Scheduling as a Cornerstone of the Smart Factory [Part 1]
One of the consequences of the pandemic has been an incentive to deglobalise, as regions suffered from the issues with supply chains and geopolitical dependencies. Significant delivery issues in the chip industry – and in particular wafer manufacturing – have had a negative impact on the global economy. However, onshoring this high technology industry will also bring its own challenges. Expertise and cost efficiency to name a couple. Zooming in a bit closer on so-called wafer fabs, we can distinguish two types of factories. The legacy and smaller fabs serving niche markets with older technology nodes, and the cutting-edge giga-factories, recently built or in the making. Both types have different problems to tackle, but one key component of their roadmap could be surprisingly similar.
The newest fabs have well integrated automated systems, but operating them efficiently on such a scale is a challenge of its own. The older factories have the downside of being less automated but they realise the need to become more efficient in energy consumption, labour cost and capacity utilisation. In both situations, digital transformation is coming to the rescue. Industry 4.0 is no longer a buzzword, it has become a matter of regional technological sovereignty.
The fundamental building block of Industry 4.0 is data; an asset which is present in abundance in wafer fabs. So what is preventing these factories from levelling up? The answer is simple, the solution is not: complexity. It’s an inherent part of wafer manufacturing, stemming from; increasingly high numbers of process steps, job shop factory types, re-entrant flows, product diversity, sensitivity to quality issues and so on.
The problem with complex systems is that there’s so much variability and interaction, it's hard to get actionable insights from data. Instead of accepting the stochastic and complex nature of the fab, factories can better control it by using advanced production scheduling to understand in which order lots get processed, on which tool and – the most important difference when compared with common rules-based approaches – when they get processed. To begin, this can be employed in certain bottleneck areas and then once you do it for the entire factory, you get a holistic picture of what is going to happen. Sounds great, doesn’t it? But how exactly will this benefit your fab? To explain, let’s place production scheduling in a couple of recognisable use cases.
- Lot-Tool Assignments
Wafer manufacturing has complicated recipe-tool qualification matrices within a group of tools that perform similar processes. The weaker tools can process fewer recipes than the stronger ones. We want to avoid stronger tools “stealing” lots away from the weaker tools, because it leaves fewer lots for the weaker tools to process, therefore wasting capacity. The same is true for faster and slower tools: while faster tools are preferred, pushing all the WIP through the faster tools leaves the slower tools under utilised. Advanced schedulers allow for better anticipation of incoming WIP and superior use of available capacity for weak and slow tools. The bigger and more complex the matrix grows, the harder it is to find the optimal processing of WIP. On top of the scheduling itself, mathematical programming helps to optimize lot-to-tool assignments over time. This results in a capacity booster, similar to putting a turbocharger on an engine: it’s the same engine, but with more power.
- Reducing Timelink Violations
Process steps with timelinks are common in wafer manufacturing to control the maximum amount of time a wafer spends between two or more process steps. If a timelink is violated, the wafer requires rework – or worse still, scrappage. A system that avoids timelink violations requires the ability to intelligently plan into the future. And that’s exactly what an advanced scheduler does. It has been proven to drastically reduce timelink violations, even in the most complex of scenarios.
- Improving Batching Efficiency
Batching is a complex decision making process since it involves an estimate of lot arrivals and how waiting longer trades off with running smaller batches. Predicting lot arrivals is difficult in such a complex environment, and trading off wait time against batch efficiency is even harder because the costs and gains are not always clear. Determining and automating this process is well within an advanced scheduler’s remit. Once the algorithm is tuned, it makes the most efficient decision, and perhaps even more importantly: it generates consistent output.
