• Barcoding
  • Data Collection
  • Oracle SCM Cloud

Garbage In, Garbage Out: How Small Errors Cause Big Problems in the Supply Chain

Written by Mark Gemberling
March 5, 2020


  • Modern ERPs, like Oracle Cloud, are built on data. But if that data is inaccurate, then decisions based on that data will likewise be inaccurate.
  • Manual data collection processes allow low-quality data to enter your operation, which can have significant impact downstream.
  • Modern technologies like mobile data collection ensure only high-quality data enters your ERP environment with 99.9% accuracy or greater.

When you update your ERP, it’s also important to consider the quality of data that gets entered into it each day.

When you update your ERP, it’s also important to consider the quality of data that gets entered into it each day.

Data serves as the foundation for today’s digital world. Many of us operate with the assumption that this data is accurate and so we don’t question its validity. Important business decision are made based on this valuable data.

Often, we don’t realize data values are incorrect until after they’ve made it into the ERP or business environment – if we ever realize it at all.

If you don’t intentionally input low-quality “garbage” data into your ERP, then where does it come from?

The front door, aka data collection. That is, the point or process where your data is collected and communicated to the ERP database.

How Manual Data Collection Lets Errors In

If you still rely on paper-based recording processes, manual data entry into spreadsheets or human memory (“tribal knowledge”) to keep track of data before your workers input it into your ERP at a computer workstation, inaccurate data will creep into your supply chain, often without you realizing it.

Manual data collection can only achieve about 60% accuracy. That leaves a lot of room for error and guesswork.

This problem may originate with frontline workers who receive shipments, interpret and transcribe handwritten sheets into the ERP or handle inventory. Managers or IT may not immediately recognize these inefficiencies. Meanwhile, frontline workers don’t have access to the entire picture, so they don’t realize the big effects these small problems are creating.

Fig. 1.

Figure one explains how this can happen:

  1. Your receiving team processes an incoming pallet. Unfortunately, there is a large backlog, so the new material sits on the receiving dock for two days. When your receiving clerk finally gets to the shipment, they may hastily scribble down an incorrect item number on the print-out before handing it off.
  2. The receiving clerk then commutes to the workstation, where another clerk takes the paper, incorrectly interprets the handwriting, and also incorrectly transposes an item quantity when keying the receiving slip into the ERP.
  3. Meanwhile, the manager receives a high priority production order for an important customer. They look in the ERP and finds they don’t have the raw materials required.
  4. Not trusting the system, the manager sends a worker to double-check the physical inventory on the shelves. After an hour, the worker returns empty-handed. (The material is there, but since the ERP doesn’t know that, the manager and their team doesn’t know.) The manager rush orders a new shipment.
  5. When the new rushed order arrives at receiving, the manager hurries the pallet into production. The order is completed and delivered. When the order arrives to the customer, it may be late, the contents incorrect or the label non-compliant.
  6. Dissatisfied with an incorrect order, the customer returns it with a chargeback, eroding the customer’s trust in your business and cutting into revenue. In the meantime, the missing materials are processed in receiving and put into storage. No longer needed, they turn into a write-off.

Using manual data collection, inventory processes are too slow, opaque and inefficient for the manager to make the correct decision because the data driving that decision was inaccurate and outdated.

Read More: 7 Hidden Leaks of Outdated Manual Processes »

How Mobile Data Collection Keeps Bad Data Out

Fig. 2.

Mobile data collection is an industry-best practice that combines automated data capture (ADC) with enterprise mobility and barcoding technology to eliminate opportunities of human error in data collection. Employees scan barcodes to instantly capture inventory data or transact materials, which is then communicated to the ERP in real time with validation steps along the way. The end result is world-class accuracy up to 99.999%.

In this way, mobile data collection keeps errors out of your environment. Validations along the way ensure new errors don’t pop up as inventory is transacted during receiving, putaway, transfer, production, shipping and beyond.

Figure two illustrates the previous scenario, but this time using mobile data collection:

  1. Your receiving team processes an incoming pallet. Since the receiving team scans barcodes from mobile devices to process materials, received inventory is automatically updated in your ERP. As a result, there is no backlog.
  2. (Step two is eliminated.)
  3. Meanwhile, the manager receives a high priority production order for an important customer. They look in the ERP to find the raw materials needed are sitting at the receiving dock awaiting putaway. This time, she can trust that the inventory is where the ERP says it is. She immediately puts the materials into production and sends them to the customer.
  4. (Step four is eliminated.)
  5. The order is completed and delivered. When the order arrives to the customer, it is either on-time or early, and includes the correct contents, quantities and compliant labels.
  6. (Step six is eliminated.)

The manager was able to make the correct decision and react agilely because the data she used was accurate and up-to-date – thanks to mobile data collection.

Mobile Data Collection as a Best Practice

Mobile data collection technologies, like mobile barcoding, provide built-in best practices because they integrate decades of supply chain and ERP experience into their architecture.

With manual data collection, you just can’t trust that your data is correct, even with a modern ERP like Oracle Cloud.

With mobile data collection, however, you eliminate the lag time between when work is performed and when it’s updated in your ERP. With built-in validation steps along the way, you can automatically check, double check and re-check your data to keep it accurate without any additional labor or time-wasting manual recounts. This creates perfect or near-perfect data accuracy.

Since you can trust that your data is high quality at all stages, you can now make more informed business decisions and more strategically grow and scale operations.

In addition to enforcing perfect or near-perfect data accuracy, mobile data collection provides numerous other operational benefits:

  • Automatically creates a transparent and traceable path
  • Ensures data is timely and up to date with real-time transactions
  • Increases worker productivity by 30% or more
  • Drives workflow efficiency by 25% or greater with the same labor force
  • Minimizes employee wait/walk times and admin overhead
  • Is flexible, extensible and scalable

Increasing Data Accuracy with Mobile Data Collection

Modern ERPs, like Oracle SCM Cloud, are built on data. It’s crucial that you can trust that data’s accuracy to oversee and control stock levels, inventory movements, and make important decisions.

When low quality data enters your ERP, it has negative downstream effects across production, order fulfillment, analytics, customer service and overall decision making. If you use the wrong part to fix a machine and it subsequently breaks down, it can halt production and throw timelines into disarray. The same thing happens with low quality (garbage) data – but to your business plan. And just as that machine may be irreparably damaged because of the wrong part, so too could that plan, or at least be cumbersome to fix or reconcile after the fact.

If you have been letting “garbage” data into your ERP, even unintentionally, then it’s time you examined your data collection practices.

Once you can trust your data, you can more easily make smart supply chain decisions for your business.