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Putting Machinery on a Strong Fitness Plan Starts with Real-Time Monitoring

Louis Columbus
By Louis Columbus Principal, DELMIAWORKS
By Michael Buchli Dassault Systèmes

Having a plan for maintaining and improving the performance and reliability of every machine on a shop floor is vital to manufacturing operations. Reliable machines make short-notice production runs possible. And the more flexible manufacturers are, the more new customers they’ll attract.

The foundation of the most effective strategies for getting higher levels of shop floor productivity starts with real-time monitoring. Having a contextually rich, real-time data stream from every machine on the shop floor is invaluable for improving every production run and enabling design to manufacturing to achieve its full potential. This article examines:

  • the role of real-time monitoring in defining a machine’s baseline performance,
  • how real-time monitoring helps design-to-manufacturing (DTM) strategies to succeed,
  • the benefits of using real-time monitoring to fine-tune your machinery fitness plan,
  • how to get started in preparing a machinery fitness plan, and
  • strategies for aligning machinery with an agile design-to-manufacturing process.

Defining Baseline Performance

Strong fitness plans that deliver lasting change start with a true baseline of performance, and the same holds true for anyone starting to exercise as it is for machinery across a shop floor. In order to create fitness plans for their machinery that drive results, manufacturers rely on real-time monitoring for the baseline data they need. By making real-time monitoring an integral part of every production run, they’re making permanent improvements the new normal.

Two recent surveys conducted by Decision Analyst in conjunction with IQMS/Dassault Systèmes illustrate how manufacturers now rely on real-time monitoring to improve shop floor productivity including establishing machinery performance baselines that are foundation of creating individualized machinery fitness plans.

Notably, Decision Analyst’s survey of 150 North American manufacturers in 2019 revealed that 82% of manufacturing respondents are putting a top priority on upgrading existing machines or purchasing new machinery to gain insights from real-time monitoring. These companies are replacing fully depreciated production machinery with state-of-the-art smart, connected machines that can self-diagnose their condition and report problems—providing the real-time data in the context of their operating conditions that is invaluable in fine-tuning fitness plans. Additionally, to optimize their real-time monitoring, 38% of manufacturers surveyed are investing in manufacturing execution system (MES) software, as seen in Figure 1, immediately below.


Meanwhile, in Decision Analyst’s survey from late 2018, 81% of all manufacturers stated that real-time monitoring was improving their business. And in process-intensive industries, particularly plastics manufacturing, 87% of manufacturers reported that real-time monitoring as being essential to their operations. Overall, 63% of manufacturers anticipated that they would be able to better track each machine’s individualized fitness through real-time monitoring, as review utilization rates by type of production run, scrap, and downtime reporting, as illustrated in Figure 2, immediately below.


Helping Design-to-Manufacturing Succeed

Real-time monitoring is essential for synchronizing the diverse base of manufacturing systems and processes needed to create a single design-to-manufacturing environment where designers, engineers, quality management and production teams can collaborate together. Design to manufacturing is predicated on an integrating engineering, quality and manufacturing teams on the same product data model. Real-time monitoring from production machinery contributes to this data model by providing vital feedback to every team regarding the manufacturability, quality and scale of every new product produced. It’s an essential feedback loop for everyone in the design-to-manufacturing process, directly contributes to extending the useful life of machinery on the shop floor.

Designers, engineers, quality management and production teams gain valuable insights into how changing a product model impacts product machinery’s efficiency and reliability including mean time between failures (MTBF). Design-to-manufacturing teams take a more agile, iterative approach to creating new products fueled by the insights they gain from real-time monitoring data. Most important is real-time monitoring’s contribution to enabling the diverse members of these teams synchronized on a common product model. Taking a more collaborative approach to creating new products predicated on design to manufacturing improves both product quality and profitability by reducing errors in production while increasing yield rates.

Fine-Tuning a Machinery Fitness Plan

There are three main reasons why a fitness plan predicated on real-time monitoring is a great place to start putting together a design-to-manufacturing strategy that sets the foundation for solid revenue growth.

Create prototypes faster based on initial model designs while evaluating their quality and production scale based on real-time monitoring feedback. For instance, a plastics manufacturer that specializes in marine products relies on design to manufacturing to define the initial product model then produce prototypes in a matter of hours using a high-speed 3D printer combined with a stereolithography-based imaging device. Once product model prototypes are approved by customers, the same model is produced using computer-aided manufacturing (CAM) software to create the initial product mold. From there, the production process begins. Digitally creating and testing the products’ quality and durability first saves the customer and marine products manufacturer thousands of hours and dollars a year. It demonstrates how a design-to-manufacturing approach catches problems earlier and gets new products to market sooner.