- Optimizing Changeover Decisions
Another use case related to the problem of lot arrivals is the problem of changeover decisions. One toolset with different machine setups can serve multiple different toolsets down the line. A bit like a waiter in a restaurant serving multiple tables. Waiters have to make sure no table is without food or drink, and to do that, they visit the tables regularly to ask for any orders. But for machines, you can’t switch the setup too often because it only increases non-productive time. Preferably, you also plan setup changeovers at a time when planned or predicted downtime for the machine occurs, to reduce downtime variability. To put it simply, it’s a decision on when to switch over from the type A process to the type B process on a tool. An advanced scheduler can solve that equation, finding the optimal point in time. Schedulers are better at this than human reasoning or rule-based logic, as solving to a time dimension is what they are designed for.
- Flow Control and Line Balance
Line balancing is – even for experienced manufacturing engineers – difficult to grasp. One can intuitively understand what it means, but how do you define “balanced” in the first place? Even if you can, it is absolutely beyond the capabilities of a human brain to manually and continuously make decisions that control it. And once it’s out of balance, to recover it. Again, considering the time dimension is a crucial aspect of what advanced schedulers offer, which enables them to recover faster from unforeseen circumstances and maintain better risk-control for generating continuous output.
- Operator Task Lists
As opposed to dispatch lists that only tell the order in which to process lots, advanced schedulers can also tell when a lot is supposed to start and finish processing on a tool. Combine that information with which operators are serving which tools, and you can move away from tool-centric dispatch lists towards operator-centric task lists. With a handheld device, that could even allow you to send push notifications when urgent intervention is needed. It can reduce idle time on tools that have no available operator. Even more so, it can allow for an entire rethink of the workflows operators are used to.
So far in this blog, we’ve focused on scheduling use cases where lots are scheduled on tools, leading to higher throughput on tools, toolsets or the entire factory. All these use cases can also be addressed by improving some rule-based dispatching strategies, but what advanced scheduling offers is the ability to optimize for future decisions rather than just real-time. With that comes better visibility on what will happen in the factory, and it also leaves opportunities for re-organising workflow and freeing up resources. In part 2 of this blog, we’ll begin to look at the future and what could happen when we integrate even further. Enter, Industry 5.0.
Author: Ben Van Damme, Industrial Engineer and Business Consultant
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Join Felipe as he shares his typical day at Flexciton, highlights the most rewarding aspects of his role and offers valuable career advice in this month’s edition of The Flex Factor.
Join Felipe as he shares his typical day at Flexciton, highlights the most rewarding aspects of his role and offers valuable career advice in this month’s edition of The Flex Factor.
Tell us what you do at Flexciton?
I’m an Optimization Engineer, which deals with mathematical optimization and software engineering. At work, you’ll find me working on new components to our optimization model, thinking of and/or implementing improvements and fixing some bugs that appear from time to time. In general, it involves understanding the semiconductor manufacturing process and writing and maintaining production code to incorporate mathematical optimization into our software so that we can deliver the best schedules for our clients.
What does a typical day look like for you at Flexciton?
Treat myself with a cappuccino before anything else (I always regret it at the end of the month, it’s an expensive addiction), then I’m ready for our daily stand-up. That’s when the team meets to discuss priorities, status of ongoing work, if there are any blockers and how to sort them. After that, it is a mix of coding (new feature, improvement, bug fix, etc), discussing the design of a new implementation with another team member and doing code reviews. From time to time, I also present something in our knowledge transfer sessions and have also been onboarding new starters on the topic of optimization.
What do you enjoy most about your role?
We deal with very complex problems, so it really is a mix of challenging and exciting work, all done within a friendly and supportive environment! Learning a lot and having fun ends up being a byproduct.
If you could summarise working at Flexciton in 3 words, what would they be?
Interesting, fun, challenging.
If you could give one piece of career advice to someone, what would it be?
Tasks that initially seem daunting and make you doubt your knowledge and expertise are often the ones that will make you grow.
Tell us about your best memory at Flexciton?
I guess I’ll have to pick more than one here. It may sound cliche or cringe, but the first day was one of my best memories. Moving abroad for a new job and to do something for the first time is quite an intimidating experience. So it was a great feeling when I had a warm welcome on my first day. Everyone was friendly, open and looked super smart.
