Manufacturers in every field are always busy looking for ways to improve their business operations and scale up their throughput. it’s an endless challenge.
In order to achieve this goal, they must examine each step of their current workflow and form dedicated optimization strategies. This is the crucial procedure of process optimization in manufacturing, a never-ending journey that should be reviewed time and again, and in which continuous improvement is measured in micro-steps. The road towards the ultimate goal of improving the company’s bottom line is a long one indeed.
Given the ongoing uncertainties in the global economy, supply chains and labor supply, it is no surprise to find that manufacturing executives are looking at process optimization to improve the agility of their operations, with the sector seeing the highest average value from optimization steps at 47% (compared with an industry average of 41%).
From just below 30% take up of AI and machine learning today, this figure is predicted to soar to more than 50% within just 2 years. Although uptake currently remains slow, some early-adopter manufacturers are starting to reap the significant benefits of process optimization, according to a poll showing that companies investing in Industry 4.0 technology recorded decreases in the cost of quality of 10 to 20 percent and improvements of 10 to 30 percent in throughput.According to the Manufacturing Leadership Council (MLC), 89% of respondents to a recent poll expected their company to increase its rate of adoption of disruptive technologies over the next 2 years, rising sharply from 51% just a year before, with improving operation efficiency and reducing costs the leading reasons for doing so.
When it comes to optimization of process technology though, it is important that it yields real change – and in many cases it does not. Perhaps that is because process optimization is neither an easy nor clearly defined task, it requires careful planning and preparation, and it has no endpoint.
I would define it as the continuous methodical examination of the production processes, and simultaneously, the ongoing alignment of new technologies and work methods employed to improve key parameters that result from them. Technologies and data-based insights play a key role in this, as we will soon see.
Process optimization begins by closely and honestly observing the manufacturer’s weaker points (such as material waste, bottlenecks, repeating quality issues, missing deadlines, etc.) and asking many hard questions.
It is only by identifying and analyzing these inefficiencies, and the processes that may cause them, that manufacturers will be able to set specific goals and milestones regarding material utilization and waste, machine availability levels, employee productivity, and dozens of other factors that affect the cost and production rates. Spending time identifying and analyzing problems within processes and supply chains, manufacturers can ensure they get the very best out of Industry 4.0 and AI technologies. Solutions are available that, for example, use IIoT capabilities to collect information about material, equipment and operations and delivers real-time context-driven recommendations based on this data, or track products that are time and temperature sensitive throughout production to provide predictive insights.
Real-time data and dedicated technologies play an important role in the efficiency audit process and enable companies to track inefficiencies quickly and accurately by performing an automated root cause analysis when issues arise. By using the right tools, manufacturers will be able to identify recurring errors, unproportional work division between workstations, complex production management procedures, and more. You can find out more about IIoT here.
I’ve recently explored a few use cases that may help you better understand how specific inefficiencies can be revealed. I’ve looked at bottlenecks, tool misplacements, material waste through inefficient cutting strategies, and more. Click here to read all about it.
Upon discovering and listing vulnerabilities, manufacturers can set sub-goals that address their ability to boost production or cut costs on a weekly or even daily basis, thereby breaking the overwhelming concept of production optimization into actionable steps. Industry benchmarks and real-life use cases may come in handy for setting goals. For example, we’ve recently released a specific use case that shows how optimization managed to improve profitability by 4%.
(After new optimization strategies are implemented, results are once again collected and analyzed for further optimization).
Overall, the methodology of process optimization can be broken down into these steps::
- Spotting and listing inefficiencies
- Analyzing and prioritizing areas for improvement
- Forming a detailed strategy of the optimization process
- Implementing said strategy
- Gathering and analyzing results
- Spotting and listing inefficiencies
Technology plays a key role in most of these stages.
As we’ve mentioned, adopting the right technology is crucial to the success of this mission.
The following paragraphs look at ways that industry 4.0 tools help manufacturing executives turn process optimization into a practical reality for daily business.
Along came technology – The role of current technologies in process optimization
When discussing the digital revolution in manufacturing (or any other field, for that matter), we often make the mistake of assuming that its greatest achievement is in replacing manual work with automated procedures. The fact of the matter is, the best thing about digitization and technology, in general, isn’t the elimination of repetitive manual labor and the creation of faster machines (although it is a nice advantage, I must admit); for me, it is in many ways about the ability to meticulously structure a process and continuously study and optimize it.
