Part 1: Overcoming Challenges of Legacy Manufacturing Systems
Moving to Industry 4.0 can seem daunting for many manufacturing companies. This involves moving away from decades-old legacy systems that are not only highly complex but also essential to day-to-day business operations. Reconfiguring, modifying or replacing major applications can be tedious and costly, as well as disrupting business operations enormously.
However, it is possible for enterprises to upgrade legacy applications and IT infrastructure to achieve the benefits of Industry 4.0, incrementally and cost-sensitively. In the first installment of our series on digital manufacturing, we explore some of the techniques deployed by A&M experts to provide quick solutions to real customer problems.
1. Adopt robotic process automation (RPA) for greater efficiency and better understanding of data
In a shop floor environment, operators need to periodically collect data for time logging, quality checks, or productivity reports, among other compliance and business intelligence needs.
This information is often locked away in disparate legacy systems, with little or no integration between them. This makes data collection and analysis too dependent on manual labor, and therefore time-consuming and expensive.
RPA technology can bring speed and efficiency to these repetitive, labor-intensive tasks. Unlike humans, software robots can work on different systems to process and analyze large volumes of data consistently and without error.
Above all, these tools enable rapid deployment and – contrary to popular perception – have reasonable implementation costs. Not only can they be integrated into existing legacy systems without expensive software development, but measurable results can be achieved within months of implementation.
The main use cases for RPA in industrial environments vary from back-office processes (invoice processing, inventory management) to basic manufacturing operations (recording and processing of data associated with quality, maintenance, regulatory compliance or forecasting activities, etc.)
RPA can also be combined with other technologies such as artificial intelligence (AI) and advanced analytics to identify patterns in data and provide future state insights and predictions.
For example, information about the performance of production machinery can help engineers optimize their maintenance plans, take action before breakdowns occur and avoid unplanned downtime, which can cost industrial and manufacturing companies hundreds of millions of dollars a year. Centralized data is also beneficial for improvement initiatives, as it allows manufacturing operators to quickly spot deviations in processes and results across production lines, sites, divisions, and locations.
Based on A&M’s recent work with industrial customers, the benefits of RPA and other intelligent automation processes include a 30% to 40% improvement in average labor productivity, as well as a reduction 10% to 20% in machine downtime and overall maintenance costs.
While investing in RPA has been proven to yield significant ROI improvements, businesses also need to be realistic about the need for constant upgrading and maintenance as industry processes evolve. and that new products are added to the workshop. Therefore, we estimate that 15% of the initial gains can be absorbed by the annual recurring costs required to update and maintain relevant RPA.
2. Break down data silos
In manufacturing and industrial environments heavily dependent on legacy systems, engineers and administrators can spend a significant portion of their working hours searching for information in different databases and systems that are poorly integrated and often isolated from each other.
A variety of modern digital capabilities can help businesses break down these data silos, allowing them to collect and make sense of proprietary operational data like never before. One technique is data virtualization, which allows organizations to retrieve and manipulate data from heterogeneous sources without replication, that is, without moving the data to a physical repository. It does this by adding a virtual layer on top of the legacy data source (also known as “digital decoupling”), allowing data users to access it in real time, regardless of its format or location.
This approach does not require large infrastructure and excessive implementation costs, providing a cheaper and faster alternative to data warehousing. There is also the benefit of stronger security and more consistent data governance resulting from the centralized data architecture, in addition to faster flexibility, scalability, and deployment.
A data governance improvement strategy should be implemented alongside a business process integration approach.
It’s essential to focus on the architecture of the application, including how critical systems are integrated and how common data is shared between them. Among the multiple tools and methodologies designed to improve application integration, the use of application programming interfaces (APIs) is a common approach. APIs can be used to define the most critical applications in a large portfolio of legacy applications and ensure that they communicate with each other. This can be used to enable process efficiency and consistency, as well as to improve data quality and overall process efficiency.
3. Embrace hybrid cloud to enable agile innovation
Cloud-based computing is a key enabler of Industry 4.0 and 5.0 technologies. However, many manufacturing managers may be skeptical of moving critical operational data to third-party cloud service providers due to security, latency, or resiliency issues.
A common approach for manufacturing companies facing this dilemma is to mix on-premises, private cloud, and public cloud services with orchestration between them, a so-called hybrid approach. It provides the agility and flexibility to innovate in a legacy environment, without compromising quality or security.
This approach also allows for faster deployment in a large multi-site or multi-country scenario, as changes to newly introduced cloud services in a legacy environment can be quickly deployed to other factories and countries.
This can be especially useful for companies looking to modernize their IT stack in line with Industry 4.0 solutions. For example, if a new application is being tested, public cloud resources can be allocated to test the solution, and only rolled into the portfolio if and when the technology is proven.
Cloud-based systems provide manufacturing companies with faster upgrades, ensuring their software is protected against security vulnerabilities and automatically updated with new features. Since data is stored on offsite servers with strict security standards in place and multiple layers of redundancy, businesses can expect stability issues to be less frequent compared to onsite infrastructure .
Stay tuned for more on Workshop Data Analytics, Digital Twin and Cyber Risk Mitigation
This is the first part of our series of articles on digital techniques that help companies reduce costs and improve efficiency in industrial environments. The following articles will cover workshop data analytics, digital twin technology, intelligent automation, and cyber risk mitigation.
How can A&M help you?
A&M has developed a comprehensive digital factory diagnostics model based on three key pillars: leadership, technology and business value. As part of this work, we conducted proof-of-concept exercises and executed pilot implementations that include large-scale, multi-site deployments of the digital techniques highlighted in this article.
Our team of experienced operators and data scientists are equipped to help companies not only generate insights from existing data, but also assess, prioritize and deploy solutions at scale to achieve maximum return on investment, as quickly as possible. We work with our clients to build and train great teams, creating strong business processes and decision making that complement new digital and data tools.
Contact us today to learn how A&M can adapt these ideas to your business goals and deliver tangible, lasting results.
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