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Industria 4.0 necesita las reglas Demand Driven MRP para alcanzar los objetivos.

Patrick Rigoni, Socio Fundador de la Consultora Suiza SmartChain International, publicó este interesante artículo en el que analiza el enorme potencial que promete Industria 4.0 y como, a su vez, Demand Driven MRP es la pieza clave para que sea posible alcanzar esos objetivos  de mejora.

 

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

 

By Patrick Rigoni

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
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