Artificial intelligence and advanced analytics can deliver powerful insights – but only if the data is right. In this Supply Chain Byte, Lisa Anderson explains why manufacturers must get back to the basics of data before relying on AI-driven decisions.
We have been working with several large, successful clients that have struggled with data. A few of the challenges include:
- Missing data: Relying on reports with missing information is a large problem. It could be that the data was never entered or it is missing a key field and so doesn’t get picked up. Either way, the totals and conclusions reached from the data are incorrect.
- Incorrectly filtered: It is surprising how many clients reach conclusions based on incorrectly filtered information. It seems like an easy solution, but it happens at least 80% of time during client projects as it is easy to get confused about the sequencing of filters or forget which filters you have on.
- Different calculations: It is also common to have different calculations for different sites, products, customers, etc. The lack of standardized calculations can cause significant issues.
- Data dumps: In certain clients, they provide data dumps with no ability to make sense of the information. On the other end of the spectrum, they do too much manipulation to the data, and it only represents a narrow slice of what is required.
- Activity based data vs outcomes: Although it is important to manage key activities, it is easy for clients to get caught up in activity-based reporting that does not correlate to outcomes.
- Backwards vs forwards: Most of the time, clients are looking at historical data which is important; however, even more important is to look forward at predictive models such as sales forecasts and SIOP (Sales Inventory Operations Planning) models inclusive of capacity and purchase plans. Lately, the lack of proactive views into purchasing has created absolute havoc and poor service in several clients.
From reports and filters to data sources, data dumps, activity-based data, backward-looking data and forward-looking insights, accuracy matters. When the data is wrong, the decisions will be wrong. Sometimes, the smartest way to move forward is to strengthen the foundation first.
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