Synergy Between Smart Manufacturing and the Secure Supply Chain (Part 2)
Editor’s Note: To read Part 1 of this article series, click here.
With the trend of shortening lead-times within the material ordering process, and the frequency with which urgent orders for materials from the grey market are made, there is increased opportunity for the ingress of counterfeit materials. The situation is made far worse when a genuine shortage of materials in the supply chain occurs. In many cases, the reaction to material supply issues in the market are addressed by the over-ordering of the affected materials in order to ensure supply for as long as possible, which only makes the problem worse, in general, across the industry, quickly escalating serious shortages to critical levels.
At these times, assembly manufacturers become desperate for material supply, which is also felt by material distributors where contracts are in place to assure supply. The search for eligible materials to satisfy the volatile demand goes far and wide, creating motivation and opportunity to fulfil materials orders with materials that are not of the standard or quality specified. Since most assembly manufacturers have trusted supplier agreements in place, incoming inspection of materials on receipt at the factory is no longer comprehensively performed.
Any counterfeit materials are likely not to be detected until the end of the assembly process as the product is tested; and often, not even then. Counterfeiters have been found to utilize techniques that specifically defeat incoming inspection procedures and create enough confusion to render the assembly company unable to find responsibility for the counterfeit material ingress. One example was found where the first 100 parts on a reel of materials were genuine, and then every seventh part was found to be counterfeit. Incoming inspection, were it to be in place for passive devices, would normally look at only the first materials in the reel. Having each seventh part counterfeit produces a random enough pattern to cause maximum confusion, should any defect be found during assembly and test. As these are cheap passive components in this example, little effort would be expected to manage them within the factory. The number of instances of counterfeit materials with passive components is, however, increasing rapidly. Even though the passive material itself may be insignificant as compared to, for example, high value ICs, the shortage of, or defect in, just one will prevent the product, and potentially the whole work order, from being shipped. With many counterfeit materials having an equivalent function, but to a lower specification than that of the genuine materials, it is very easy for counterfeit materials to go undetected through assembly and test processes—and become a part of safety critical products. Though it is possible to detect counterfeits through the use of inspection, test, and even X-ray processes, it is simply not practical to test all of the millions of material components that are placed by the average factory every day. Manufacturing could never bear the cost of that in today’s competitive world.
On the flip side, the use of electronics has become critical to virtually every aspect of human society, being a key part of every appliance and communication device, mode of transport, defense systems, etc. The consequences of not being able to eliminate the risk of defects getting through into the market will be disastrous for individuals as well as for the company brand image, not to mention the associated costs of recovery. It is only a matter of time, unless something very significant changes.
The Smart Factory
Discussion of traceability as part of the smart factory is not high on the agenda in most conversations. The focus of the smart factory is to deliver on the goals of providing flexible manufacturing, free of legacy constraints, which can deliver highly optimized use of assets, even with a high mix of products in small lots. A key aspect of the smart factory, however, is the ability to be in full control of production dependencies, which includes material flow. Industry 4.0 represents digitalization across all operational processes, sharing data seamlessly. An Industrial Internet of Things (IIoT) standard such as the Connected Factory Exchange (CFX) from IPC is the most effective way of achieving this.
Figure 1. The Smart Factory, Industry 4.0.
Within the smart factory, data is exchanged between automated machine processes, as well as manual assembly and test processes, so as to provide clear visibility of the operation of the factory. Data is exchanged at the execution level, allowing machine or line-based software to self-optimize their operations. Data is also exchanged between the execution layer and the factory layer, providing machines with specific material and work-order information, as well as providing exact material usage and spoilage information in the opposite direction. Many MES functions work together within the factory layer to provide an overall factory optimization, working in real-time, as customer demand changes. Data is then also exchanged with the high-level business systems, which potentially includes ERP and MRP, to ensure that they have the very latest information of what products have been made, are to be made in the near term, and what materials have been used and physically allocated at each operation.
