The Global Manufacturing Execution System (MES) Software Market is on a steady and significant growth path. Anticipated to expand at a commendable annual growth rate of 11.6% from 2021 to 2028, the market holds a promising future. Grand View Research (2021) estimates it could reach a whopping USD 23.16 billion by 2028. This surge isn't without reason - it's being driven by the growing adoption of Industry 4.0 practices and the increasing need for regulatory compliance across a range of sectors.
The world of MES software is a highly competitive landscape where vendors are constantly fighting for the attention of potential customers and looking for ways to retain their existing ones. Falling behind the competition can be costly, and in a market where everyone is racing to innovate and stay ahead, the fear of missing out on new opportunities is all too real. One clear opportunity for differentiation lies in integrating artificial intelligence (AI) components into MES software.
In this article, we will discuss the main challenges for MES providers and give an overview of key differentiator factors for MES software, as well as the main bottlenecks of the MES planning feature.
In the end, you will be inspired by a real-world example of how a combination of a Manufacturing Execution System (MES) and AI has accelerated production processes in manufacturing by up to 10% and reduced planning time by up to 50%. You will leave with a clear understanding of how AI can help MES vendors stand out in a crowded marketplace.
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Let's face it, adopting MES can be a challenge for many manufacturing companies as such software can be expensive, difficult to implement, and an utter headache to integrate. And all these obstacles can undermine the attractive benefits provided by MES software vendors. Some of the common difficulties include:
Technology and Infrastructure: The complexity and cost of technology and infrastructure required for MES implementation can pose a considerable challenge. A 2019 McKinsey report highlights that only 30% of the surveyed organizations managed to successfully scale their Industry 4.0 initiatives, which include MES implementation. This limited success rate may be due to factors such as high hardware and software costs, complexities in system configuration, and the technical expertise necessary for the efficient implementation and operation of an MES.
Business Culture and Change Management: Adapting to new business cultures and changing existing business processes to accommodate MES can be difficult. As an example, a McKinsey study from 2019 found that 70% of large-scale change programs don't reach their stated goals. This is often due to resistance from employees accustomed to existing processes and lacking adequate management support for change. Implementing MES in a company for the first time often requires significant alterations in workflow and processes, and failure to manage this change effectively can lead to low adoption and ineffective use of the system.
Data and Integration: Ensuring that an MES integrates seamlessly with existing systems, and managing the vast amounts of data it generates, can prove challenging. A PwC report emphasizes that data integration is among the top challenges faced during the implementation of Industry 4.0 initiatives, including MES adoption. For instance, a need for an MES to communicate with a diverse range of systems, from ERP (Enterprise Resource Planning) to equipment on the factory floor. Guaranteeing smooth data flow between these systems, while managing and analyzing the massive amount of data produced by the MES, can be an overwhelming task for many organizations
But even if these challenges are overcome, we still need to address the measurement issues and ask if the effort is worth applying as the ROI of MES implementation remains unclear for many manufacturing companies. One of the predominant reasons is that most manufacturers do not fully exploit the potential of the data they collect. Many of them are required by law to ensure product traceability, which means collecting a wealth of data from the shop floor through MES systems. But most companies often struggle to convert massive amounts of data into useful and actionable information. The majority of companies never get to the point of transforming data sets into actionable insights, while the Accenture study identified eliminating data noise as one of the key drivers of success.
Companies that strategically scale AI and can get a handle on their data have nearly twice the success rate and three times the return on AI investments as companies that only sporadically test a few AI solutions. We can only imagine how far behind companies that don't invest in AI are. This lack of clear value and unreachable ROI can lead to skepticism and hesitation among potential customers of MES providers.
“Companies that strategically scale AI, including effectively managing their data, achieve nearly 2x success rate and 3x AI ROI”
Navigating through the stormy seas of common objections, many MES providers proudly present their unique selling points: enhanced decision-making, in-depth analytics, comprehensive reporting, and stringent quality control. But there's more to the story. They spotlight on the power of having a crystal-clear view of the production processes, achieved through traceability and real-time planning.
Indeed, real-time planning should serve as the GPS in the vast ocean of manufacturing. It not only charts the course of action but updates it instantaneously, adapting to the changing production. Without it, you're adrift, at the mercy of unseen currents and unexpected storms. With it, you're the master of your journey, ensuring smooth sailing, timely results, and meeting the plant’s production KPIs.