Help build demand for new products before they ship by taking a design-to-manufacturing approach to production in which real-time data of every machine helps production planners define the optimal workflow for every new product. For example, a leading provider of carbon fiber-based OEM products for the automotive, consumer packaged goods, and plastics process manufacturing industries first creates a model of customers’ prototypes, providing rendered images of new products well ahead of shipment dates. Customers use the rendered images to plan their marketing and selling campaigns, assemble sales enablement materials, and define packaging. Here, design to manufacturing makes it possible to deliver more value to OEM customers and help them succeed in selling their new products.

Provide customers more options with their baseline product models through improved configure-price-quote (CPQ) and product configuration strategies. Here, real-time monitoring provides feedback to the design to manufacturing teams regarding which configurable products are the most efficient, producible and profitable to build. A case in point is BMW’s highly successful approach to selling customized Mini Coopers using product configurators online and in dealerships, supported by model-based approach to manufacturing. CPQ and product configuration excel when all systems supporting these strategies are synchronized and running a common cadence. Design to manufacturing makes that happen. And that’s great news for manufacturers who can increase the utilization rates of production centers by producing more customized—and higher margin—products for customer than before.

Preparing a Machinery Fitness Plan

To put together a successful growth strategy, manufacturers must tie it back to a fitness plan for every machine tool. Not only will this extend their useful lives, the additional data on the machine health will also improve production scheduling. Assigning the most qualified technicians to the best possible combination of machines for a specific production run is greatly simplified when each machine has a consistently high level of production fitness.

Just like someone who joins a gym to get in better shape, millions of manufacturers today have the beginnings of strong fitness plans for their machinery. The challenge is putting them into action and getting results. The following four steps are a great way to start:

Capture baseline data for every machine across several shifts to check for any noticeable, easily-defined variation in output. Creating a dataset of each machine’s performance across the shop floor is the starting point for every individualized machine fitness plan.

Choose an initial set of metrics that every machine is capable of reporting today to complete the baseline comparison. Every machine can be analyzed on four metrics: cycle times, set-up times, scrap/rework rates, and yields. Differences between machines will show up immediately. Knowing how well each machine performs against these four criteria provides invaluable insight into how its useful life can be extended.

Identify the most and least in-shape machinery by analyzing the baseline data and indexing machines’ prior activity to customer returns and quality problems. The machinery responsible for the highest percentage of customer returns and quality problems are often the same machines that show abnormally high rates of wear and tear. Checking to make sure their mean time to repair (MTTR) and MTBF estimates are accurate is a prerequisite to prolonging the life of the machine and increasing product quality and yield rates.

Combine real-time monitoring with machinery upgrades to uncover how production sequencing impacts machinery reliability and performance over time. Knowing why certain machines are starting to fail may have more to do with their relative position in a production workflow than initially may be apparent. That’s why real-time monitoring combined with the latest upgrades to smart, connected machinery make sense. Together, those steps remove two potentially large sources of variation from understanding how to prolong a machines’ useful life.

Aligning Machinery with DTM

When teams capitalize on the higher performance and scale of machinery that’s being managed to a more rigorous, thorough fitness plan, they are positioned to take on more ambitious design-to-manufacturing projects. Knowing the scale, speed and reliability of every machine involved in producing a new product brings even greater agility to the concurrent design, development, engineering, quality and production processes that together create a design-to-manufacturing framework.

Where manufacturers see the greatest benefit from committing to a rigorous, ongoing machinery fitness plan is in accelerating new product development timelines while reducing costs. Knowing how every machine will react to new production requirements is invaluable in reducing errors in everything from initial design concepts to work instructions.

Another key benefit of combining fitness plans with design to manufacturing is that collaborative teams know by how much machinery yield rates have improved and what that means for future production runs.

Finally, fitness plans for machines create the strongest foundation there is for manufacturers to give their CAM, computer-aided design (CAD), simulation/finite element analysis (FEA), electrical, inspection, and manufacturing teams the assurance they need that they can pursue faster development, test and product release cycles than ever before. When every system in the manufacturing process runs at a different cadence or clock speed, achieving concurrency is a must-have, and design to manufacturing combined with ongoing machinery fitness plans are essential. (Editor’s Note: This article is an extended version of a Viewpoints column that appears in the September 2019 issue of Manufacturing Engineering)

About the Authors

Louis Columbus is a principal at manufacturing enterprise resource planning company IQMS (now DELMIAWORKS, part of the Dassault Systèmes family). Michael Buchli is a senior SolidWorks product and portfolio manager at Dassault Systèmes.

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