Revealing our manufacturing blind spots and inefficiencies is the main event, if you ask me.
Industry 4.0 technologies, and specifically, manufacturing optimization solutions, enable us to study each part of the manufacturing process in detail and to base our steps on actual facts: We can detect the exact points in time at which productivity drops; analyze all performance parameters to understand the cause for inefficiency; and finally, implement the right work methods and solutions to solve issues. Indeed, it was those manufacturers with higher digital maturity that proved themselves as having greater resilience during the coronavirus pandemic, alongside those that accelerated digitization.
Here are some concrete examples for the role of technology in setting new standards for manufacturing processes:
- Digital visualization and simulation: You must have heard the term digital twin. Creating a digital twin of the manufacturing production line can help uncover anomalies and test different solutions in a risk-free environment. This technology mimics the behavior of the real problems and solutions we are interested in examining. In the context of process optimization, think of how production processes that include running simulations ahead can be more efficient. Transparency is a must for identifying process inefficiencies, but being able to ‘see’ the entire production floor is impossible unless you have a model of sorts.
- Real-time data: Once again, we are able to study current and potential behaviors using immediate data and witness the positive (or negative) influences of each change to the process in real time. Should anything out of the ordinary happen, we will be alerted immediately instead of discovering costly moves only after the effect. Improving processes by acting upon real-time data can lead to optimized quality, minimization of unnecessary costs, and other benefits.
- Automated root-cause analysis of quality issues: In addition to real-time alerts, implementing advanced optimization solutions improves our post-event analysis capabilities and help us not only reach conclusions much faster and with greater accuracy, but also identify the faults in the processes that have led to issues in the first place, and finetune processes as well. For instance: When a throughput challenge isn’t met, and deadlines are pushed, we can use technology to identify specific processes that can be better managed – such as the time that it takes to move specific tools or parts during the production, specific workstations that lag behind, and more.
- Predictive insights: AI-based solutions allow manufacturers to simulate steps in the processes based on data that was already gathered and reflects the routine, based on calculations that take into account countless factors in and outside the factory. Predictive insights are crucial in deciding which processes are more critical to the goals and specific challenges, and should therefore be prioritized. If you want to improve things, first estimate your progress with leaving things as is…
There are many different procedures that have long been known to be inefficient, but so far we have been lacking the appropriate technology solutions needed for improving them. In the age of industry 4.0, we are witnessing change thanks to AI and IIoT tools that turn big data into a big difference.
If earlier I’ve listed abilities that technology adds to the blend, now I wish to name a few important outcomes of technologies that support process optimization and are game changes for most discrete manufacturing enterprises I know:
- Predictive quality assurance: Instead of discovering quality issues when it is already too late and delays have already occurred, money was lost and so on, AI-based IIoT solutions (I call them manufacturing optimization software) enable manufacturers to predict and alert the production managers on potential problems, for instance one that has to do with materials expiration. A good enough solution will also provide smart insights on what to do next. An optimized production cycle takes these issues into account and forms a workflow that avoids them from the get-go. Optimizing work processes based on technology means identifying where processes aren’t meticulous enough and may result in quality faults, and optimizing them by adding the right technology to the blend.
Read more on the Tremendous Power Of AI In Manufacturing Optimization and explore 5 Examples That Clear The Mist
- Prediction of late work orders: An optimized manufacturing process also includes dealing with setbacks and communicating them to employees and customers. By noticing, for instance, that a specific workstation is likely to miss a certain deadline (based on former performance parameters), managers are able to reassign the work, or (when impossible) alert customers and allow them to adjust accordingly. This creates a more responsible and responsive environment and prevents conflicts and frustration.
- Predicting and preventing bottlenecks: Once again, by getting an instant, data-based overview of the entire production line, manufacturers are able to identify potential bottlenecks or receive immediate alerts as they start. Using AI-based algorithms to prevent bottlenecks and late work orders gives the manufacturer time to readjust and, in case these issues repeat themselves, implement preventive solutions accordingly.
Process optimization is far from being a new concept. From the beginning of manufacturing history, we’ve seen workers and managers try to make the most with what they have by changing the way they work, and by defining organized processes.
Only they had far lesser tools and were able to reach results that pale in comparison to today’s advancements.
The true revolutionary force of technology makes us smarter and more informed than ever, and enables us to face our errors and correct them in time. In the AI era, where data fuels optimization, the sooner we identify what needs to be optimized, and adopt and implement these tools, the stronger our manufacturing capabilities will become.