Apart from that:
- Team Trip to Albufeira.
- Visiting a wafer fab of one of our clients and watching the manufacturing process that I’d been discussing and modelling every day, but had never seen in real life until then.
- And of course, when I won the office contest for best Halloween costume although I was the only person wearing one…
Are you interested in working for Flexciton? Head over to our careers page to check our current vacancies or connect with us.
Jamie shares his thoughts on the UK’s £1bn semiconductor strategy, why he thinks there's untapped potential with disruptive technology, and how the UK’s abundant talent pool could be the key for our growth in the global industry.
A missed opportunity
Rishi Sunak’s recent unveiling of the UK’s £1bn ($1.3bn) semiconductor strategy was always bound to provoke a reaction from critics. In an attempt to improve research and development and bolster international cooperation, the UK announced it will partner with Japan as part of its strategy. The aim of this collaboration is to foster knowledge sharing, increase expertise, and mitigate supply chain risks. The obscurity of the government’s strategy – as well as the delay from the original announcement date of autumn last year – tells me that they are very much still figuring this out. It appears the next step is to employ an advisory panel to help decipher what the actual actions will be before autumn of this year. A full year after the original date. Fundamentally, though, I think the UK has got this one right. It’s too late for us to start throwing huge amounts of money at building fabs, since we simply don’t have the capital or the resources to create our own security of supply. Instead, it’s much more beneficial for us to focus on specialisms that could make us globally relevant to the supply chain. However, what I’m less convinced about, is the government's understanding of the areas of expertise we already possess.
Let’s look at where the UK is particularly strong, as with a limited budget, focusing on creating that specialism makes sense. The obvious one here is chip design, which was detailed in the strategy unveiling last week. ARM has been at the forefront of this market for many years and, along with the spin-offs coming from the University of Cambridge, it’s a sector where the UK could be considered at the forefront. Other nations, such as China, have been offering a greater deal of support to their design companies for many years now, so it makes sense to match them if we want to remain competitive. Another obvious one is innovative new software and technology, which is not detailed in the government’s strategy. The skills shortage means that emerging technology like artificial intelligence will soon have to play a more central role in wafer fabs as they transition towards smart factories. We have a faster growing tech hub here than anywhere else in Europe, putting the UK in a prime position to establish itself as a global leader in smart manufacturing technology. Yet even with this opportunity sitting directly under their noses, I don't think the government has yet realised its potential.
For those who are unfamiliar, smart manufacturing refers to the integration of advanced technologies like artificial intelligence and automation into manufacturing processes and systems. It has the potential to transform traditional factories into intelligent, data-driven environments that enable much higher levels of efficiency with fewer skilled people required. Now, smart manufacturing is still very much an emerging field. At this point, only a handful of leading-edge manufacturers are concerned with it and even fewer have begun actually adopting it. But the current challenges faced by the industry, such as the skills shortage, are making its importance ever-more apparent.
The talent pool on our doorstep
The government seems to think that the best way to solve the skills shortage is to invest in the education of relevant fields. There’s no doubt that this will help somewhat, but it’s going to take a very long time. What they fail to take into consideration is that working in semiconductors used to be one of the most exciting prospects for skilled engineers. In some cases, it still may be, but now it has to compete with working for companies like Google or Apple. So as the demand for people rises with the construction of new fabs and tech companies continue to attract graduates, it’s going to be a challenge to attract the level of talent the industry needs in the time it needs it. As many of the vanguard wafer fabs are realising, a quicker and more realistic approach to solving the skills shortage is implementing smart manufacturing technologies.
The key component of smart manufacturing is software. The tech startup ecosystem here in London has a value of over £250bn ($314bn), which is over triple that of the next largest in Europe. The UK government is well aware that novel technology is a domain that the UK – and London in particular – is well positioned to become a leader in. But it seems they haven’t yet figured out that our strengths in this area could be applied to our semiconductor strategy.
