead time. What MRP does is very simple. It takes the demand signal and calculates backward in time what needs to be ready by when for each manufacturing step, including the procurement of raw materials. But let’s look at the reality today; your customers don’t want to wait for the entire manufacturing lead time to get their products. You may be procuring some materials and intermediates globally or you may be outsourcing to third parties around the globe; lead times are longer than they used to be. This forces you to procure and manufacture some of your products based on a forecast. This is the first big assumption break. A forecast is not an accurate demand signal. Procuring goods or running production based on forecast data is guaranteed to build inventories that your customers don’t want yet. And while you are doing that you are using materials, capacity and resources that could have been better used to manufacture products that your customers actually want. This goes against flow. As said earlier, MRP calculates backwards in time each step of the bill of materials. There are several parameters in the MRP that drive its calculations. Some of them have a truly dreadful impact: dynamic safety stocks, minimum order quantities, rounding factors, time fences and so on contribute to amplify the error in the demand signal and amplify the variability which is inherent to every manufacturing process. This effect is not linear. A small change in the initial forecast will result in major shifts down the manufacturing chain. These effects are well documented and known under the terms of bull-whip effect and MRP nervousness. Because in an MRP the entire bill of material is made dependent, all variability in the system is amplified. The result of all this is that the supply chain performance of your company has become just unacceptable. Inventories are generally too high and not positioned where they are needed. Some inventory positions are too high, resulting in slow moving and obsoletes, while other positions are too low, resulting in loss of sales or delay in production due to missing materials. When these delays in productions affect bottleneck resources, this results in an additional loss of precious capacity. The costs of correcting these inventory misalignments are enormous and they are never captured as cost of goods. These costs include all frenzy expediting activities that result in massive productivity losses (meetings, emails, phone calls, re-prioritizing, rescheduling, crisis management), distribution costs (express shipments), capacity loss (over-utilization and rescheduling of capacity bottleneck resources). At the end of the day you may well have achieved an acceptable service level to your customers, but at what cost!

Demand Driven supply: a revolution in supply chain management. In the last 20 years or so, many process improvement philosophies have been proposed and used more or less successfully. Lean Thinking, Theory of Constraints (ToC), Six Sigma, have all provided benefits in the past and will still be useful in the future. Unfortunately, many implementations of these concepts were celebrated as local best practices but never managed to spread across entire supply chains. There may be several reasons for that. One primary reason is that none of these methods could provide a viable planning and execution alternative that can be deployed across an entire supply chain. In addition, major ERP houses with their marketing power and the complicity of corporate IT departments were able to steamroll local Lean best practice implementations under the “standardization” imperative.

In the last few years however, things have started to change. Some of the best ideas from Lean, Theory of Constraints, Six Sigma and planning best practices have been combined to create what is today officially known as Demand Driven MRP (DDMRP). Demand Driven MRP provides a complete planning, inventory management and execution framework that can be deployed to entire supply chains, including the integration of customers and suppliers. Powerful DDMRP planning software is available as Software as a Service (SaaS). These software packages are extremely flexible, can be implemented within weeks or months and come at fraction of the typical cost of an MRP implementation. Demand Driven is fully aligned with CAS theory. The two main principles behind Demand Driven are a) the decoupling of the supply chain in discrete units by the use of specially designed stock buffers. These buffers are positioned in critical points in the supply chain and have the function of stopping the variability (bull-whip, MRP nervousness) to be passed across, both from the demand side and from the supply side. And b) the use of true demand signals (not forecasts) to generate manufacturing orders, purchase orders for raw materials and distribution orders (stock transfer) for finished goods. Hence the tagline of DDMRP: Position and Pull. A complete description of the methodology was first published in 2011 in the third edition of Orlicky’s classic “MRP” (2). There is a crescent number of companies that have and are implementing Demand Driven MRP or more generally Demand Driven Supply approaches (3) and the results in all cases are stunning. Where there used to be chaos on the shop floor, due to constant expediting, now there is quiet; schedules are stable and capacity is fully utilized to produce what matters. Planners can generates their production plans in hours instead of days. Customer lead times are shorter and market responsiveness is increased, and with it sales and financial returns as well. Inventories are cut by 30-50% and optimally distributed to protect sales or critical operations. Flow and cash velocity are maximized and result in true financial benefits and increased competitiveness.

