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ML Journal

ML Journal

Found: The Hidden Value in Manufacturing Operations

Once armed with data, the hidden value is often in plain sight. 

The October 2022 Manufacturing Leadership Journal article, How to Find Value Hiding in Your Operations, introduced the value stream (Figure 1), which illustrates the various objectives and challenges manufacturers face to produce “as planned” while delivering products to customers on-time, in-full, without quality defects, and at a competitive price. Looking deeper at value streams, here are seven key areas where FORCAM customers are finding tangible value.

Downtimes / outages

There is no better place to start than reducing downtimes and outages, typically one of the first places to look for hidden value and an area with great potential. The key is collecting and seeing the detailed reasons for downtime, and then analyzing those reasons over time and by various criteria, such as in comparison to plan and across lines and plants. While any one incident is easily observable, seeing data in aggregate provides the visibility to see root causes and trends.

In one project, a manufacturer was surprised to learn that true production utilization of an expensive press was actually lower than being reported by the ERP system. This was due to the ERP not having the more refined level of detail of machine status and reporting truly non-productive time as productive. With this greater insight, they were able to pinpoint that a significant amount of downtime was due to waiting for specialists from the tool department to perform routine maintenance on forms for the press. They determined that basic maintenance could be done by the machine operator, reducing the downtime and – as an additional benefit – freeing up the specialist for other tasks. This manufacturer was able to increase availability from 53% to 82%.

Shorten cycle time / setup

Once downtimes are reduced, a next logical area of opportunity is the reduction of cycle times and set up times. Accurate cycle time measurement and monitoring can reveal differences in lines, plants, operators, and machines, as well as trends over time and deviations from standard. This can lead to training opportunities, equipment improvements, process changes, and establishment of more accurate standards.

Setup time reduction is a major area of focus for another FORCAM customer. As an example, they found that a pallet-changing system for a machine was being underutilized, so that the machine was unnecessarily idled for setup. By adjusting the staging of work and through operator training, they were able to increase machine utilization by 20% by performing setup work on an off-machine pallet. They were also able to make greater use of after-shift production during lights-out operations. Further setup time reductions were realized through the automated downloading of CNC programs from a central repository and grouping of orders to reduce changeovers.

Less scrap (Quality)

Continuing on the ring around “as planned” on the value stream, the next stop, less scrap, is all about quality. Bad quality diminishes value not only in wasted material and personnel and machine time, but if bad products leave the factory the company’s reputation and, ultimately, business are at risk.

Timeliness of collecting, analyzing, and reporting quality data held the key to finding value in another FORCAM customer project. Previously, the data was manually collected from each machine at the end of the shift. It was then manually entered and often not analyzed and available for days or longer. Now the information in available in near real-time, enabling action to be taken immediately to prevent substandard products from being produced. The data has also yielded longer term insight into patterns of quality issues that led to process improvements in material handling and changeover, as well as reducing material waste.

OEE up

In a project that focused on improving Overall Equipment Effectiveness (OEE), the company was under continual pressure to reduce costs and ramp up performance. Its present OEE was insufficient. Once enabled with data, it was able to see a large variance in the time taken to perform a tool change on a key manufacturing process. Further, the times experienced were substantially higher than when the process was observed. The company determined that installing a countdown clock on the machine would allow the operator to anticipate the need for a tool change and to be ready and at the machine when needed. In addition, monitoring the time to change the tools against a standard reduced the time to actually perform the change. The company was able to increase OEE by 20 percentage points.

“While any one incident is easily observable, seeing data in aggregate provides the visibility to see root causes and trends.”

Accurate OEE can also help with investment decisions. In another example of a surprising insight that an asset is running below its specification, a company had to deal with a large order. At first glance this new business made investment in new machinery appear necessary. In this case, the company was lucky because a data-driven OEE project was just started and, as a side effect, showed that the existing equipment was, in fact, able to handle the new order. The tricky part was that from the viewpoint of the shopfloor, the machines appeared to be running at capacity, and therefore the additional machinery met the payback criteria. In this case data-driven versus experience-driven did have a clear winner.

Reduce emissions & energy costs

As highlighted in the Manufacturing Leadership Council’s Critical Issues Agenda, sustainability is an increasingly important topic for manufacturers. And with rising energy and other resource costs, it has become a financial imperative as well. Manufacturing 4.0 and associated digital tools and approaches can serve as an enabler.

One project in this area began with enabling shop floor machines with the ability to collect detailed data on resource usage, including electricity, gas and water. But the hidden value became revealed when this data was related to what the machines were doing, that is, were they in production, setup, warm up, idle, or another state, and what order or operation the machine was performing. This identified opportunities to improve scheduling, grouping, and sizing of production orders with regard to resource consumption, prioritizing use of the most energy efficient machines, and data to support decisions as to when to shut down and when to power up machines. The result was a 20% reduction in energy usage while maintaining the same processes and increasing production volumes.

Improve delivery performance, OPE & scheduling

Improvement opportunities build on each other while working through the value stream, returning to the overall goal of delivering on-time and in-full to the customer, while maximizing Overall Production Effectiveness (OPE).

“Just as the tools for collecting shop floor data keep getting better, so do the analysis tools that lead to discovering hidden value.”

Understanding true cycle times and establishing better standard target times based on real experience data led one company to better schedule and synchronize activities across the plant. They were able to reduce wait and idle times, and make sure materials and personnel were in place. This was especially valuable in a low-volume, high-mix, high-precision environment. With a variable takt time like in this case, only data-driven analysis can produce insights.

In a different project, better understanding cycle times, downtimes, and other information enabled better scheduling, resulting in elimination of escalations and the associated disruptions and cost to prioritize certain customer orders.

Getting from data to value

Just as the tools for collecting shop floor data keep getting better, so do the analysis tools that lead to discovering hidden value. Traditional techniques such as observation, daily or weekly production review meetings, root cause analysis, and continuous improvement programs – enabled with robust, accurate, and timely data – reveal improvement opportunities that are often hiding in plain sight. And emerging analytical techniques such as artificial intelligence and machine learning provide promise to reveal even more value opportunities that are more deeply hidden but waiting to be found in areas such as predictive maintenance, scheduling, and inventory optimization.  M

About the authors:

Christian Nagel
is Country Lead – U.S. with FORCAM


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