In almost every supply chain project we’ve been involved in it has been necessary to improve master data.
It’s almost a cliché these days but any system is only as good as the data you put into it. As they say, a perfect analysis of useless data is a perfectly useless piece of analysis.
When we sit down with new customers one of the first things we hear is almost always; ‘our company has quite a lot of problems with its data so this might be a challenge …’
So, if that sounds like you, and you’re considering implementing a SCM solution, you might well be thinking ‘Oh no, my supply chain master data is completely inadequate. What’s the point?’
Well, for starters, be reassured, you’re not alone. Far from it. Almost every company we’ve worked with has had some challenges with its data. Is there a single company in the world that has no issues at all with its master or transaction data? We’ve yet to encounter it.
At the outset having clear goals is much more important than having perfect data. Equally important is being sufficiently flexible that you’re able to make changes and adapt on the fly. Don’t expect to have everything in perfect shape before you start because it will never be perfect. As they say; ‘the best is the enemy of the good.’
At the outset having clear goals is much more important than having perfect data. Equally important is being sufficiently flexible that you’re able to make changes and adapt on the fly.
Data issues are often completely understandable. For instance, if you have a system that isn’t capable of factoring weather into demand forecasts then why would you bother to make a note of it? By the same token if your colleagues are recording data you don’t use, consider asking them to focus instead on improving the maintenance of data that you do use or that you might anticipate using.
Quite often, when we’re working with a new or a prospective customer, we carry out an analysis of their operations and run their existing data through our systems (prior to their being configured precisely to the requirements of that business) which gives a good indication of the impact the latest SCM software will have on their operations.
One thing that routinely becomes clearer at this early stage as a result is where a company has good data and where it needs to be improved. As the quality of data will become critical when the implementation begins in earnest with the pilot phase, most companies start working immediately on cleansing and organizing their data.
However, at this stage most businesses only have a broad idea of where the challenges will come from. And, as a rule, as the implementation of a new system progresses, more and more data issues typically come to light. So most companies we’ve worked with simply accept that improving supply chain master data will be an ongoing process throughout the implementation and beyond.
With accurate sales data, data that can be broken down day by day, store by store, product by product, you can build up a good picture of likely demand.
With accurate sales data, data that can be broken down day by day, store by store, product by product, you can build up a good picture of likely demand. And if you realize you haven’t tracked all the data you now wish you had the good news is that with time you will accumulate the information you need.
If sales is one side of the equation, the other is inventory. In aiming to master your supply chain master data it’s useful to keep uppermost in one’s mind the basic principle that unless you know what you’ve got it’s really difficult to judge what you should order. It’s critical for an efficient replenishment operation is to have more-or-less up to date and accurate inventory balance data or stock balance data. That is an ongoing task and often means attention needs to be paid to operations and processes as well as systems. Nail these two and you’re well on your way to mastering your master data.
Other Data Issues
We typically spend some time during the implementation process considering how to handle the open purchase orders; goods that are on their way to stores.
For example, how do you handle an open order that was supposed to come one week ago? Do we treat it as though it’s coming or should we treat it as though it’s not coming anymore? It’s about defining rules and deciding how to interpret the data in these circumstances.
But one needs to go further and think of data more holistically. It’s one thing to keep on top of all the internal data generated by POS systems, stock control and so forth. But more companies need to be equally assiduous about external data – supplier business terms (such as freight-free or full truck), offers, discounts and so forth. If that data isn’t up to date, then it will affect the quality of the decisions made when using it. Someone needs to be responsible for ensuring that data management doesn’t stop at the boundaries of your business.