While Demand Driven will not solve all issues encountered by your Swiss manufacturing company, it can certainly help improving your cash position and reduce your total supply chain cost. These cost savings could be passed to your international customers, thus allowing you to recover some of the lost competitiveness. Additionally, you can use the increased responsiveness to the market as a competitive advantage: some customers may still be ready to pay the Swiss premium for a shorter delivery lead time and better service.

  1. Debra Smith, Chad Smith: Demand Driven Performance (2013, McGraw-Hill)
  2. Carol Ptak, Chad Smith. Orlicky’s Material Requirement Planning, Third Edition, (2011, McGraw-Hill)
  3. Implementations have succeeded in all kind of industries, from Engineering to Order to FMCG and retail and all in between.

Software Standardization and The Agile Supply Chain

We are all aware of the benefits of standardizing processes and I won’t address these obvious benefits here. What I would like to explore is the danger of going a bit too far, especially in the areas of software packages for supply chain planning. And then I will provide a vision for a better future.

Some ERP software houses are working very hard to convince manufacturing companies that their ERP packages – which are constantly expanding to fit new modules and functionalities – can be used to solve any kind of enterprise issue. Sometimes with success. Unfortunately we see again and again companies that – in the spirit of standardization – miss the opportunity to deploy excellent processes by force fitting their requirements to the limitation of the software package they happened to have installed. We have observed several times how best practice pull execution systems have been abandoned and replaced by substandard push systems on the ground that the company-wide ERP system could not “deal” with the existing best practice process.

What many companies failed (and still fail) to realize is that by implementing standardized ERP solution they are also standardizing their operational performance and kill the potential to become best-in-class supply chains. Therefore, if you are one of these companies that has – or is planning to – deploy the latest multimillion-dollar ERP solution in the hope to become best in class in supply chain management, then here is the bad news: you won’t. At most you will become average. Why? Because plenty of other companies have done the same and therefore you cannot hope to get better processes then they have. True excellence can only come from innovative and new approaches that put the process first, and the tool second, as an enabler of the process. The tool must support you in consistently execute the process in the way you want and not the other way around.

Fortunately, times are changing. There is now a new generation of supply chain leaders that start recognizing the issue. They are not satisfied about the way their expensive ERP package is limiting their ability to improve the performance of their supply chain. In today’s ever changing environment they need to have agile supply chains and they see their ERP system as a barrier to achieve that. And they are looking for creative and cheap solutions to overcome this barrier.

We all know, what form most of the time these “enhancements” and solutions looked like in the past: excel spreadsheet! But there is some good news: mini applications. They are fast to design and configure and agile to deploy. Their software-as-a-service and cloud based technology allow to deploy such solution fast and cheaply. These user-friendly mini applications allow collaborative planning, data integration and real time performance tracking on your notebook or iPhone or iPad. They exchange data with your core ERP system, which is still (and rightly so) the backbone of your company and the sole repository of master data and allow the users to complement these data with information from other sources. More and more companies are starting using these tools for anything from forecasting to resource management and continuous improvement. I strongly believe these tools will revolutionize the way companies will be managing their day-to-day business. The future looks like a stable standardized ERP backbone combined with an ecosystem of mini applications to build flexibility, adaptability and agility on top of it. This will allow companies to design innovative ways to manage their supply chains and quickly develop the IT solutions to support their processes, thus breaking free from the rigidity imposed by big, massive, inflexible ERP systems.

Is it really possible to achieve a fast & sustainable reduction in ‘end to end’ supply chain costs without hurting service?

Context

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.

S&OP

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.

Additional benefits

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

If you don’t have Rhythm, you can’t dance !

Providing excellent customer service in volatile markets while keeping a tight leash on working capital is like dancing the tango, beautiful if you get everything right, but a nightmare when something goes wrong.

Over recent years the predictability of demand in many industries has diminished to a point where an accurate forecast which can be used to drive production is about as real as centaurs in Central Park. To get ahead of the game in these demanding commercial environments you have to have an agile and efficient supply chain. However, many companies in manufacturing industry have capacity constraints, budgetary targets and equipment and planning systems which were not designed for this environment. In addition major capital expenditure on new equipment to create capacity headroom or systems is not viable in the short-term either.  Often the solution has to be found using the equipment and technology you have with the minimum of upgrades, and it has to done quickly.

