By Eric Whitley
Digital manufacturing has created unprecedented ways to improve long-standing processes in the industry. At the core of a highly effective digital factory, you will find the integration of both virtual and physical systems working alongside one another.
While there is an extensive range of potential technologies to deploy, finding the most appropriate solution for a customer’s plant needs can be challenging. Engineering design practices can guide such decisions in identifying a practical – yet effective – approach.
Design thinking in Digital Manufacturing
Digital manufacturing can be a perfect opportunity to apply the practices of engineering design thinking. As an example, one key application of engineering design helps manufacturers avoid the overwhelming desire to blanket the shopfloor with connected technology in hopes that improvement will spring from the sheer weight of data taken by hundreds of sensors on the factory floor. This application, Smart Manufacturing software, enables engineering to capture real-time shop floor data that can then be used in the discovery and idea phases of engineering design. By using early data to pinpoint when and where the improvements and technology should be applied, the engineering design process eliminates excessive expenditure of capital and increases the likelihood of success by focusing resources instead of throwing money at blanket technology solutions.
Engineering design also comes into play at the planning and implementation phases as technology is deployed on the shop floor. As machine data collection technology becomes simple, inexpensive and easier to analyze, the planning, execution, and test phases of engineering design become quicker and repeatable. As an example, smart devices and sensor bots are becoming as simple to deploy as Smart Home devices in your house. Thus, the testing phase of technology is virtually risk-free to engineering groups that may be testing a technology hypothesis on a particular manufacturing process.
The right questions
Coming up with a solution requires asking unconventional questions, the most intriguing of which arise when uncovering new perspectives.
From a business perspective, the definition of success eventually links to realized value. A perfect world with zero errors, which requires infinite resources, is not a practical solution. The next best thing is weighing which risks are worth prioritizing to maximize the worth of any cost. Performing Design Failure Mode Effects Analysis (DFMEA) processes can help with making the decisions on focusing efforts, by identifying the consequences of particular incidents.
Of course, ingenious designs are only truly effective when implemented as intended. The involvement of the workforce becomes a differentiator in the success of an approach. Clear and open communication through routine engagements provides an avenue to exchange ideas and evaluate real-world performance.
Breaking new ground with digital capabilities
Engineering design is a prime example of how data takes the lead in making informed decisions. Older ways of gathering data involved meticulous steps: recording readings from measuring implements, connected to various types of equipment.
It is tempting to think that tapping into the Internet of Things (IoT) instantly resolves all the issues of more dated practices. While the level of interconnectedness is significantly improved, there are some things to learn – and unlearn – to adapt to newer digital capabilities. The Cloud, for example, offers virtually limitless connectivity. What is not as obvious are the impacts of such changes on the expectations of design engineers.
As available IoT technology has allowed real-time data collection, the design engineer shifts from having to validate manual data points to analyzing information from a broader perspective. Manual inspections and facility walkthroughs can now be handled with sensors and measuring implements.
Given the additional capability of interconnectedness, the features that define quality software relate to their ability to make sense of the data. The design of how systems integrate with each other also ensures that tools can maximize the collective potential. It is typical for a manufacturing facility to have several dedicated systems for maintenance management, inventory monitoring, and master data governance. Designing for the collaboration of several applications sets up the demands of a digital manufacturing facility.
Tools for innovation and improvement
Especially within the manufacturing space, opportunities to innovate continually present themselves through emerging technologies. Think about how the essential components of a traditional plant have evolved to form what we know as a smart factory.
The level of communication between devices, for example, has opened the doors for integration within various areas of the production process. Cloud-based applications and systems powered by artificial intelligence (AI) can crunch massive amounts of data into actionable insights. Conventional production techniques can then develop into more digitized methods that maximize interconnectedness and free-flowing exchange of information.
Additive manufacturing practices illustrate how incorporating design thinking can combine the best of both traditional and innovative methods. 3D printing is a relatively high-tech practice that builds upon layers of material to come up with the final product. A possible solution is to make components using additive manufacturing and forego more traditional subtractive processes such as machining. But applying a more structured process design might highlight the benefits of combining both practices to develop the most efficient way of producing a unit.
There is always the risk of being overly eager to use technologies and maybe even collecting too much information with limited analysis. It then helps to take a step back and rely on the systematic approach of asking the right questions and exploring efficient resolutions.
Best practices in design do not end with identifying a solution. The measure of success is in the continuing effort to seek ways to improve through realizing efficiencies. Engineering design practices demonstrate this feature by optimizing an already working solution.
The importance of data and what to do with data are the game-changing attributes of a digital factory. Modern ways to collect data from equipment and their interaction with the workforce redefine the limits of what a plant can do. For instance, comprehensive condition monitoring using sensors and capable maintenance software moves the needle from preventive ways of working to a more predictive approach. In itself, maintenance directly impacts the reliability of assets and the overall productivity of a plant. But an end-to-end view of things does not limit the efficiencies to just servicing parts.
Building upon an improvement, such as in the maintenance area, paves the way for further efficiencies in related steps in the process. Using the same example, imagine how a streamlined maintenance strategy can create a domino effect of improving scheduling, planning, and sourcing of parts. These can further impact the cost of inventory, working capital, and ultimately, the bottom line.
The idea of implementing design solutions, while technical in nature, lends itself to practical benefits down the line. Realizing efficiencies even in indirectly related areas maximizes the returns of a seemingly straightforward initiative.
Digital manufacturing is an all-encompassing idea that brings in some of the most advanced technologies available today. It is often a long-term initiative that transitions between legacy practices and emerging conventions. Because of the wealth of available tools for innovation, it is essential for companies to have a structured design approach to stay true to their objectives.
For over 30 years, Eric Whitley has been a noteworthy leader in the Manufacturing space. In addition to the many publications and articles Eric has written on various manufacturing topics, you may know him from his efforts leading the Total Productive Maintenance effort at Autoliv ASP or from his involvement in the Management Certification programs at The Ohio State University, where he served as an adjunct faculty member.
After an extensive career as a reliability and business improvement consultant, Eric joined L2L, where he currently serves as the Director of Smart Manufacturing. His role in this position is to help clients learn and implement L2L’s pragmatic and simple approach to corporate digital transformation.
Eric lives with his wife of 35 years in Northern Utah. When Eric is not working, he can usually be found on the water with a fishing rod in his hands.