June 28, 2018
|by John Hayes, VP Marketing and Sales|
Manufacturers are facing competitive price pressure, shrinking brand loyalty, and increasing costs. Compliance constraints are impacting business efficiencies while quality expenditures continue to increase. The only winning strategy is to increase efficiency and improve quality, while reducing fixed costs and lowering cost-of-goods sold (COGS).
One way to implement this type of quality initiative is a cost management platform. Vecna Robotics is the first company to embed such a platform, with help from artificial intelligence (AI) and the Industrial Internet of Things (IIoT). Sensors collect data 24/7, which is then used to measure overall equipment effectiveness (OEE) on the plant floor. A ten percent improvement in OEE can generate a greater than 60 percent increase in operating income – a tremendous increase to the bottom line that could represent over $5 million on sales of $100 million.
The speed of complex machine changeovers has a substantial effect on the profitability of a production run, but manufacturing line workers are often 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. Any discrepancies help to validate – or improve – estimates and quantify automation processes.
Overall Equipment Effectiveness
OEE is critical to quality control; it directly measures productivity (actual production compared to capacity to produce) and is correlated directly with operating income and profit.
Production machines are designed to meet a certain capacity. In practice, output lags far behind that volume for a variety of reasons. This means that there is hidden production capacity. Only Vecna Robotics closes that gap in knowledge, using AI to design immediate corrective action.
In the book, “Overall Equipment Effectiveness: A Powerful Production/Maintenance Tool for Increased Profits,” author Robert C. Hansen compares the business case of a ten percent improvement in OEE for a manufacturer producing and selling $100 million per year, generating earnings before interest and tax (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. Firstly, comparing the reduction of direct labor’s impact to operating income is a 21 percent improvement in EBIT; secondly, comparing the impact of increased sales to operating income is a 62 percent improvement to EBIT.
Machine performance is always measured in comparison with an ideal machine—specifically a machine that always operates at maximum speed, 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 percent). World-class OEE is considered to be 85 percent, made up of 95 percent each of availability, performance, and quality.
OEE is a simple and easily understandable tool for improvement that can also provide specific measurable results:
Most manufacturers like to believe they are operating at a high efficiency level. Using operator-collected data and traditional methods of measurement is no longer effective. Most executives and engineers know there is room for improvement on the shop floor, but only with AI and IIoT does a plant manager have direct access to actual data.
When using a world-class metric of productivity like OEE, manufacturers often discover that they are operating at only 50 percent of operating efficiency. By automatically collecting OEE data from the machines, a company can apply the paradigm “if you can measure it, then you can manage it.” Exposing the plant team to the three components of OEE — quality, performance, and availability — allows it to focus on the most critical issues and produces real benefits for the company.
When OEE data is captured directly at the machine level, the concept and information also become accessible to the machine operators (the employees who have the most to gain from tracking and improving the effectiveness of the operating equipment).
Implementing the OEE solution from Vecna Robotics ensures that customers clearly identify the root causes associated with unplanned downtime. On average, companies see a 25 percent reduction in total downtime with an effective. Lowering the incidence of machine stoppages improves material flow, helps with energy use management, and keeps the costs of quality at a minimum. The ROI on implementing this solution across all the machines has a payback that can be measured in as little time as a few months.
Better product quality results from more efficient use of materials because of AI improved accumulated learning, and total operational visibility into production provides managers with access to actionable information from anywhere in the plant.
John Hayes, Vice President of Marketing and Sales for Vecna Robotics, is a widely respected thought leader for the manufacturing, distribution, and material handling industries and a Supply & Demand Chain Executive “Pros to Know” recipient. For more than twenty years he has been evaluating, designing, developing, and implementing innovative software and hardware solutions with a particular focus in the AGV (automated guided vehicle) space. Contact John at email@example.com.