Steve Wise, VP of Statistical Methods, InfinityQS, discusses how companies can make the most of the new manufacturing Intelligence system.
In the manufacturing world, implementing a new software platform can be a BIG project. You need to consider retooling for the new system, unplugging the old system, and developing a backup plan in case there are glitches after the go-live date. But it’s a great opportunity. If you can overcome the natural lethargy and resistance to change that all of us experience in our personal and business lives, software can be a powerful enabler that will allow you to understand things about your environment that you could only guess about before.
Evaluate your data
A recent survey revealed that almost two-thirds of respondents felt their organisation wasn’t effectively using all the data housed in their ERP system. This is neither surprising nor new, but why does this trend continue?
When data-based systems are installed, the implementation is too often considered successful once the data are flowing and people can get to the data. Of course, watching data automatically flow into a database is a beautiful experience, but visualising data is just the first step. There are three milestones I look for in order to most effectively put manufacturing data to work. I can’t tell you how rewarding it is when a company implements a plan and the light bulb goes on and they finally see how data can drive truly effective action.
The first milestone is successfully processing all the streams of data to determine their usefulness. Some data are useful for making real-time decisions, some data are useful for making long-term strategic decisions, and some data are just taking up space. Real-time data need special analysis and alert tools to warn you if a change is needed. These are also required to provide feedback that indicates true anomalies so you don’t risk altering anything that will unbalance an otherwise stable situation. Finally, data that are used for longer-term decisions need to be cobbled together in a way that exposes all key trends and indicators, while useless data need to be turned off, throttled back or repurposed.
The second milestone is making sure that you don’t forget about your other data sources. What about data from Quality and Manufacturing Execution systems? Are there meaningful ways to integrate that data to aid in decision making? If so, what needs to happen to bring everything together?
Thirdly, what about the metrics? You need to make sure that the information you are receiving can support decision-making both up stream and down? Does it reward the right behaviour?
Sampling: don’t overlook the variables
The most overlooked data opportunity is when companies use attribute data sampling rather than variables data. Many quality-related data streams are measuring weak indicators such as yield, go/no-go, pass/fail, or conforming/nonconforming. These types of data are all attribute data; more specifically, defectives data. These data boil down to a 1 or a 0.
Defectives data sets only help someone feel good or feel bad about whatever those data represent. If the yield is bad, more powerful variables data are needed to isolate and help solve the problems. Variables data can be measured on a continuous scale, such as temperature, diameter and cycle time.
One of the biggest mistakes companies make with industrial data is to take a stream of variables data and trivialise it to a pass or fail. A better way to report and visualise data is to understand the data set’s distribution and predictability. This is efficiently achieved only with variables type data.
We’re all on the same side
There is a tradition in manufacturing that the operations and IT teams are in a permanent state of conflict. However, the truth is that each has an opportunity to make the other look very good. The complexities of today’s manufacturing systems result in so many visibility gaps that it can be hugely advantageous when a quality platform is able to deliver relevant intelligence to the people that can most effectively use it. Because operations and IT have an overlapping interest in taking the guesswork out of manufacturing operations, both departments regularly experience significant benefits once a company becomes more data driven.
That said, many of the most successful software-based projects are led by the IT department. This is particularly true when IT leaders rely on the operation’s user requirements as their guide. The problems between the two are magnified when IT is brought into a project late. It does happen that someone in operations will have a bright idea for a plan and will try to bypass the IT department and just get the project done without “unnecessary IT delays.” Eventually, non-IT people do usually realise their technical shortcomings, but by then the damage is already done.
Asking the right questions
Finally, when it comes to data management, manufacturers could help themselves by asking a few basic questions that challenge the purpose of every stream of data.
- Who are the consumers of the data?
- What decisions are going to be made with that data stream?
- If the data values start to increase (or decrease or remain the same), is that good or bad?
- If the data stream says that something needs to be done, is there any infrastructure in place to act on the data?
The responses to these questions will help identify what data are needed for real-time decisions, what data are best used for long-term strategic decisions, what the corrective actions are and what data just might become a waste to collect.
