If you’re in the IT game, and responsible for data in a manufacturing, sales, or logistics organisation, you’re likely at some point to provide the supply chain team with data for network optimisation, supply chain modeling, or some similar project requiring data analysis.
Alternatively, you may be called upon to provide access to the data via analytics software, rather than supplying datasets for somebody else to analyse and interpret. Either way, the following ideas and guidelines should be helpful in offering up high-quality data for your supply chain team to work with.
Express the Need for Patience
It’s all too common for project managers to want quality data for their supply chain analysis, but at the same time to want it yesterday. As you probably know though, high-quality data means clean data, and data cleansing takes time.
As tempting as it may be to expedite the supply of data in response to pressure from the supply chain team leader or project manager, it’s far better to go ahead and clean it thoroughly first.
As unpalatable as the truth may be, you must try to stress the importance of clean data. If necessary, explain that although the supply chain team may have to wait a little longer, clean data will ensure the results of their analysis are accurate and meaningful. After all, you will be even less popular if your data leads decision-makers down the wrong path, regardless of how quickly you were able to provide it.
Supply Accessible, Complete, and Consistent Data
Don’t make the supply chain team jump through hoops to access the data its members require. If your IT budget will stand it, try to provide a platform through which the supply chain team can select, sample and work with the high-quality data it needs, when it needs it.
If you can make high-quality master and transactional data accessible to functional teams, you and your IT colleagues will have more time to focus on maintaining that quality, instead of starting every project with an extensive data cleansing exercise.
The supply chain team will also need datasets which are complete and consistent. Inconsistent data can lead to confusion and inconclusive analyses, while incomplete data is little better than having no data at all.
Since some supply chain projects, such as network modeling, might drive important decisions about asset use and investment, supply chain analysis is especially sensitive to data gaps and inconsistencies. Bear this fact in mind when the supply chain team comes to you for help.
Encourage the Use of Raw Data
It’s not uncommon for supply chain consultants and project managers to request data in specific formats, usually according to a template.
The problem here though, is the risk that such a template will introduce errors, in the form of miscalculated or missing data.
For that reason alone, it’s a good idea to try and persuade supply chain professionals to let your team provide (or give them access to) raw data relevant to their needs.
Drive Supply Chain Success with High Quality Data
The data your team supplies for logistics and supply chain projects will make a difference to your company’s fortunes.
While expedience is a virtue, it shouldn’t come at the expense of healthy data—a fact which you might occasionally need to impress upon an anxious logistics-project manager or two. Don’t be afraid to do so. They’ll thank you in the end when the right decisions have been made, driven by the high-quality data provided by you and your team.