The Future of Manufacturing Part 3: Getting Data Right
In previous installments of this series, we discussed the key trends in the future of manufacturing and provided a checklist for new ERP and MRP projects. Now we need to talk about a critical commercial issue for any manufacturing business: getting the data right.
We meet many manufacturing CEOs who are frustrated that, despite spending huge sums on new systems, they lack visibility of the true cost of production, have higher than expected waste, and have no clear view of inventory.
New systems like IFS, Nav, AX or Dynamics 365, SAP, Sage, Epicor, Oracle or Syspro can cost big money. But if the project fails to deliver, often the root cause is that the master data is wrong. The system may be fine (though often it isn’t!), but if the data is wrong then everything is built on sand.
Poor master data confuses everything, embedding waste, errors and poor service throughout the organization. For example, product costings, bills of material, recipes, or routings, may have not been set up correctly in the first place or may have become out of date.
We’ve frequently seen examples of businesses where the same customers, finished goods (FGs) or raw materials (RMs) are entered multiple times, but called different things, creating all kinds of confusion. The bigger and more widely distributed the company, the more possible this can happen.
These issues usually result in reports that are wrong or time-consuming to fix. Staff costs increase, especially for the finance team, who may have to clear up the mess in Excel.
Procurement may over-order to create safety stocks, tying up cash; sales can’t accurately forecast delivery dates. Eager salespeople may “borrow” items from different orders to fulfill today’s priority, creating more problems down the line. Inventory turn is lower than planned and OTIF targets get missed.
Customers are disappointed by long lead-times or upset by incorrect, incomplete, or late delivery. Labels or documents may be wrong, which is inconvenient at best and, at worst, can have legal or safety implications.
On the other hand, well-structured manufacturing data provides insights to senior managers, allowing them to answer important questions:
- Who are our most profitable customers?
- What are our least profitable products?
- Which processes require the most rework?
Well-structured data also allows for accurate real-time data, empowering supervisors to organize work within defined boundaries.
Fundamentally, poor data can make it hard to take advantage of efficiencies of scale. You can roll out new systems, but the problems will remain. And a growing business becomes less profitable rather than more profitable.
So then, how does a mid-market manufacturing business get data right?
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1. Instill strong leadership and ownership.
Data is difficult and detailed. And let’s be honest: it’s not very interesting. Solution vendors are contracted to deliver technology, so they don’t really care about the data.
Everyone’s too busy doing their day job, so it may get left to the Finance or IT teams to work out data problems, and they may not have the knowledge to fix issues or the authority to get people to change bad habits.
But data has strategic implications, so an executive must take ownership. And whoever takes charge of the data needs to have time to get to the bottom of the issues, experience in this kind of work, and authority to make decisions and get things done.
Production and business teams will have to be involved as well, taking responsibility to help get the data right and keep it that way. And since data quality is an ongoing exercise, the senior team must receive reports at regular meetings.
2. Identify the problems and their solutions.
It may seem obvious, but it’s often overlooked: data quality issues will keep reoccurring, even snowballing if you don’t find the root causes and create solutions.
One place to start is to look for who is supposed to be in charge of data quality – if anyone. Data problems often reflect process problems or a lack of alignment between people and departments.
It may not be clear internally who is responsible for what, for updating data as things change, or for correcting data when errors are found.
Perhaps data quality falls to some very overstretched, helpful people who may be vital but have a very low profile. Or there may be no-one who has the time to manage data quality.
Using multiple systems without proper integration is another common cause of poor data quality. Sales, finance and production teams’ reports simply won’t agree if they are working from different base information. Fixing the problem may require process changes, technology changes and some retraining (or even “redeployment” if the real issue is particular people!).
There may be good reasons for using multiple systems: for example, specialist warehouse management solutions that work with advanced technology such as voice- or sight-picking, which isn’t supported by a basic ERP platform.
But with separate systems, there must be clarity as to which system owns what data (e.g. ERP owns stock quantities, WHM owns stock location) and the interfaces need to be tested and working.
3. Make rational decisions about when to solve problems.
Data issues often arise because time and commercial pressures make shortcuts necessary. Getting data right may be a matter of diminishing returns, as obscure problems can be very difficult and time-consuming to fix, and they may just not be worth it!
The most important thing is to make rational decisions about your data. List the data problems, estimate the necessary effort for solving each of them, and the business impact.
If short-term pressures mean that a problem won’t be fixed now, then perhaps it’s on the list for next month. In the meantime, monitor its impact. It may make sense to tolerate a problem for now. It will never make sense to sweep it under the rug.
Even poor systems can work effectively if the data is structured, maintained, and policed. Most importantly, this is a good platform for system improvements: maintaining data quality 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.
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The Future of Manufacturing Series:
Part 3: Getting data right