Perhaps in the future, sentient robots looking back at humans today will consider that we were a somewhat random bunch of people as no two of us are the same. Human actions and choices cannot be predicted reliably, worse even than the weather. As with any team however, our ability to rationalize in many different ways in parallel is, in fact, our strength, creating a kind of biological “fuzzy logic.” Robots will have to cope with mankind’s wishes for differentiation, no matter how illogical that may be. Rather than repetitive automation, the Industry 4.0 computerization concept has been created to find efficiency in automated randomness. The crux of such a process is the ability to be prepared for, and to be able to manage, the increasing mix of products and their variants effectively, just as smoothly as if factories were producing in high volume. When considering Industry 4.0 therefore, being able to digitally configure to order (CTO), rather than having to be responsible for hundreds or thousands of individual bill of materials (BOMs), in a simple and effective way is an essential, critical business need.
Automation has been a key element since the start of the third industrial revolution, where electrical systems and controllers have been running machinery designed to replace human manual dexterity. Until quite recently, automation has been rather dumb. Simple automation was dominant where machines simply followed a set of sequential instructions, or, in the case of assembly robots, copied a series of movements. More evolved automation can apply alterations and corrections based on, for example, the processing of visual checks on SMT machines, which correct the position and orientation of materials picked up or positioned for placement. The challenge for the creation of the vast majority of automated processes was made very much easier by the notion of mass production, where once set up, the automation would simply repeat what it was doing in a very efficient way, with very high equipment utilization. Unfortunately, the heyday of repetitive high-volume manufacturing for most of us has passed.
It seems ironic that automation has played the initial part in the downfall of mass-production itself. Adopting automation meant that the quantity of products made could be increased drastically over manual production lines. This meant that the market for such products expanded. Requirements in expanded markets meant that there needed to be different versions made, for example with different electricity or communication standards, including of course human language. The concept of variants was born. Once established, marketing teams got involved, using variants to find ways of targeting against competitor’s products, making lower cost versions, higher featured versions. Any company could then target increasingly well-defined niches of customer need with exactly the right cost-effective product.
After marketing, came fashion. Technology has become fashionable, creating the need, for example, in personal devices such as cellphones, to be available in many different sizes, colors and styles. Perhaps as an extreme example, but genuine nonetheless, a specific original design of mobile phone is now manufactured with thousands of individual variants, depending on feature level, resource level, wireless options, software options, language, service provider, colors, etc. Keeping track of which phone is which during manufacturing is an absolute nightmare. Most of the variants look the same throughout most of the processes, but have different assembly combinations, including subtle differences in electronic component placement positioning. The worst aspect of this is yet to come.
Figure 1: Knowing what is currently executing and the status of each process in the factory provides critical information for Industry 4.0 computerized management systems.
Since there are so many variants, the cost of storage of semi-complete and completed products has increased in line with the number of variants being produced. In most cases, the business demand has been to avoid stock of products as far as possible. The number of days of stock throughout the distribution chain has typically shrunk from being many months, to a few days at most. For manufacturing operations that are remote from their markets, such as China, the reduction can only be achieved by air-freight rather than sea-freight, increasing distribution costs and environmental impact. Companies that manufacture close to their markets, typically those remaining in the West, are pushed to be almost “make to order” so as to keep needless investment of stock to an absolute minimum. In-factory warehousing is included. The opportunity for manufacturing to smooth the effect of high product mix on the lines by creating stockpiles in the warehouse is rapidly running out. The reality now must be faced. Manufacturing must be capable of producing multiple configurations with complete control and without any loss of productivity. Those companies that can achieve this are surely the companies that will succeed, having reduced the extortionate operational overhead of mixed and low-volume production to an absolute minimum. This is the condition that Industry 4.0 achieves through the use of computerization.
When considering implementation of Industry 4.0 solutions, following the hype in the market, attention typically is focused on the need for communication between machines on the shop-floor. Knowing what is currently executing and the status of each process in the factory, as well as all the related resources and support operations, provides critical information for Industry 4.0 computerized management systems. This includes work-order creation and assignment, as well as the control and planning of related resources and support operations. What is often neglected however until much later in the process, is the management and control of the engineering definitions of products and related variants that are to be made in this ultra-flexible factory. Engineering control is quite a challenge considering the many tens, hundreds or indeed thousands of products and variants that can potentially be live in production in a single factory at any time. The management of the precise engineering data, which includes visual aids and documentation, to be supplied to all processes exactly when needed can quickly become an extreme drain on engineering resource. Add to this the need for conformance, where every set of engineering data, whether comprising a set of machine instructions, or an ISO controlled operation standard for a manual process, needs to be issued and confirmed into place prior to specific execution of a work-order.