One of the key components of the agile supply chain is Rhythm: a simple inventory planning and scheduling approach based on Demand Driven Supply principles which allows the right amounts of the right products to be produced on demand while level loading the plant and minimising working capital.

Rhythm has its roots in the original Lean Planning Wheel concepts for minimising changeover downtime, but takes the approach to the new dimension by allowing batch sizes and production frequencies to flex while the sequence remains fixed.

This simple adaptation allows the ABC segmentation by volume and different approach to supply for runners, repeaters and strangers, and even make to order to be abandoned resulting in a supply process which is simple, consistent and able to automatically respond to demand changes. While Rhythm is best suited to a make-to-stock (MTS) VMI environment where there is visibility and management of inventory at the customer, it is not restricted to this, rhythm can also work well in a pure make-to-order MTO environment.

How it works.

The rhythm concept comprises four main processes: three as part of the periodic supply planning process and one as part of the production execution process.

Planning.

Inventory Replenishment Level (IRL) calculation – This is the level to which customer inventory should be replenished by production. The calculation is similar to conventional inventory requirements calculations taking into account demand and supply volumes and variability, and post production release and logistics lead times. However, production lead time is replaced by rhythm time – to be discussed later. The IRL can be forward looking to provide a required inventory profile to cater for seasonality and demand trends and is based on historic demand patterns and future forecast.

Forecast is only used to define the IRL and provide visibility of future materials and capacity requirements. It is not used in any way to plan production.

Rhythm calculation – Rhythm has three primary design parameters: sequence, rhythm time, minimum make quantity.  Products are grouped by like production process and allocated to a production line based on this. All products falling into the process family should be included, irrespective of demand profile or if they are MTS or MTO.

Production sequence for the line is determined by applying a changeover hierarchy to minimise the changeover downtime between the one product and the next in the sequence. The most critical design parameter for rhythm is the rhythm time, the time expected to complete one full cycle of the rhythm sequence. This should be no more than 1 week if the full benefits of rhythm are to be realised, this of course means that aggressively reducing changeover time is has to be a high priority.

Myth Buster #1 – A one week rhythm time does not mean that every product is made every week – it means that every product has the opportunity to be made every week if there is demand for it.

The actual production frequency for any product is governed by the actual demand and the “minimum make quantity”, the minimum practical and financial quantity of product which should be made on the line. This is further modified to balance available line capacity against aggregate average demand run time and combined changeover times.

Using these parameters, the line should be  loaded to a minimum of 85% of total available capacity based on design rhythm time and average aggregate demand for the products on that line. The unallocated 15% of capacity is to provide for maintenance and other planned downtime.

A further enhancement is to factor in product value so that high value or high margin products are triggered for production more frequently.

Event Management – An essential part of the IRL renewal, this process determines the impact of demand and supply abnormal events on ability to supply the customer and factors these into the IRL to ensure that product is available to the customer when it is needed.

Execution.

Consumption Trigger – The first thing to understand about rhythm execution is that there is no fixed forward schedule or firm planning horizon. In theory, the next product to be made, the quantity to make and when to make it will be determined at the time the previous product run has finished. However in practice a report called the Consumption Trigger Report (CTR) is run at the start of each shift which gives a forward view of what is likely to be made in the next shift. Firm horizon is governed only by the length of time to stage and prepare materials for production.

The CTR is in rhythm sequence order and uses the following logic: for each product, current available inventory, in-transits and any open production orders are netted off against the IRL. The result of this is compared to the minimum make quantity and if greater, a production order is created for that amount (this can be modified to suit raw material batch sizes or packaging multiples if necessary). If the result is smaller, then the product is skipped and the next product in the sequence is reviewed in the same way. For MTO products, the requirement is simply the order quantity with no modification. A final check is made on material availability immediately prior to production and the production order is released and started. All of this happens a maximum of 24 hours before physical production of that product is started. Ideally, this should be within minutes of starting. At the start of the next shift or day, the current CTR is destroyed and new one created to manage the next period of production. The traditional weekly schedule which absorbs so much time being created, changed and changed again can be dispensed with freeing up time to manage events better.

