Jun 20 2019
The Use of Data to Improve Manufacturing | Luis Mauleón | Empresa XXI
This is the translation of article recently published by Asenta’s Partner-Director Luis Mauleón in Empresa XXI, summarizing the key points of my lecture last month at the Faculty of Engineering of Deusto University in Bilbao, as part of a series of events organized by Asenta Management Consultants.
Contents
Practical lessons from Michel Baudin
In an environment with an overabundance of data, it is a paradox that we still have difficulties to finding simple answers to simple questions.
The data cleaning time sink
Understanding that data is at the service of improvement in decision-making and that the volume of data generated and collected has grown exponentially, we can conclude that a significant percentage of time should be used in the cleaning and validating data. Data scientists do indeed spend much of their time preparing or conditioning data.
New tools
In turn, the increase in data volume leads to changes in analysis methods and gives a growing importance to the tools for communicating the results of observations and analysis.
Make it useful
In Manufacturing, the main focus is to use data is to better understand customer demand, improve the design of operations, speed up the diagnosis of process problems, and measure performance.
Direct observation and information systems
About data sources, Michel Baudin points out that, although the most valuable information may come from direct observation, it must be complemented with the analysis of data that comes from the information systems. About this, he highlights two frequent problems in companies:
- Manufacturing software systems like ERP, CMMS, or MES provide information for the management – planning and control – of the processes, without displaying the necessary engineering information or technical data.
- The interpretation of the data is often subjective, and sophisticated tools are useless in the absence of objectivity.
Challenges in making data talk
The five challenges identified in the use of the data relate to accessibility, quality, the analysis skills, communication methods, and integration with the improvement of the factory.
Why are they challenges? The message regarding the availability of the data is that given the amount of data, we must decide to collect only what is necessary.
Data quality
The quality of the data refers to their meaning and their credibility, which will be different depending on their origin. The quality of the data from internal sources is easier to control and the information easier to understand.
Mastery of tools
For effective data analysis, we must learn a growing number of tools but also how to apply them to find answers.
Visualization
The fourth challenge consists in making the manufacturing data talk. We must use visualization tools that provide relevant information in a comprehensible and agile way, avoiding “chart junk.” Some ways of presenting data are very complex, yet do not make patterns stand out or hep understanding what happened.
Finally, we must also highlight the importance in Manufacturing of visualizing the relationships between dependent variables in the processes and connecting the results with the improvement actions in the factory.
Conclusions
The Master Class concluded with a recommendation to companies: data scientists learn must about manufacturing and that manufacturing professionals must learn about data science.
#DataScience, #Asenta, #UniversityofDeusto, #OperationalExcellence, #Lean
June 20, 2019 @ 1:45 pm
When discussing data in the workplace, we should always remember Sensei Ohno’s warning.
“In a production plant operation data is highly regarded – but I consider facts to be more important. When a problem arises, if our search for the causes is not thorough, the action taken will be out of focus. This is why we repeatedly ask ‘why.” — “People who can’t understand data are of limited use The Gemba where data is invisible is bad. However the people who only look for data are the worst of all.” — We must always remember that data is a measure of the effect. The solution comes from identifying the detailed causes of this effect and their actions of correction.
Vilfredo identified this process. “Give me a fruitful error any time, full of seeds (facts), bursting with its own correction.” Vilfredo Pareto.
March 16, 2021 @ 10:22 am
Hello Mr. Baudin. Once again a great, precise article that proves to be quite informative. I have had personal experience with the manufacturing systems software that you mention, especially ERP. My question to you is: of the three system which one provides the most benefit to an organization, or does it depend on the type of business? From a lean standpoint, is there a system that is preferred and seems to be the most efficient?