7 Avoidable Data Errors in Your Supply Chain Data

The Devil is in the Data Details: Entering a New Global Market

So you’re about to launch into a new global market. It’s thrilling, full of potential, and has its own set of opportunities and challenges. One of these challenges that you might not think of immediately is big data in your supply chain—specifically the quality of your data. What might seem like a minor detail, such as differing measurement systems, can dramatically affect how effectively your supply chain functions and how well products move to market. Here are 7 data issues you might need to watch out for:

7. Currency

This is probably an obvious one. You’ll need currency conversions at sales points, but you’ll also benefit from accurate currency conversions at your assessment and planning levels.  For example, tracking revenue in each location’s currency helps you to assess those values within that market context. Failing to accurately convert costs into the same currency as the price for a product can lead to inaccurate margin calculations and erroneous profit values.

6. Measurement System

This can vary from using the metric system instead of imperial measurements to using the right shipping dimensions. Inconsistent and incorrect dimension data can result in products that don’t fit into shipping containers, that are properly processed at distribution centers, or that fit on store shelves. Improper configuring of shipments can also lead to issues. For example, you might have ordered 100 items with the expected configuration of 4 boxes of 250 items. If you receive those 100 items in a 10 x 10 configuration, that order doesn’t exist with that configuration or date in your system, so it can’t be processed at your warehouses. You end up with ghost inventory – excess inventory that physically piles up at your distribution centers but doesn’t exist in your system of record.

5. Clear Communication with your Suppliers

Something as straightforward as shipping dates can wreak havoc on your supply chain if you and your suppliers are not clear on what a shipping date actually means. For your company, a shipping date might mean the date the shipment arrives at your distribution center. If your supplier takes the shipping date to be the date they ship the order out, then you can end up with late stock, stalled orders with long lead times, or shipping and delivery delays.

4. Tariff codes missing or incomplete

To stock products, you need to enter information about each item into an ERP. It could be dozens of fields for a single product, and the system requires correct data to function properly and ensure products move as anticipated.

3. Inappropriate Forecasting

In a brand new market, you have no historical data to generate any sales or other types of forecasts. While it might be tempting to substitute historical data from what seems to be a similar market on the surface, you may actually get stuck with highly inaccurate and misleading forecasts guiding your business decisions. For example, Ottawa and Calgary are two Canadian cities with similar-sized populations. While they seem to have some parallels (same country, similar population size, some language requirements) they are very different markets and what works in one city might not work in the other. Using historical data from one city to generate forecasts for the other city is random. This method doesn’t even account for the fact that you are a newcomer in the market and have to draw customers from established rivals.

2. Vague or inconsistent data

Accuracy matters. Whether it is about numerical values or product descriptions — accuracy matters. Just as miscalculating the available date for a product can hamper your sales cycle or entire value chain; vague descriptions, missing information, and typos can also be detrimental. Providing too little or no information can lead to confusion, wasted time, and possible mistakes. Typos are mistakes, plain and simple, and can be very difficult to discover after they are in your system.

1. Incompatible systems

Communication and the flow of data between different systems needs to be smooth, reliable, and functional. If the data at your distribution centers cannot sync with orders in your ERP, product will not move, orders will not be filled, and your customers will not be satisfied. Systems that are incompatible will inevitably lead to complications in your supply chain operations. To conclude, the quality of your data can impact your entire value chain – from confused shipping dates to inaccurate forecasts to ghost inventory. In a new market, if enough of these data quality issues are compounded, they can severely damage your value chain, your reputation, and your business. The devil is truly in the details.