Once a rhythm cycle is completed, the next starts immediately whether the design rhythm time has expired or not. The result of this is that: in cycles where there is less than average aggregate demand the rhythm cycle finishes early and the next is started; in cycles where there is greater than average aggregate demand the rhythm cycle finishes late. This “breathing” of the rhythm to demand allows production to be continuous and level loaded at the line’s design capacity, while output of product is in line with demand irrespective of the variability of the demand. With sufficient products in the sequence, the demand variability of individual products can be very large, but the aggregate variability will be within acceptable limits of +/- 20% of average on any one cycle.

Myth Buster #2 – Rhythm is applicable to almost any product demand profile, it is not restricted to products with stable demand with low week to week volatility.

Rhythm is self-regulating and will recover from most minor demand and supply events without intervention. In addition, if a product’s demand profile changes (say an increasing demand trend over time), the CTR calculation result will exceed the minimum make quantity more frequently and trigger production more frequently as a result. There is no predetermined ABC product classification which determines a fixed production frequency for a product. This is one of the major departures from Planning Wheel logic.

The primary KPI for rhythm is Rhythm Time Attainment (RTA). This monitors the actual rhythm time for each cycle and provides early warnings of changes in either overall product demand for the line and changes in capacity. This should be monitored using a control chart with the upper and lower process control limits set by the rhythm design parameters. Any trends identified which violate these control limits indicate that something has changed and action may be necessary to resolve it – either fixing a problem, or refreshing the rhythm design. Rhythm time trending low means smaller batches and wasted capacity on excessive changeovers, rhythm time trending high means that there will be more days of demand on the next cycle which will further increase actual rhythm time and ultimate lead to service issues.

The final key component of Rhythm which overarches all the others is discipline. Rhythm is a simple process with a few basic rules which can transform the efficiency and effectiveness of a supply chain. However all will be lost if the organisation does not understand the rules and does not have the discipline and leadership to stick to them when the going gets tough.

The rhythm concept has been tested and proven to be effective across industry sectors in blue chip companies by our consultants over a period of 15 years. SmartChain has developed Smart Apps and processes to support Rhythm as part of our Agile Supply Chain concept, to work alongside existing planning and execution systems, and which can be configured and installed at a significant discount relative to developing custom modules for SAP ERP or APO/APS systems.

For more information please contact us at info@smartchainllp.com

Current Advanced Planning Systems in the market: which are their weak spots?

 Subject Matter Expert: Planning, Scheduling and Execution Solutions for Manufacturing, Supply Chains and Services

Which are the industries where most implementations of APS have failed? And Why? Lack of functionalities? Lack of a suitable architecture? Lack of domain and product knowledge? …

Are there still Planning & Scheduling processes that are not yet well covered and that are business critical? Which ones?

Any feedback is highly appreciated,
Thanks Fabio

Don’t Worry (too much) about Forecast Accuracy!

Demand Planners spend an inordinate amount of time, frequently using very sophisticated software, trying to generate accurate sku level forecasts. They are often disappointed when their efforts fail to achieve a ‘world class’ mix accuracy performance of above 75% and can often feel aggrieved when their perceived failings are blamed as the cause of service misses or inventory problems.

Even if world class performance is achieved, however, it will inevitably include some individual sku’s with accuracies in the upper 90s but also many well below 50% and maybe even in negative territory  (ie. the error is greater than the forecast , as is often the case with supply chains in which integrated DRP isn’t feasible due to lack of appropriate systems so the supplier is having to service a country distributor, maybe an affiliate, on an ex-stock forecast driven basis). The range of sku level accuracies within a  ‘world  class’ forecast  is related to the level of sku  demand volatilities. In general, the greater the demand volatility (as measured by the coefficient of variation) the more inaccurate the forecasts will be. This is because large random errors cannot be predicted,  forecasting  algorithms  tend towards stability (as designed for driving supply) and such demand patterns often  attract additional, well intentioned, forecasting intelligence which, ironically, tends to generate even greater volatility and inaccuracy.

To get round the problem of forecast error, forecast consumption rules can be selected (although choice of the most appropriate is not always obvious and they can generate quite different outputs) and there is always available the calculation and use of ‘backcast’ error based safety stock to buffer the supply schedules. Forecasting systems have also become ever more sophisticated through using Bayesian techniques and,  recently, ‘big data’ demand sensing and shaping technologies between trading partners have begun to appear using the premise of ‘Why forecast when you can calculate?’

