How do we define machine data capture? What does it involve? Well, on a fundamental level, machine data capture involves using information to plan and direct production orders. The phrase machine data capture (MDC) refers to the interface that bridges the gap between your production equipment and information processing. Your equipment, for example, may include machinery used to make your products, i.e. a laptop, phone, or television. Computer systems handle information processing, monitoring processes throughout production. But how do we use this data? And how can Six Sigma improve it? In this article, we will address machine data capture and show how Six Sigma can improve your data collection.
How Do We Use Machine Data Capture?
The types of machine data your computer systems collect will vary. It can range from the volume of satisfactory products to sell vs. rejected sub-standard ones to machine capacity effectiveness and utilization. Additionally, your systems should also monitor factors like machine cycles, production time, availability and reliability. Machine status is an equally critical component of MDC and denotes a whole spectrum of important production factors. These factors include primary and secondary time, breakdowns and maintenance, as well as service requirements.
But what happens to all this data? How do businesses use it? It’s simple. All your captured data travels through interfaces where your manufacturing execution system (MES) collects and collates it. Think of your MDC as the gold miner and the MES as the sieve through which they filter all the information they collect. The combined MDC data and MES findings allow you to use data effectively. Furthermore, your goal is to use your data to plan and push production in the direction you want it to go. That is, toward achieving optimum conditions. This allows for increased productivity and better results.
How Can Six Sigma Improve Data Collection?
You’re probably wondering, how does Six Sigma fit into the equation? Six Sigma improvement projects are an extremely reliable method for process improvement. It allows you to make the changes necessary for a strong data collection plan. Moreover, Six Sigma project leaders, usually Black Belts, employ DMAIC to define, measure, analyze, improve, and control data collection processes. There are several prerequisites you must meet for an effective data collection plan.
First, we have the pre-data collection steps. Ensure your project team defines your data collection goals. What data do you need? Equally, for what purpose will you use it? What insight will it offer and what do you wish to achieve through collecting it? Using tools like brainstorming, affinity diagrams, and root cause analysis can help generate answers to these questions.
The project team should then be able to agree on your plan’s methodology and operational definitions. Teamwork is always most effective here, and we recommend examining previous data to compare to the current. It’s essential that you determine whether past, present, and future data will factor into the data collection plan, plus what methodologies you are likely to use. Skipping this step will certainly deliver insufficient, if not deceptive, results. Finally, it is essential you ensure the repeatability, accuracy, stability, and reproducibility of your data collection and measurement. Once you have defined and planned your data collection process using the above data, you can then move forward. Black Belts can oversee implementation and to nudge it in the right direction, should you run into any obstacles.
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