These days we expect information to materialize at our fingertips, and it usually does. From Google to ChatGPT, nuanced answers are just a few keystrokes away. Accurate answers? That’s another story. Google results are only as solid as the content that already exists online, and ChatGPT’s uncanny outputs draw upon pre-existing material.
Even with the most advanced technology at play, neither source can pull information from thin air, and what comes out is only as good as what goes in. This same principle applies to your Service Supply Chain and data. No matter how you go about forecasting which parts to order and what moves to make, data feeds your strategy. The cleaner the data, the more accurate your forecast.
Our recent whitepaper, Data’s Role in Digital Transformation for Modern Service Supply Chains, covers how data can make or break your strategy. We also summarized this whitepaper into a two-part blog series. The post you’re reading now will help you identify if and how your organization could be dealing with bad data. Part two explains how to keep your data clean. For a deeper dive, read the full whitepaper.
Supply Chain Data
Clean data is accurate and complete: a postal code with all the right digits, for example. Bad data is inevitable and comes from human errors and omissions. That said, humans are also responsible for catching these mistakes, and the more eyes, the better.
When it comes to data ownership, you must ask questions like, “Who has visibility into your data and who can update it?” If one of your planners catches a mistake in your installed base (IB) data, but they do not have the agency to change it, they need a sense of urgency around passing that information along to the right party. This sense of urgency comes from understanding the profound impact a single data point can have on your organization.
Data in Service Parts Planning
So how exactly does data impact your organization? In the context of planning, data is used for forecasting, calculating target stock levels, generating orders and transactions, analysis, and informed decision making. These actions bleed into each other, and so does the data informing them. Available data, such as demand history or IB and service level agreement (SLA) data is used to calculate a demand forecast. The demand forecast, combined with other SLA data and figures like part, product, and customer criticality go into calculating target stock levels. The target stock levels, plus other factors like on-hand and on-order data can help determine necessary orders (replenishment, redeployment, repair, and purchase). Some of those orders, such as replenishment, feed into your demand forecast calculation — and the loop continues. This interdependent process means that any bad data will go round and round through the system and potentially beget more bad data.
Bad Data: A Look at the Layers
Sometimes bad data glares out at you like a postal code with letters in it. More often it hides in a murky mix with clean data. To root out bad data, you need to get granular. “Missing data” can look like data you did not even know to capture in the first place. For example, this could be a code that differentiates types of orders. You may think of “customer demand” data as its own complete category, when in reality, it has multiple layers:
- Same-day demand
- Next-day demand
- Replenishment demand
- Sales (direct-to-customer) demand
- Sales (on open market) demand
If you capture clean and accurate data for each of the above demand streams, your aggregate forecast will be much more reliable.
Service Execution with Bad Data
The business cost of bad data is a bad forecast, a bad plan, and bad execution. These ultimately lead to higher costs to support customers and their SLAs in the form of:
- Overstocking inventory
- Expedited transportation costs
- Courier charges
- Technician return trips
It also makes it tough to meet time-sensitive service contracts, which leads to customer dissatisfaction and brings in costs like:
- Real penalties for service not provided in accordance with the SLA
- Reduced or lost revenue on future service contracts
- Increased escalations for your team to manage
- Damage to your organization’s reputation
At the end of the day, it all leads back to cost. Whether it’s physical costs going up or the cost of a dissatisfied customer, the negative impact remains. Ready to take action to avoid these unnecessary costs?
Now that we’ve discussed the implications of bad data, it’s time to talk about solutions. Check out part two of this blog series for tips on how to keep your data clean, or read our full data cleanliness whitepaper here