In general however, most sku level forecasts, even those which are ‘world class; are so inaccurate that they shouldn’t be used to directly drive replenishment execution. 75% accuracy is, of course, 25% wrong and most products in a portfolio are below this – it  is only the relatively few stable demand sku’s which can be forecasted with accuracies above 95% and it is these which positively skew average performances towards ‘world class’ due to their high volumes. Even if the forecasts were all above 90% accuracy, use of the forecast to drive replenishment would still be wrong because there is a replenishment signal which is always 100% accurate………….that signal, of course, is real demand itself – so why use a forecast?

To understand why ‘demand pull’ is vastly superior to ‘forecast push’ it is worth considering the impact of forecast inaccuracy upon  DRP/MRP calculations. These generate replenishment recommendations aimed at  achieving safety stock levels but, because the forecasts are incorrect,  and are always being updated,  exception messages  are ceaselessly generated  and suppliers  and factories are frequently asked to amend and change their schedules at short notice.

Not only do these exception messages indicate that the wrong quantities of stock have been sent to the wrong places, and the wrong product mix is being produced, the schedule changes also lead to unplanned machine changeovers and  lost capacity. Inevitable knock on effects up and down  the supply chain cause lead times (and  WIP) to fluctuate and,  unfortunately, as ‘Factory Physics’ (1) proves, the greater the level of variability that a supply chain experiences, and the higher are desired levels of capacity utilisation, the longer and more volatile lead times become which is absolutely antithetical to the core MRP tenet that they be stable. Service issues and supply schedule instability are the result with their consequent costs, and average stock levels tend to exceed the theoretical ‘safety stock plus half the average batch size’ as planners tend towards ‘safe scheduling’ rather than actually using the safety stock.

As if  forecast induced  volatilities aren’t bad enough by themselves, they are a whole lot worse when the end to end supply chain is affected by ‘bullwhip’, which it always is when “forecast push DRP/MRP” techniques are used. ‘Bullwhip’ occurs when small changes in consumer demand get amplified by the forecast  as they are passed up the supply chain causing factories and suppliers to respond to very much higher levels of sku demand variability (and its attendant costs) than they otherwise would.

Bullwhip is caused by batching and the impact of the well intentioned behaviours of the many supply chain players all attempting, in their own way, to make sensible forecasting and replenishment decisions. Unfortunately, due to poor supply chain visibility and information delays (ie. ‘latency’)  the outcome is a cycle of increasing over and under error correction resulting in end to end demand amplification and volatility as explained through, and can be modelled by, engineering control theory, systems theory and, even, chaos theory (2). Readers may have experienced these problems when playing the ‘Beer Game’.

So, if your factory and your suppliers are working hard to eradicate their sources of variability then your ‘forecast push DRP/MRP’ driven replenishment and ordering process is simply adding another source of that same variability which is increasing  costs and/or causing you/them to have to work with unnecessarily high levels of stock buffer or extended response lead times. Any  instability, of course, can also disrupt and slow down the CI process as everyone focuses on meeting the latest service issue instead!

The impact upon product costs of bullwhip volatility has been demonstrated to reduce product margins by up to 30% (3). This is due to the stock holding costs and because all operations (ie. those of factories, warehouses and freight as well as those of suppliers) perform most efficiently when under predictable  stable conditions as demonstrated by the well known Toyota House schematic in which ‘Stability’ is the foundation;  and as any Operations or Supply  Manager will tell you.

Despite all the efforts of Demand Planners and investment in technologically sophisticated software, the use of forecasts to drive replenishment through MRP/DRP logic  will always generate the volatility creating latency and inaccuracy issues to a greater or lesser extent. The only way to avoid generating forecast induced variability and bullwhip is to stop using ‘forecast push’ replenishment.

Is there an alternative? Fortunately there is and it’s called ‘Demand Driven Planning & Replenishment’ (DDPR) in which, at each stock location for each item, an appropriate non-forecast based replenishment technique is selected and, across the full end to end supply chain, the techniques, replenishment triggers and buffers are aligned to ensure they successfully support each other and the rates of consumption at each and every echelon. 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.