However, these claims often fail to translate into significant improvements in operational performance and bottom-line results. While the planning features in MES solutions are meant to become a game-changer for their customers, in fact, they are underperforming.
While MES solutions promise to revolutionize planning, they often miss the mark. Picture this: manufacturing organizations grappling with manual data entry, inflexible planning structures, and even shying away from barcode tracking. As a result, they find themselves going back to old-fashioned paper processes, sabotaging the potential for efficiency gains.
Traditional MES planning bottlenecks that MES users face include:
These bottlenecks can lead to a range of negative consequences, including missed deadlines, difficulties in meeting regulations, poor product quality, reputational risks, and profit losses.
Artificial Intelligence offers a promising solution to these challenges. By leveraging AI, MES software providers can deliver not just data, but valuable information to their users. AI-driven fine planning provides decision-makers with actionable insights and recommendations, enabling more informed and effective decision-making in manufacturing. According to an Accenture study, AI can improve decision-making quality by up to 40%. Moreover, AI-powered production planning ensures:
In summary, an AI-powered MES can automatically orchestrate all complicated production processes, ensuring that all production goals of their customers are met efficiently and effectively.
Let's look at a real-world example: Vacom, a prominent German company in the field of vacuum application components and measurement technology, had a dramatic challenge with manual production planning. In addition to offering standard solutions, this company focuses on the production of customized components.
Download the entire case study to know more about the successful implementation of AI into MES.
The manual planning of their production process was not sustainable. Even with five employees focusing solely on the task, managing and tracking approximately 2,000 production orders per month proved overwhelming. As a result, delivery times were approximate at best, leading to regular problems meeting deadlines.
The solution was found in an Advanced Planning and Scheduling System (APS) integrated into their MES from MPDV, which employed KAYROS, an AI technology developed by Vernaio. Post the initial rough planning by SAP, the AI took over the final planning of all processes, dynamically adapting to unforeseen events like personnel or machine failures by continuously rescheduling the production process.
In practice, all production orders are pooled together each night. The AI then performs five optimization runs to bring all operations into proportion. After the initial optimization, the AI performs additional optimization rounds - all in the background, and then comes up with the schedule for each machine (which previously was done by men).
The AI can also immediately react to changes, like updates from the customer or any production issue, and automatically rebuild tasks sequence on the shop floor so that the whole production would still operate in an optimal manner just within minutes. In essence, the AI assigns each machine an individual task considering legal regulations, production procedures, and KPIs.
As Kevin Mahler, the COO of the company, mentioned, now all the decisions on the plant floor are made by AI, leaving no room for hesitations or human error, which transformed their plant into Smart Factory. This automated approach saves time for the team, increases the machine put through along with the OEE, and grants the management the full and truly objective view of the production processes.
Smart Factory with Kayros streamlined efficiency and ensured on-time delivery. Like this we achieved 10% productivity boost and 50% time savings.
The impact? A significant 25 percent increase in productivity, thanks to the complete automation and optimization of production planning processes with AI and MES. At least 10 percent of this increase is directly attributable to improved planning with AI through Kayros. Prior to AI integration, up to 80% of time was spent on planning, which was suboptimal, while AI adoption resulted in 50% of time being freed up. The AI solution streamlined efficiency and freed up staff allowing them to focus on other value-creating activities of production while ensuring timely delivery.
The world of Manufacturing Execution Systems is filled with opportunities and challenges, making it an exciting landscape for innovation. The journey to capitalize on these opportunities is riddled with challenges. Among these, the elusive ROI stands as the most formidable roadblock on the path to true differentiation for MES software providers. The inherent complexities of technology, infrastructure, and data integration, coupled with resistance to change, further complicate the process of delivering clear value. Furthermore, conventional planning often translates into a cumbersome process that is unable to keep pace with the evolving manufacturing landscape.
To MES software providers aiming to stand out in this dynamic market, AI integration might just be the game-changing approach. The race to differentiate is on, and integrating AI components into MES solutions might be the winning stride. A vital takeaway is that moving forward with Industry 4.0 requires not only the collection of vast amounts of data but the intelligent utilization of it.
As we have seen, the strategic investment in AI technologies and the Smart Factory approach, implying integration of an MES, Advanced Planning System, and AI engines are just handy. This combination allows manufacturers to increase their efficiency by up to 25% and overcome the drawbacks of conventional MES planning.
But as we move forward, let's continue to ask ourselves: How can we better leverage AI to enhance MES? How will the landscape evolve as more vendors adopt AI-driven MES?