Apart from the number of different products and their variants, managing the many sets of BOMs on an individual variant basis is made more difficult where the differences between each variant can be quite small, but critical. Many of the differences will be at the end of the production process, for example the casing, manual, or packaging which is different. Of course, making a mistake here carries the same level of importance as anywhere else, as no-one wants to get a product with the wrong manual or power adaptor, or worse, with the wrong version of software. Small differences earlier in the manufacturing process, such as those in SMT production may make no discernable visual difference, but can affect many of the tests and processes that come later, as measurements and inspections may show up different results. Any level of confusion about what is supposed to be there and what is not, is simply not an option in manufacturing. The management of individual variant BOMs therefore requires a very thorough team of engineers.
The concept of configure to order reduces the number of individual BOMs that need to be managed, eliminating the confusion and risk associated with individual variant BOMs. The classic perception of configure to order is on a final assembly line, where products are being made according to individual customer specifications, or, where there are defined sets of features that makes up a set of standard variants.
An example of this is common in the automotive industry, where on the final assembly line, for each base model of car, there are defined model variants, each adding a set of upgraded components such as engine, multimedia, lights, navigation system, etc., as well as individual choices that the customer has made, such as the color of the car. Production of the Ford Model T is a famous example of a non-flexible, automated mass-production line that achieved success by reducing costs and increasing productivity.
Today’s final assembly lines boast that they can produce flexibly on a non-stop, fixed-tact, final assembly line capable of making any combination of models, variants and individual customer choices. While this implies 100% efficient production, it hides a great deal of associated waste, which is constantly having to be optimized behind the scenes. Without knowing what is to be made each day would require material availability to satisfy any quantity of any combination of any specification each day, representing a huge amount of material investment.
Many of the optional components, especially the more expensive components, are likely to be rarely needed as compared to options lower in the range. It is a waste of investment to have unnecessary stock at the line, so some logic and planning needs to be done to ensure that the materials are there only when needed. The suppliers of such optional materials, which today mostly feature complex electronics sub-assemblies, would bear the brunt of the randomness of customer demand, and so face the very high volatility of demand on individual product variants that they supply. This can make their business very inefficient. Some grouping therefore, especially for high-cost, rarely-used materials would reduce investment cost overheads, but only if the “random” production could also be grouped. The compromise of the flexible final assembly line then creates follow-on issues. The waiting list for a car to be manufactured after placing an order with options, is quite significant. It can now take between 12 and 20 weeks for a specified car model to be scheduled, made and delivered from placement of a customer order. Many customers will not wait that long, and could go to competitors. The time is required by the car maker to optimize the supply-chain to minimize the risk of shortage of key components without incurring the need for material storage.
By focusing on an efficient final assembly, the costs of variant manufacturing has not necessarily been avoided, but is more likely to have been shifted up the supply-chain. The challenge to produce on demand at a competitive price is spread among all the suppliers. The cost to the business for flexibility is still there, but accounted for in a different way. Though this example is related to a flexible final assembly production line, the concept and challenge applies equally to any factory where the factory output is controlled by the customer and needs to be flexible, including by the way, all the suppliers of sub-assemblies that support the automotive line.
The better the communication between manufacturing and the supply-chain, the “Leaner” the whole process of assembly with a high degree of variants, and high mix, will be. At the core, in engineering, is the product definition as described in the BOM. A configure to order BOM looks much the same as a regular BOM, except that certain part numbers refer to a choice of actual materials, representing options. These can be individual material choices or the choice of a set of material dependent features, or a combination of the two. Rather than having to create and select a unique BOM from literally thousands of potential variations, a single configure to order BOM will represent all feature and option combinations.
The configure to order BOM has all the various options defined through the use of the part number key-word, which is set up just once. It can then be simply used when planning and creating any and all of the different specific product configurations that are required, whether for a set group of products or even a single unit. As the configure to order BOM is processed during, for example, the creation of a work-order, the choices are offered to select the material, set of materials or sub-assemblies to reflect the specific variant need. This selection is stored as part of the work-order, ensuring that the correct engineering information is available at all work-stations and processes throughout manufacturing, and as a look-ahead for material preparation and supply-chain. All aspects of the MES software that works with configure to order BOMs will automatically follow the designated selected options without further manual overhead. The whole process of MES with the configure to order BOM is therefore several orders of magnitude simpler and more secure than handling multiple unique BOMs.
Systems supporting the configure to order BOM as an integral part of a singular MES solution provide the most efficient way to plan and execute in the high mix and volatile environments associated with the extreme flexibility that Industry 4.0 requires. No matter how the operation is planned, how work-orders are created, or how flexible production has to be moving products between configurations, the ability of the engineering team to remain in control of the product data across all of the variants is critical. The management of visual aids and documentation throughout the process is imperative. For those operations that are yet to see significant numbers of variants, it should be remembered that as the number of product options and variants increases, the number of unique BOM combinations increases exponentially. With the configure to order BOM this is not the case, each additional variant is simple to introduce. Any assembly factory that is aiming to become an Industry 4.0 operation, with flexibility to make products according to the real demand, without the use of expensive stock in the distribution chain should seriously consider the adoption of an MES solution that includes built-in configure to order capabilities.