From a stability perspective, these techniques protect Operations and Suppliers from demand volatility by preventing it being amplified into ”bullwhip” and ensuring that any remaining is correctly buffered and that the safety stock is actually used..

The key ‘make to stock’ demand driven replenishment techniques available are, broadly, the following

  • Consumption-based pull – 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 a 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/small batch production.  Ironically, stable 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 more effective and simple alternative is available? Level schedule is also very useful for product launches, with regular review, as it generates the stability that operations need to enable them to focus upon  improving the, possibly, new manufacturing processes.

Whereas ‘forecast push’ requires the forecast to be accurate on a time phased basis, which it can never be, these ‘demand pull’ processes allow the supply chain to autonomously respond to demand variations and enables Planners to concentrate properly upon Inventory Planning. Inventory is now effectively managed as ‘capacity’ to respond to demand and meet service requirements. Through replenishment parameter management, inventory planning therefore has as significant a role to play as conventional capacity planning. The parameters up and down the supply chain need to be aligned and should include an appropriately calculated element of safety stock to reflect demand uncertainty. Unlike ‘forecast push’, however, these safety stocks are actually planned to be used and are designed to protect operations against any residual variability. Despite the criticality of effective inventory planning however, the replenishment parameters 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.

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 error and ‘bullwhip’. When volatility is very significant, and ex-stock service is not an option, other demand driven replenishment technique options can be selected. These are those in which time is used as the buffer and, depending on the required service strategy, the technique chosen might be ‘assemble to order’ (ATO) in association with postponement strategies, 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. In this way postponement with ATO, and supported as necessary by appropriate ‘design for manufacture’ and asset configuration, combined with upstream ‘demand pull’ or level schedule, can deliver cost effective and responsive ‘Agility with Stability’.

S&OP, of course, is an essential support process for demand driven replenishment as it is  forecast based and forward looking. In addition to aligning commercial and SC operations with the financial plan, one of S&OP’s aims is to also align material and capacity availability with the demand plan. To achieve this, however, high levels of time phased sku level forecast accuracy aren’t required. As work centre capacities and materials are used across a range of products, aggregated forecast accuracies of 95% are generally quite easy to achieve.

Companies that use DDPR not only benefit from significantly better operational performance but 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 ‘blaming’ of the forecast for service misses and less short term schedule changes which reduces stress, pressure and the need to ‘achieve the impossible!’

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 variability and capacity utilisation with average queue times. DDPR has been part of the Lean toolbox since at least the early 1980s and, in fact, ‘pull’ 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)

It is important however, 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, senior cross functional leadership support and a capable and interested supply chain team that includes Order Management through to  Supply Management.

How would successful implementation of DDPR affect Planning and Supply Management activities?  The good news is that it will remove a lot of tedious and non -value added work such as continuously cutting and re-cutting of supply plans, expediting and then having to explain why there are service/inventory issues, inbound late deliveries and lost production hours. Planners should be able to allow Operations to “respond to what they can” while they plan what is necessary. These planning activities will include S&OP, demand profile analysis, replenishment technique selection and management of  replenishment cycles and inventory parameters…………all of which might be termed supply chain “conditioning” or “tuning”. Other key activities which can now receive adequate focus will be collaboration initiatives with customers and suppliers, new product launch planning, promotions and tenders management and, by exception, the abnormal demand (and maybe supply) management process. Supplier Management teams will also be able  to spend more time on their value add activities such as supplier S&OP, price/cost analysis, supplier development etc.

Additional significant benefits are that warehouses, factories and suppliers will all notice that their daily ‘work to’ lists become far more predictable and stable. In consequence adverse cost variances will diminish, unplanned overtime will become a thing of the past and more quality time can be spent on CI initiatives that minimise supply variability and cost  instead of chasing the latest backorder.  Supplier 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!

Should we be concerned about forecast accuracy? If using DDPR, forecast accuracy needs only reach that required for supporting ‘family’ level S&OP and this is relatively easy to achieve. SKU level accuracy on a time phased basis is not at all important and little effort should be put into achieving a ’world class’ mix performance. Instead, Planners should focus upon the far more feasible and value add task of Inventory Planning  in order to ensure that the replenishment parameters are aligned and capable of meeting average demand levels and trends.