The talent pool of software and data engineers we have access to here in London rivals that of anywhere else in the world. It’s one of the main drivers behind the capital’s success as a tech hub. With support from the government, this abundance of skilled engineers and software companies could be harnessed to create a specialism in smart manufacturing technology. Many of the disruptive technologies that will be used in wafer fabs over the next 20 years will come from outside of the traditional semiconductor supply chain, many of which could be already operating in London today. All of this means that the foundations for this new specialisation are already laid, giving the UK a head start to become a global leader in smart manufacturing.
To conclude, the UK's semiconductor strategy reveals both missed opportunities and potential for growth. While the government's collaboration with Japan and investments in chip design are steps in the right direction, our potential with emerging technologies seems overlooked. The UK's thriving tech hub, particularly in London, presents a pool of software companies and skilled engineers that could be leveraged to establish the country as a leader in smart manufacturing technology. By embracing smart manufacturing, the UK can help address the skills shortage, drive efficiency in the industry, and secure a position of relevance in the global semiconductor supply chain. However, it remains crucial for the government to recognise and harness these existing strengths to fully realise the potential for growth and competitiveness in the semiconductor industry.
Author: Jamie Potter, CEO and Cofounder
In Part 2 of this blog, Ben Van Damme delves further into the potential of advanced optimization-based scheduling for wafer fabs in the not too distant future.
In Part 1 of this blog, we focused on use cases where lots are scheduled on tools and how advanced scheduling gives users the ability to optimize for future decisions as well as real-time. When we say "advanced," we are referring to autonomous, optimization-based solutions. Our emphasis was primarily on how scheduling can enhance productivity in a fab today. In Part 2, however, we’ll delve further into its potential for fabs in the not too distant future.
Previously, I discussed how task lists are typically associated with human workers. However, it is worth noting that task lists can also be applied to automated systems such as automated guided vehicles (AGVs) and automated material handling systems (AMHS) with the use of an advanced scheduler. With task lists, an advanced scheduler can not only determine which lot is assigned to which tool and when, but also which operator – or robot – will be serving the tool. There’s a whole set of new opportunities that arise with that, as humans and robots, just like tools, have a limited capacity that can be optimally utilised. It’s clear then that the possibilities for advanced scheduling go beyond the stand-alone Industry 4.0 applications and have the potential to integrate vast amounts of fab data into a holistic system.
One of the use cases of such a holistic system is described later on in this blog as a type of ‘digital twin’, but the capabilities of an advanced scheduling system go beyond that. With a digital twin concept, the human is still very much inside the cockpit. An advanced scheduling system, on the other hand, is more like an autopilot, augmenting the capabilities of other systems and taking control of manufacturing decisions when necessary. As such, advanced scheduling is a cornerstone of the so-called ‘smart factory’. Let’s try to understand the huge array of benefits it can bring. First, we’ll cover a couple of use cases that can benefit the manufacturers. Second, we’ll share some thoughts on how advanced scheduling aligns with the idea behind Industry 5.0 and how the technology can serve ourselves as humans.
- WIP Transport Scheduling
Once a lot is intelligently scheduled, we know when to process it and on which tool. The lot can be transported to that tool’s specific staging rack just before it gets processed. It enables fabs to eliminate waste by optimizing transport capacity, which removes the likelihood of a lot being transported at half capacity only for it to wait in queue. Transport scheduling also enables splitting logistics and processing workflows; some workers focus on keeping the tools running, others focus on getting the lots to the tools in time. Multi-cleanroom fabs will make better use of their capacity in areas that for logistical reasons are not preferred. Which means no more remote idle machines waiting for a lot that doesn’t arrive.
