Improving Overall Equipment Effectiveness With Lean and Value-Stream Mapping

Blount is a discrete manufacturer specializing in chain-saw components. At their plant in Guelph, Ontario, Canada, the company operates a 1,200-machine facility and serves a global market. Manufacturing executives were looking for a performance management solution to support the company’s lean initiatives and needed to find a means to improve production reporting accuracy, reliability, and frequency.

The company had a particular machine that was only performing at 30 percent overall equipment effectiveness (OEE) and backlogged with thousands of parts, which caused downstream production to be starved, a classic bottleneck effect throughout the plant. Blount sought a technology solution to help identify the exact cycle time of the machine and gain insight into the process flow (as well as the decision-making flow). Memex Automation offered OEE quantification metrics directly at the machine through hardware and software. Within 48 hours of the implementation the manufacturing engineers were able to reduce waste relative to downtime, labor, energy, and raw materials, and increased OEE by 100 percent. The technology enabled real-time production reporting for more reliable and accurate data, as well as significantly increasing visibility into day-to-day operations with a resultant benefit of increased collaboration between operators, engineers, and production.

The primary challenge was to eliminate aspects of the manufacturing process that did not add value to customers. The application of lean principles, with accurate OEE data, including continued process improvement, was central to the success of the project. Utilizing lean tools such as value-stream mapping accounted for the activity within production, as well as the management and the information systems that supported basic processes. Value-stream mapping is the process of observing and understanding the current condition and drawing a map that becomes a blueprint for lean implementation.

Lean OEE

Excessive waste resulting from unplanned downtime and over consumption of raw materials necessitated drastic changes at Rose Integration Ltd., a manufacturer of precision-machined components located in Carleton Place, Ontario. Based on historical data, annual unplanned downtime totaled 25 percent of the aggregate of the company’s production lines. Taking the cost of labor, quality, and lost production time into account, the shortfall represented hundreds of thousands of dollars in lost opportunity. Adding to the cost of downtime was the waste due to over-processing, which translated to excess energy consumption and chemical usage. While parts on the production line waited to move from one process to the next, energy was wasted.

At the end of every shift, employees physically counted the number of parts produced and entered the figures into a spreadsheet manually. By automating the process directly from the machine, the operators were freed to do more tasks. “To stay competitive, we needed to invest in lean technology to provide customers with the best possible service,” says Graham Whitelaw, president of Rose Integration Ltd. “Since implementing the OEE solution, we have clearly identified the root causes associated with unplanned downtime and as a result we were able to reduce total downtime by 25 percent. We also improved our material flow by lowering the incidence of machine stoppages and are better able to manage energy use and keep the costs of quality down to a minimum. The ROI [return on investment] on implementing this solution over all the machines had a payback of less than three months.”

Higher output and increased productivity were key factors in the justification to invest in a lean OEE solution, however, the effect is felt throughout the organization from the CEO on down to each machine operator. With access to real-time feedback on overhead displays, operators have the information needed to understand where they are vs. the expected output. Employees gain a sense of ownership and pride in their work and positively affect the bottom line.

Real-time reporting accuracy and reliability lead to a number of other capabilities in Progressive Inc.’s Arlington, Texas, facility. The automatic detection and escalation of events allowed supervisors and managers to keep on top of issues as they happened rather than after the fact. Better product quality resulted from more efficient use of materials; total operational visibility into production provided managers with access to decision-support information from anywhere in the plant.

“Not all OEE technologies are created equally,” says John Rattray, a principle of Memex Automation. “Many are inefficient manual systems, involving operator input on a clipboard, re-keying into a spreadsheet. An intelligent performance management system getting signals direct from the machine is designed to increase operational efficiency and accuracy, improve quality, and drive higher levels of performance in manufacturing and other production operation.”

Lean manufacturing principles require that companies are able to leverage existing investments in equipment and business systems to enable operational visibility and real-time information flow that is fast enough to be actionable, with the accuracy and reliability to facilitate strategic planning and tactical execution.

Manufacturers are facing competitive price pressures, shrinking brand loyalty, and increasing costs. Compliance constraints are affecting business efficiencies while quality expenditures continue to increase. “The only options to battle these trends is to increase efficiency, improve quality, while reducing fixed costs and lowering cost-of-goods sold (COGS),” says Rattray.

One option to implement this type of quality initiative is a cost management platform. Another option is to track the true OEE on the plant floor. Consider that a 10-percent improvement in OEE can generate an increase in operating income of more than 60 percent, a tremendous increase to the bottom line that could represent more than $5 million on sales of $100 million.

The speed of complex machine changeovers has a substantial effect on the profitability of a production run; manufacturing line workers are not encouraged to measure effectiveness and profitability suffers accordingly.

Built-in intelligence goes beyond raw data collection to provide a fully integrated production and quality performance management application. The quality component for manufacturers is the ability to differentiate and compare real-time data against planned estimates. These discrepancies allow estimates to be validated and automation processes to be quantified.

Overall equipment effectiveness

OEE is critical to quality control, as it directly measures productivity (actual production compared to capacity to produce), and is correlated directly to operating income and profit.

Production machines are designed on the basis of a certain production capacity. In practice, actual output lags far behind the capacity of the equipment. If the output of product lags far behind the capacity of the installed machinery, there is hidden production capacity.

The business case for OEE is very sound. On on pages 47–56 of Robert C. Hansen’s book, Overall Equipment Effectiveness: A Powerful Production/Maintenance Tool for Increased Profits (Industrial Press, 2001), the author compares the business case of a 10-percent improvement in OEE for a manufacturer producing and selling $100 million per year, generating earnings before interest and taxes (EBIT) of $9 million. The base business case of operating at a 60-percent OEE is compared to the same company operating at a 66-percent OEE by first comparing the reduction of direct labor’s effect to operating income—a 21-percent improvement in EBIT, then comparing the effect of increased sales to operating income—a 62-percent improvement to the EBIT.

Machine performance is always in comparison to an ideal machine—specifically, a machine that always operates at maximum speed and with a quality rate of 100 percent. OEE is determined by losses in availability, performance, and quality. The OEE indicates how effectively a machine is being used compared to the ideal machine (OEE = 100%). World-class OEE is considered to be 85 percent, made up of 95 percent each of availability, performance, and quality.

In seeking OEE technology solutions, there are specific measurable results that should be realized while remaining a simple and easily understandable tool for an improvement process:

• OEE solutions must automatically and quickly identify the problem (make them visible).
• Prioritization of processes to improve becomes clear very quickly
• The ability to make well-founded choices for specific improvements is factually (data) based.
• OEE increases are a direct result of the improvements and can be observed immediately.

 

“Most manufacturers like to believe they are operating at a high efficiency level according to traditional methods of measurement using operator-collected data,” Rattray says. “Many executives and savvy engineers feel there is much room for improvement on the shop floor, but have difficulty getting actual data to support their case. When utilizing a world-class metric of productivity such as OEE, manufacturers often discover that they are operating at only 50 percent of operating efficiency. By automatically collecting OEE from the machines, a company can apply the paradigm, ‘if you can measure it, then you can manage it.’”  

By exposing the plant team to the three components of OEE—quality, performance, and availability—they’re enabled to focus on the most critical issues that produce real benefits for the company.

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