 

References

1 – Hopp & Spearman (1996) “Factory Physics”

2 – See for instance

-Disney, Dejonckheere,  Lambrecht, Towill (2003) “Measuring and avoiding the bullwhip effect: a control heretic approach” European Journal of Operational Research  147(3), p567-590

– Sterman (2006) “Operational and Behavioural Causes of Supply Chain Instability” p17-56The Bullwhip Effect in Supply Chains: A Review of Methods, Components and Cases

-Wilding (1998) “Chaos Theory; Implications for Supply Chain Management”, International Journal of Logistics Management, 9(1), p43-56

3 – Metters (1997)  “Quantifying the bullwhip effect in supply chains”.  Journal of Operations Management  15(2)  p89-100

 

Do you want your Supply Chain to be Agile without Destroying Operational Performance?

Anyone who works in a commercial manufacturing business will be all too aware that there is continuous pressure to achieve service excellence while reducing stock levels, costs and component prices. And, with  growing focus upon penetrating emerging markets, off shore manufacturing and increasing ‘low cost’ competition, that pressure just gets greater but harder to achieve.

One of the main barriers, for many companies, to achieving a step change in Supply Chain, Operations and Procurement performance is their use of sku level forecasts to drive replenishment through DRP and MRP calculations. These generate replenishment recommendations aimed at achieving safety stock levels but, because forecasts are inevitably incorrect (which sends stock to wrong places and drives manufacture of the wrong product mix) and are always being updated, the exception messages never cease (irrespective of whichever forecast consumption technique has been selected) and warehouses, factories and suppliers are frequently asked to amend and change their schedules at short notice.

This, as the ‘queuing theory’ behind ‘Factory Physics’ (1) clearly proves, leads to unplanned machine changeovers and lost capacity with inevitable knock on effects up and down  the supply chain which cause lead times (and  WIP) to  fluctuate frequently, requiring everyone to have to respond, at short notice, to apparent changes in their customers’ requirements. Unfortunately, the greater the level of variability that a supply chain experiences, and the higher are desired levels of capacity utilisation  the longer (and at higher levels of capacity utilization the lead times increase exponentially) and, significantly, more volatile lead times become which is absolutely antithetical to the core MRP tenet that they be stable.

As if these forecast induced  volatilities aren’t bad enough by themselves, they are a whole lot worse when the end to end supply chain is affected by ‘bullwhip’, which it always is when ‘forecast push DRP/MRP’ techniques are used. ‘Bullwhip’ occurs when small changes in consumer demand get amplified by the forecast  as they are passed up the supply chain. It is caused by batching and the various players within the supply chain behaving in way which, while well intentioned, actually amplifies volatility, as can be explained by engineering control theory, systems theory and, even, chaos theory (2).  ‘Bullwhip’ causes warehouses, factories and suppliers to respond to very much higher levels of sku demand variability (and its attendant costs) than they otherwise would.

So, if your operations and  suppliers are working hard to eradicate sources of variability then your ‘forecast push DRP/MRP’ driven replenishment and ordering process is simply adding another source of that same variability which is increasing  costs, generating service issues and, usually, also causing high levels of stock  and extended response lead times. Any instability, of course, can also disrupt and slow down the CI process as everyone focuses on meeting the latest backorder instead!

The impact upon product costs of bullwhip volatility has been demonstrated to reduce margins by up to 30% (3). This is due to the stock holding costs,  the wasted unplanned capacities and because all operations (ie. those of factories, warehouses and freight as well as those of suppliers) perform most efficiently when under stable conditions as any Operations Manager will tell you.

If  the only way to avoid generating forecast induced volatility and bullwhip is to stop using the forecast to drive replenishment, is there an alternative? Fortunately there is and it’s called ‘Demand Driven Planning & Replenishment’ (DDPR) in which, at each stock location for each item, an appropriate non-forecast based replenishment technique is selected and, across the full end to end supply chain, the techniques, replenishment triggers and buffers are aligned to ensure they successfully support each other and the rates of consumption at each and every echelon. These techniques, of course, don’t just apply to a company’s internal supply chain, they can also be the basis for collaboration with suppliers and customers.

From a stability perspective, these techniques protect Operations and Suppliers from demand volatility by preventing it being amplified into ”bullwhip” and ensuring that any remaining is correctly buffered. The key ‘make to stock’ demand driven replenishment techniques that can be used are, broadly, the following

  • Consumption-based pull – 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 a 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/small batch production. Level Schedule is also very useful for new product launches as a means of achieving stable production when demand is notoriously fickle.

