Manufacturing Insights — Part 3: Getting Data Right
This is the third of our three-part series on the future of manufacturing. Check out Part 1: The Impact of the Internet of Things, and Part 2: Checklist for Successful ERP Projects.
Previously in our series on the future of manufacturing, we discussed the impact of the Internet of Things, and we provided a checklist for a successful enterprise resource planning (ERP) project. Now let’s take a look at data, a third and arguably most critical component of the future of manufacturing. More specifically, let’s talk about how to get data right.
Getting Data Right is a Critical Commercial Issue for Manufacturing Businesses
We meet many manufacturing CEOs who are frustrated about their data. Despite spending huge sums on new systems, their waste is much higher than expected, and they are still unable to get a clear view of inventory or the true cost of production.
When a project fails to deliver, often there’s a simple cause: the master data in the system is wrong. The technology may be fine (emphasis on may), but if the data is wrong, then everything else is built on sand. When new systems roll out with poor data, problems remain and a growing business becomes less profitable.
What do we mean by poor data? One example is duplication — when the same customers, finished goods (FGs), or raw materials (RMs) have been entered with different names, often multiple times. The bigger the company, the more likely it is that these mistakes can happen.
Poor data leads to some (or all!) of the following problems:
- Reports are wrong and time-consuming to fix
- Customers are upset by incorrect, incomplete, or late delivery
- Procurement must over-order to create safety stocks
- Sales can’t accurately forecast delivery dates
- Labels or documents may be wrong, which may have legal or safety implications.
Poor Data: Causes and Solutions
Broadly speaking, we’ve identified three root causes of poor data. We’ve explained them below as well as provided possible solutions:
Leadership is weak or ownership is unclear.
Data is difficult, detailed — and (let’s be honest) not very interesting. So who is going to take ownership of it? Solution vendors don’t really care about your data, and your people are too busy with their regular tasks. Often it gets left to the Finance or IT teams to sort out. And they may not have the knowledge to fix the issues or the authority to get people to change bad habits.
This issue has strategic implications, so an executive needs to take ownership. He or she also needs (a) time to get to the bottom of the issues, (b) experience in this kind of work, and (c) the authority to make decisions and get things done.
The strategy is confused or vague.
Processes need updating. Data problems often reflect process problems, or lack of alignment between people and departments. It may not be clear internally who is responsible for what, such as for updating data or correcting errors.
Perhaps this kind of thing falls to some very overstretched people. Or there may be no-one getting to the bottom of what goes on and why. So it’s a good idea to take a hard look at your processes. Fixing the problem may require process changes, technology changes and some retraining (or even “redeployment” if the real issue is an individual).
Multiple systems create confusion. We often see data issues when companies use multiple systems. There may be good reasons for this. But if you have separate systems there needs to be clarity as to which system owns what data, and interfaces need to be complete, tested and working.
Monitor systems and processes. You’ve straightened out your processes and standardized your data. Now you need to monitor these activities so you can make corrections when necessary. This will be easier once you’ve established who has authority and ownership.
You’re planning and reacting for the short-term.
Data issues often arise due to time constraints, and commercial pressures result in shortcuts. Getting data right may be a matter of diminishing returns, as fixing obscure problems can be very difficult and time-consuming.
If this sounds like your business, it’s time to make some rational decisions. Short-term pressures don’t necessarily mean that you can’t plan for the medium- or long-term.
List the data problems, estimate the necessary effort for each and the business impact. If there’s no time to fix something right now, can it be on the list for next month? Can you monitor the impact to ensure it doesn’t grow? Deciding to tolerate a problem for now is not the same as sweeping it under the rug.
Even poor systems can work effectively when the data is policed, maintained and structured. Most importantly, this is a good platform for system improvements. Well-structured data can eliminate a whole range of problems and inefficiencies, can boost profitability, and can give everyone new energy as less time is wasted on distractions and snags.
Read the rest of our special series on the future of manufacturing:
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