For the scope of this article, the focus is on just four of the many areas that are the key elements belonging to the smart factory:
- Process engineering
- Adaptive planning
- Lean material logistics
The approach to process engineering in the smart factory is the complete opposite of what has been done traditionally. Instead of the assembly configuration being set in advance by process engineering, a common digital product model is created up-front, which can then be easily exported into data ready for any capable line configuration as required. The process starts with the elimination of all the disparate documents previously used to transfer the design and bill of materials (BOM) information into manufacturing.
The recommended method in the smart factory is to use a single file that includes all aspects of the design data, for example, the IPC-2581 format. This contains all the needed elements from electrical and mechanical design, material specifications, as well as the design BOM information, within a single file. The need for the manual creation of the product model, cross-referencing of information and conflict resolution is completely eliminated with the use of IPC-2581. The effect, however, on the assembly engineering process is to reduce the approximate eight days of work and lead-time down to just a few minutes. With a capable digital process manufacturing engineering tool, the digital product model can be utilized to output on demand the many different formats for any selected machine or line configuration, which is seen as the best way to meet the immediate customer demand. While this step-change improvement in engineering practice will resonate with those seeking to reduce the lead-time for the introduction of products as well as the cost of data preparation, for Industry 4.0, this also provides the critical ability to introduce engineering flexibility in the manufacturing process, allowing the movement of products between line configurations at short notice, to meet changing rates of delivery as demanded by customers. As well as operational flexibility, the reduction in work and lead-time also allows a wider acceptance of materials to be used, for example where ICs are delivered on trays instead of reels. The accommodation of a much wider degree of material physical qualification, can be easily built into the operation, allowing far more flexibility for material purchasing.
Adaptive planning is a term that has recently been associated with smart factories, but in fact it represents the evolution of the actual practical planning method that has been in use on most shop-floors for many years. The legacy adaptive planning tool of choice is simply Excel. Other planning tools, of which there are a great variety available, whether based on infinite, finite or APS models, are focused on mid-range or long-term planning tasks, meeting customer deadlines agreed far in advance, with little actual regard for execution level optimization of the operation. This has been left to production engineers to figure out for themselves, based on their knowledge of the processes, with the aid of Excel to help plan out the actual optimized sequence of work. With Industry 4.0, this model is pushed to the extreme, as continuous assessment of work allocation needs to be done in line with almost daily demand changes from customers. Excel is a great tool, but is simply not capable of understanding and representing all of the live planning constraints needed to avoid risk of execution or on-time delivery failure, such as with consideration of the actually available materials. The smart adaptive planning solution builds on the experience of those who have depended on Excel for so long, to bring visibility and understanding of constraints into focus, allowing planning engineers to make quick and accurate allocation and sequencing decisions whilst retaining the maximum possible operational efficiency. This is achieved through the live sharing of information from both automated and manual operations, in terms of products completed and materials consumed, as well as transactional processes such as material logistics.
Having reliable data about material consumption, including usage and spoilage, is another key element of the smart factory. A lean “pull” signal for materials can be generated based on actual material consumption rates, together with details of follow-on scheduled work orders, which can then be used to create a live logistics plan for the transport of materials from the warehouse, or local point of use stores to the machines, only as and when needed, in other words, just in time (JIT). Live data collection from assembly processes ensures that every material is accounted for, creating a near-perfect record of inventory, which can subsequently be shared with ERP and MRP. The need for the creation of pushed kits can be completely avoided. The accurate visibility of material inventory with which to execute work-orders in the foreseeable future is accurately determined by the use of adaptive planning, so the build-up of unnecessary physical material buffer stock, both on the shop-floor and in the warehouse, can be completely avoided, without any risk of unexpected internal material shortages. With MRP now able to know exactly what materials are in the factory, an accurate and less urgent series of material orders can be placed, with more flexibility.