Whereas ‘forecast push’ requires the forecast to be accurate on a time phased basis, which it never can be, these ‘demand pull’ processes allow the supply chain to autonomously respond to demand variations. This enables Planners to concentrate properly upon Inventory Planning. Inventory is now effectively managed as ‘capacity’ to respond to demand and meet service requirements. Through replenishment parameter management, inventory planning therefore has as significant a role to play as conventional capacity planning. The parameters up and down the supply chain need to be aligned and should include an element of safety stock to reflect demand uncertainty. Unlike ‘forecast push’, however, these safety stocks are actually planned to be used and are designed to protect operations against any residual variability. Despite the criticality of effective inventory planning however, the replenishment parameters 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.

The other key demand driven replenishment technique options are those used when demand volatility is so great that it is uneconomic to provide an ex-stock service. In these situations, time can be used as the buffer and, depending on the required service strategy, the technique chosen might be ‘assemble to order’ (ATO) in association with postponement strategies, 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. In this way postponement with ATO,  and supported as necessary by appropriate ‘design for manufacture’ and asset configuration, combined with upstream ‘demand pull’ can deliver cost effective ‘Agility with Stability’.

The choice of replenishment technique is an essential part of the ‘Demand Driven Planning & Replenishment’ process and should be undertaken for each material at every echelon. The criteria for selection is the item’s demand profile (volume and variability) and ranges from level schedule for high volume / low variability items, through ‘demand pull’ or ‘make/ship to replace’ to ATO and MTO for the high variability items.

S&OP of course, is an essential support process for demand driven replenishment as it is forward looking and, in addition to aligning commercial and SC operations with the financial plan, one of its aims is to align material and capacity availability with the demand plan.

If DDPR is so effective, it’s reasonable to ask why it isn’t practiced more widely? In fact ‘pull’ is increasingly common within the factory walls and, in some industries, with suppliers     (eg. grocery multiples and automotive) but is rarely seen 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 Warehouse, Factory and Procurement Managers 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).

It is important however, 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, senior cross functional leadership support and a capable and interested supply chain team that includes Order Management through to  Supply Management.

How would successful implementation of DDPR affect Planning and Supply Management activities?  The good news is that it will remove a lot of tedious and non -value added work such as continuously cutting and re-cutting of supply plans, expediting and then having to explain why there are service/inventory issues, inbound late deliveries and lost production hours. Planners should be able to allow Operations to “respond to what they can” while they plan what is necessary. These planning activities will include S&OP, demand profile analysis, replenishment technique selection and management of  replenishment cycles and inventory parameters…………all of which might be termed supply chain “conditioning” or “tuning”. Other key activities which can now receive adequate focus will be collaboration initiatives with customers and suppliers (using DDPR principles!), new product launch planning, promotions and tenders management and, by exception, the abnormal demand (and maybe supply) management process. Supplier Management teams will also be able  to spend more time on their value add activities such as supplier S&OP, price/cost analysis, supplier development etc.

Additional significant benefits are that warehouses, factories and suppliers will all notice that their daily ‘work to’ lists become far more predictable and stable. In consequence adverse cost variances and unplanned overtime should diminish significantly, and more quality time can be spent on CI initiatives that minimise supply variability and cost  instead of chasing the latest service problem.  Supplier 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 should be able to operate far more efficiently and some of the consequent cost savings should accrue to their customers!

References

1 – Hopp & Spearman (1996) “Factory Physics”

2 – See for instance

-Disney, Dejonckheere,  Lambrecht, Towill (2003) “Measuring and avoiding the bullwhip effect: a control heretic approach” European Journal of Operational Research  147(3), p567-590

– Sterman (2006) “Operational and Behavioural Causes of Supply Chain Instability” p17-56The Bullwhip Effect in Supply Chains: A Review of Methods, Components and Cases

-Wilding (1998) “Chaos Theory; Implications for Supply Chain Management”, International Journal of Logistics Management, 9(1), p43-56

3 – Metters (1997)  “Quantifying the bullwhip effect in supply chains”.  Journal of Operations Management  15(2)  p89-100