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How Real Time Information Can Save Money and Boost Efficiency
Too many companies still rely on data gathered from the last shift, last day, last week or last month to make decisions. Identifying problems in real time saves real money.
March 17, 2014 | By Luigi De Bernardini
Most companies today are making significant investments around the collection and analysis of information obtained from operations—particularly production lines. This information is used to investigate aspects of quality, efficiency, productivity, and personnel costs.
While data collection and analysis is clearly important, many of the companies I interact with are primarily focused on information and reports relating to the past: last shift, last day, last week, last month, etc. The production manager then compares these reports with similar information from other periods and with defined objective data.
As I pointed out, this kind of data and analysis is extremely useful as it allows plant managers to learn from the past to avoid repeating mistakes that may have compromised the expected result. But is looking to the past truly the best way to increase efficiency?
Is looking to the past truly the best way to increase efficiency?
Often, when discussing data collection with clients, I tell them that the process is similar to a man who comes home and discovers that he lost all the money in his pocket due to a hole that he hadn’t noticed. Recognizing the hole allows him to sew the pocket and avoid losing money the next day. But it would have been different if he had heard some money fall and had moved the money to a safer place, or at least had kept one hand on the pocket until he came home to avoid losing the rest of the money. The point being: If he had recognized the problem before he could have immediately taken action to reduce the losses.
The same reasoning can be transferred and adapted to any production company. It is definitely important to analyze production data to understand what factors are causing a loss of quality, production or efficiency. However, it is much more useful to be able to recognize in real time the onset of any problem and be able to intervene and reduce the possibility of compromising the production target.
This difference in approach can bee seen when manufacturing execution systems (MES) and manufacturing operations management (MOM) projects are instituted "top-down" rather than "bottom-up." Top-down projects, by their nature, focus on gathering information that can be analyzed to make operational decisions. Bottom-up projects, which are typically related to production lines and the causes of problems, provide granular information that can be made available in real time to allow all the actors involved in the management of production to know how things are going rather than how they went. This allows them to take action before the problem affects production results, thereby leading to immediate savings.
In a recent project that we implemented for a food company packaging department, we saw firsthand the value of the availability of real-time information. The client was equipped with a sophisticated system of reports generated from the information collected and recorded in the enterprise resource planning (ERP) system. This allowed him to run a detailed shift analysis. Not satisfied with this information, the client decided to implement a system to collect data in real time from the packaging lines. This information is then used to compare real time efficiency with efficiency targets. Plus this information can be shared through a dedicated big screen above each line so that everyone is aware of it.
The availability of this information allowed operators to take action as soon as they recognized a trend that deviated from the expected. Within a few months, this led to double-digit percentage improvement in the overall efficiency of the department. These efficiency gains translated into significant cost savings that allowed the client to repay the cost system with unexpected rapidity.
Recent Blog Posts
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What’s Next for Packaging Machinery Automation? | September 22, 2014
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