When a modern smart digital MES system, specifically designed for the IIoT environment, is providing support for manufacturing execution—lean supply chain management on the shop floor in the smart factory—full traceability occurs as a natural outcome, being essentially “free” from an operational cost perspective. With lean materials, all carriers of materials, as well as individually marked key components and sub-assemblies, are uniquely labelled and tracked from their original packaging. As each instance of a material is used in production, there is traceability of the exact origin of the material used, whether an expensive IC or an apparently inconsequential passive component. If any material quality error should occur, such as the detection of a counterfeit material, the exact incoming material packaging can be identified, as well as all of the uses of material from that packaging, including the location of any remaining material.
Traceability Vs. Counterfeit Values
The availability of exact traceability data has a profound effect on potential consequences of counterfeit incursion. This is illustrated from the following examples of real cases of counterfeit materials causing issues in the market, though details have been changed to protect the identify of those concerned.
In one example, there was a case of an automotive manufacturer who found that during the life-testing of a batch of airbag deployment sensors, some did not deploy correctly. The engineering team carried out an investigation and found the cause of each was down to a failure in a component within the deployment controller, which was immediately suspected as being from a defective batch, or even being counterfeit. Without traceability data being available, around 400,000 cars would have to be recalled in order to check whether they were affected, or, swap out the controller just in case. The number was decided based simply on the time range in which the faulty units were manufactured, what materials were there at the time, and the time that the materials could have continued to have been used, either side of those dates. With basic traceability and the available knowledge about which materials were used, and what other batches of products they were assigned to, the number of cars subject to recall was reduced to 15,000. In both these cases, however, the recall was a national public announcement, very damaging to the logos on the cars affected. Where exact traceability data is available, it can be used to discover patterns in the data that would indicate the cause of the defects. This may not necessarily be the material itself. In this example, with the use of exact trace data, the fault was tracked back to a specific operational process, and not in fact a specific material. All of the defective units had been through a specific process at a time immediately prior to when a faulty earth strap had been reported, and replaced, but no action had been taken to check products made during the time period for which the strap was not connected correctly. As a result, the corrective action procedure for dealing with this type of earth-strap fault was modified, and the exact quantity of 882 cars known to have been specifically affected were discretely “serviced” to replace the suspect controllers.
In a second example, random failures of a smart domestic central heating controller were being experienced in the market, increasing significantly over time. At the repair center, a commonly used IC was found to have irregular markings, with evidence that previous markings had been removed. The IC was determined to be counterfeit. Unfortunately, that same IC was in very common usage in many types of critical and non-critical products. In all, without traceability data, about one million products already in the market were at risk of failure due to the possible use of the counterfeit material. With basic traceability data, the applicable batches of materials that came from the same source were identified, which reduced the potential scope of the issue down to 50,000 products. With exact traceability, all of the counterfeit instances were found to have come from one common source material lot, and in all, represented just 3,000 products.
In both of these examples, exact traceability data, collected as part of the smart factory operation, was able to turn a potentially company-/brand-threatening issue into an unfortunate, but manageable situation. This ability is a major step forward, and is thought by many should be mandatory in the industry today. In itself, it is not a complete solution to the threat of counterfeit material ingress, as, after all, there were still the actual instances of counterfeit materials to deal with. Action taken had no effect on eliminating the risk of such occurrences happening again.
The Smart and Secure Supply Chain
In order to completely eliminate the ingress of counterfeit materials, it is necessary to strike back at the source, to bring accountability and even prosecution for those responsible. The use of exact traceability in assembly manufacturing is a very low-cost, yet definitive link between any counterfeit material found, back to the original packaging in which the material arrived at the assembly manufacturing site. This ability represents a critical enabler for the creation of an effective end-to-end secure supply chain, utilizing the latest security technologies, such as blockchain, to securely track counterfeit ingress from assembly, up through the supply chain, and by so doing, to discover the point of ingress.
Figure 2: Overview of the secure supply chain.
Any such technology needs to be available as a global standard; and to be sustainable, needs to provide benefit for each of those companies or entities that are involved.
