Manufacturing analytics is proving to be a tough nut to crack. In my journey with shop floor data analytics, I have probably spent half my time learning from industries with more notoriety, specifically retail and health care. I will say, both of those industries are more focused on specifying results rather than standardizing the data. Our customers each use data differently and that is what drives our approach at 10in6. I am passing on some of ideas of what works for us. I am starting with the ground zero for data analytics, data collection.
WHAT IS DATA COLLECTION?
Shop floor data collection is the process of gathering and validating the information about the operations and movement of material on a shop floor. Don’t be surprised if the data collection portion of your analytics journey is frustratingly iterative. Good data is invaluable but hard to encapsulate into a well-organized insight.
STAYING FOCUSED.
Every analytics project starts with a question that you aim to answer. The next step is to define the data you need to answer that question and where it is coming from. Pun intended – a shop floor has many moving parts; you won’t find the data you need in one place.
HOW SHOULD DATA COLLECTION BE DONE ON A SHOP FLOOR?
Google “data collection” and many articles will be returned, each one with a list of best methods to collect data for an industry such as retail. Now google “manufacturing data collection” and you will find plenty of ads and articles that link back to advertising for data devices and software but not much about best methods.
Yes, 10in6 is among the many listed. I’ll start with our data collection credo, technology follows methodology. In other words, we emphasize the data gathering process above the technology used. We have learned that prioritizing technology is a very costly rabbit hole that can lead to months of delays only to be abandoned when the next technology offers more hope.
COMPARISON TO RETAIL ANALYTICS
Let’s compare your shop floor data collection to retail sales data collection.
With retail analytics a good question might be, “How many of these spring jackets can we sell?”
In comparison to manufacturing, “How many of these parts can we deliver?”
Focus Groups
In retail, good place to start would be interviewing focus groups. Just as not every member of the population is useful for your focus group, not all the assets on your shop floor are relevant. Start here. Find the assets that you need to interview to get the data you want to answer your question. These assets may be devices, controllers, or employees. I’m not suggesting that you attempt to interview a controller by offering it donuts but reviewing the data that the controller uses for an operation will give you plenty information to consider for collection.
IIoT devices do a great job of collecting data but often in a proprietary manner that is hard to put in context with data from other sources. At 10in6, it is part of our methodology to collect information from IIoT devices and ‘concentrate’ it along with other data flows to create more context.
Surveys
Another retail data collection technique is surveys. Have you ever noticed that most surveys are multiple choice or ratings? This is to make sure that the answers are concise and only relate to the question that the surveyors want answered. That’s the beauty of a good survey, you retrieve only the data you need. Let’s translate this to your shop floor. Plan your data collection strategy around the ability to select only the data you need, avoid the temptation to deal with as much data that is available, you know, for the future. Stay focused and invest only in technology that gives you the ability to easily choose your data.
A survey often has a finishing section for additional comments. Individual observations are essential for quality issues, there are so many ways things can happen on the shop floor, don’t let actionable problems become foggy memories after the fact. Provide and train operators with the technology to enhance process input as it happens and keep the interface simple, like a survey to automate critical and supplemental content.
Historical Data and Documents
Why to you think you get asked for an email address at the cash register…every…single…time? Sure, they will put it on file somewhere to spam you later but linking that address to your sales receipt is essential for predicting what you might buy next time, including a new spring jacket.
A comprehensive manufacturing data collection strategy will link information such as recipes and shift details taken from these documents to shop floor information. In turn, this information can be turned into robust future planning material.
Often, spreadsheets are full of historical data to drive a forecasting project. This is how 10in6 started, by putting historical and real time shop floor data directly into a spreadsheet and our customers love having their favorite analytic environment full of valid data to work with. Talk to people to see what they are doing with spreadsheets to get an idea on where historical data comes from and try to automate it.
Historian Data
Every cash register has a journal that keeps a flow of timed transactions. That gets translated into sales cycles, so stores get staffed properly and inventory is kept current.
Most software systems also have built-in operational log . In manufacturing, a historian records the data of processes running on the shop floor and is usually generated by a PLC or software package. When scheduling and part information is enhanced by this data, you can build a clearer picture of what and when things happened on the shop floor. If that data is combined with data from surrounding areas of the shop floor, you add a very powerful and comprehensive flow of information.
Manufacturing Data Collection Steps
I’ll summarize by leaving my own list of best methods for shop floor data collection, and saying good luck.
- Decide on a concise question you want answered.
- Be specific about the data you need to answer it.
- Plan to retrieve data from more than one source.
- Invest first in the process of retrieving the data, then chose the necessary technology.
- Build on your successes for the next question.