- Dynamic Capacity Models
With better control of lot processing, intra-fab logistics, and workforce planning, we get a more realistic view on the true capacity of a factory. We call it a dynamic capacity model, resembling the idea of a digital twin of a production plant. A dynamic capacity model better reflects the current state, loading and dynamics in a factory, as opposed to the static capacity models commonly used. Until now in wafer fabs, dynamic capacity models have at best been approximated by what-if scenarios in simulation models, but the potential goes beyond that. When playing around with different scenarios – e.g. when to plan maintenance or shutdowns, which availability increase has the most impact on the whole factory, what’s the effect of frequent product mix changes, what lead times to expect and so on – it should allow factories to better judge the impact of their decisions. Optimization can even help by not only interpreting the outcome, but suggesting the best decision for a fab’s goals.
- Multi-factory Models & Supply Chain Planning
Eventually, dynamic capacity models could scale to corporate level in multi-factory models. Further up, these models could feed into supply chain planning software. During the supply chain crisis, it was striking to see how disconnected sales and operations planning cycles in semiconductors were from the actual operational challenges of factories. Part of it was because of models that don’t properly comprehend the actual situation the factory was in. Fabs were treated as black boxes with a simple input and output signal, but just because you have promised your customers a sooner delivery date, it doesn’t mean it will happen automatically. You need a driver towards that new target, and that’s where advanced scheduling software helps, by optimizing towards shorter lead times. Its integration into dynamic capacity models and supply chain planning software would lead to more reliable input for inventory and order fulfilment optimization engines. This translates into lower inventory costs and better delivery performance of a company.
Eventually, we want technology to help us overcome the challenges we face as humans. From what has been written so far, this blog might give the impression that this technology is primarily serving profitability. But becoming a smart factory doesn’t necessarily contradict with a human-centric approach. Industry 5.0 is the theoretical concept that’s been introduced for that. It counters the illusion that the future of manufacturing is one in which humans play a minor role. Instead, we should embrace both the capabilities of new technologies, as well as those of humans and find synergies to make the best of both worlds. While Industry 4.0 can do a great job in automating repetitive tasks or making sense out of masses of data, humans have the advantage of better interpretation of context, require fewer data points to understand, and can make value trade-offs. Humans will not miraculously disappear from the factory shop floor, so we’ll benefit from thinking about how these advanced technologies can harmoniously coexist with people and yield mutually beneficial outcomes.
- Staff Workload Balancing
The obvious fear with advanced scheduling is that operators and technicians will turn into de facto robots, where only adherence is of importance when aiming to get more out of the workforce. Let’s turn that thought up-side-down: what if the same work could be better distributed amongst the team by offloading peaks to underloaded co-workers? Advanced scheduling can better predict and hence properly distribute work aligned with an individual's availability and level of training. Also the workflow itself - the number and order of actions to perform - can be streamlined to lower physical and mental workload.
- Training, Evaluation & Individual Productivity
With detailed production schedules, any lack of staff or training becomes directly visible and quantifiable. Hiring and training programs could become more timely and data-driven, just as annual evaluations will become less subject to biases of the manager. Even on-the-spot productivity can be monitored and optimised. This may sound like a “Big Brother” concept, but compare it with the advancement of sports analytics and medicine in the last decade. Professional athletes don’t complain about data integrity and privacy issues, because (1) it’s part of their job and (2) it helps them in what they want to achieve. If athletes ignore their data, they simply don’t reach the top anymore. Similarly, the fourth and fifth industrial revolution will bring staffing to higher levels of productivity, not because they are squeezed out more, but because the data will reveal where there’s room for improvement or when a red line is about to be crossed.
Given the increasing scale and complexity described above, significant computational power and data storage capabilities will be necessary. This makes it likely that cloud-based technology will be adopted to facilitate the transition to smart factories. Although many fabs are currently far from achieving smart factory status, it is clear that the industry is moving in this direction. Therefore, factory managers must acknowledge that the transition to becoming a smart factory is not just a concern for the future and must be implemented within a realistic timeframe. The foundations for this transition, including employee readiness, are already being established today. And given the use cases discussed, let there be no doubt that advanced scheduling will play an integral part in the next generation of wafer fabs.
Author: Ben Van Damme, Industrial Engineer and Business Consultant