The secure supply chain idea starts with the original material manufacturer, who creates a unique blockchain ledger for each unit of material packaging dispatched. It is probable that standard unit quantities will be packed into standard sizes of packaging, that is tamperproof, with unique labelling or other form of identification that is also tamperproof and uncopiable. Several technologies exist for this purpose. Standards are more related to the procedure and audit trail, rather than the selection of the technology itself. The blockchain will record the material package information, which should include all of the mechanical and electrical specification of the included materials—in effect, it is an electronic datasheet. Having been designed digitally, this data about the materials should be readily available without additional cost to the manufacturer.
Once shipped, distribution events are recorded into the blockchain. By its nature, no data that has been added to the blockchain can be changed or tampered with. Any damage to the secure package or label is recorded, rendering the material content insecure. Distributors may repackage materials, when the packaging is damaged or if the package contents need to be divided, but then it is the specific entity performing this action within the distributor that assumes responsibility for the contents of the new packages.
At the assembly factory, the security of the packing and identification are confirmed, and the blockchain data retrieved. As well as being able to check the path of the distribution of the materials, the assembly manufacturer also gets the digital information about the material itself, an essential constituent of smart material management.
The benefit to the assembly manufacturer is that should there be any quality or suspected counterfeit issue, the true entity responsible can be quickly and accurately identified. With this technology in place, there is a strong deterrent to those who would attempt to substitute inappropriate materials, as there is no doubt that on detection, they will be tracked and held responsible. In addition, the assembly manufacturer gains the digital specification for the materials, which previously had to be derived manually from data-sheets, being another contributor to delays for new product introduction where new materials were introduced, or, where a different material had been approved as a substitution or alternative. For the distributor, there is the return to the trusted status with the customer, as the source of counterfeit ingress can be traced through their distribution and back to the source, without suspicion.
For both assembly manufacturer and distributors, as counterfeit material ingress is ultimately eliminated, there is the reduced cost and need for testing. For the original material manufacturer, there are also benefits relating to their brand protection and trust from their customers. Though it may not be the responsibility of the material manufacturers that others may be creating counterfeits of their materials, customers will nevertheless tend to associate them and start to avoid the use of those materials, as the risk of getting an issue with a counterfeit would appear far greater. Though this is difficult to quantify, there is no doubt that such an effect will become very significant as time goes on, were nothing to be done to combat counterfeit ingress. The likelihood of a very significant event taking place related to a counterfeit material is surely just a matter of time if nothing is done.
The creation of the secure supply chain depends on the creation of industry standards and the cooperation of a significant number of companies within the industry, in the areas of material manufacturers, distributors, assembly manufacturers, digital secure infrastructure providers for blockchain, packaging and labeling specialists, and digital manufacturing execution system providers for smart factories.
There is a “perfect storm” brewing in the electronics assembly industry. Traditional tools such as MRP and ERP are unable to address material shortages in the industry, driven in no small way by the increase in flexibility of factories that is effectively reducing the lead time of material ordering. The industry as a whole is not in a position to be flexible based on the accumulation and expense of raw material stock or finished goods at the factory. Looking at the key business drivers, smart Industry 4.0 operation represents a long-term survival and growth plan for manufacturing in western countries, through the optimized control of automation for high-mix production with software. As a synergistic consequence, the secure supply chain concept is born, as direct accountability of counterfeit materials can now be tracked back to the source. With the creation of the path of accountability throughout the supply chain, counterfeit ingress can effectively be halted, as each detected attempt is likely to bring prosecution.
The secure supply chain in the modern digital smart factory is not confined to raw materials, but is also equally as applicable to sub-assemblies, internal or external—all of which combine to create the complete digital build record for every product produced. Living in a world where electronic devices and circuits interact with us in almost everything that we do today, it is essential to retain trust in our devices, in a way that is cost effective. Having the secure supply chain potential being driven by smart factory operations is a great example of the synergy of new technologies, such as IIoT, blockchain and Industry 4.0, supporting the business goals of sustainable, reliable local manufacturing.
Michael Ford is the senior director of emerging industry strategy at Aegis Software.
This technical paper was originally published in the proceedings of SMTA International 2018.