How AI Improves Transportation Management Systems (TMS)
Dirty data simply refers to any data containing incorrect information. When you think about how dirty data affects your computer algorithms, you might immediately consider viruses, ransomware, and other types of cyberattacks. While this may be true, it isn't always the case for dirty data.
In fact, dirty data is something that you and your supply chain deal with on a daily basis. And most of the time, the real threat comes from errors due to:
- Missing data
- Duplicate data
- Incorrect data
- Outdated data
- Corrupted data files
These issues often result from conflicting rules in your data management system, such as an ERP system.
In this blog post, we'll explore how dirty data affects your supply chain and provide a strategic approach to data cleansing that protects your operations.
Performing a Supply Chain Data Cleanse
It should go without saying, but your supply chain needs to occasionally have its various databases cleaned up and data cleansed.
However, to maintain the ins and outs of your daily operations, this is something that really must be handled strategically and preferably performed in multiple stages. Doing a complete overhaul or data cleanse as it is commonly referred to can put stress on your processes and create additional issues if not handled properly.a0
But when it is, it can help you:
- Better streamline your operations
- Forecast the ebb and flow of demand
- Fill missing critical tasks
- Analyze employee performance
And the list goes on.
Having said that, a full data cleansing could end up shifting the focus of your IT team if anything major shows up, so it is always best to be prepared and to have an understanding of what the data cleanse process ideally looks like before diving in.
Find the Issues
Understanding that the issue with your data may have likely occurred from various internal errors over time will help you to identify and clean the data much more efficiently.
Of course, you should look for dirty data originating from anywhere if it affects your supply chain integrity. By giving yourself a general idea of what may be the easiest to spot first, you avoid a lot of undue stress while also covering a lot of ground quickly.
Instead of overwhelming your supply chain management team, simply break the cleaning process down into smaller steps that allow for a calm, yet meticulous pace.
Team management is essential to getting to the root of the dirty data problem. Management should be cautious not to panic and shift the focus from daily operations to data cleansing as this is more likely to cause more problems than it solves. Instead, prioritize data sets that need to be evaluated. Depending upon the type of data that needs to be cleaned, verified, or updated, assign the task to the appropriate staff.
When your supply chain management team works together in a cohesive manner under one common goal, you will undoubtedly make better progress at cleaning up your data and enabling data-driven decisions.
Multiple Eyes Solve Multiple Problems
Perhaps the best way to clean dirty data from your supply chain is to have multiple IT eyes focusing on the problem.
Too often, when you identify that you have an issue with the ERP data, you might assign one person or one team to find and fix the issues. Apart from this taking an extraordinary amount of time, it also opens the door for issues to arise due to too much strain being placed upon the IT team.
For the best results, have multiple teams handling smaller, more manageable data cleanup tasks. Only those, even in the IT department, who have knowledge of your ERP should be involved in the cleaning process.
Check Your Algorithms Then Let Them Work
Algorithms that handle correctly entered data are essential to the operations and distribution of that data throughout the entirety of your supply chain. Human interference with algorithms is one of a couple of main reasons that data within these programs become corrupted. Having fewer hands on deck means that there will be less potential for data corruption.
Additionally, have IT verify that any of the software processes they manage for the input of data across your supply chain is properly maintained and managed. Algorithms can range from providing data to leveraginga0quantity discounts and cost management, so when it comes to these Information Technology areas of your supply chain, trust your IT team and don't be afraid to lean on them so long as that is an area of their field that they have been trained to oversee.
Cleaning the dirty data within your database so that the algorithms have the correct data to use and distribute involves removing or "scrubbing" any duplicate information from the database. Verifying that information such as bulk agreements, client information, and delivery destinations are correct is crucial for your algorithm to perform in the way you expect it to perform. These algorithms have specific tasks that they are programmed to handle, but they cannot mentally compute or recognize certain information as useless unless it is programmed to do so.
Verify, Clean, Repeat
Cleaning up your supply chain's dirty data isn't a "set it and forget it" ordeal. It is an ongoing effort and if done correctly and maintained well over time, there will never be much of a need for a full-on data purge.
Daily operations and verifications of data is the quickest and simplest way in which to reduce ongoing issues within the database. Simple things such as checking the spelling of all information, counting the inventory being delivered, and then counting again, and taking the time to record all of this data just as carefully with whatever software system you utilize can go a tremendously long way in helping you combat bad data from piling up.
AI Integration
Larger companies have found that certain supply chain operations have benefited from integrating AI and analytics software. When used properly, going the route of AI brings with it a wide variety of benefits that is deserving of its own article in the future.
With AI, the biggest benefit around this topic would have to be that it removes the need for routine manual data cleansing by making decisions and creating intelligent data categories from which to operate with little to no human interaction aside from the initial input of data.
Final Thoughts
Maintaining clean data is essential for supply chain efficiency and accuracy. By implementing strategic data cleansing processes and leveraging modern technology, you can protect your operations from the costly effects of dirty data.
FAQs
What is a supply chain data cleanse?
A supply chain data cleanse is the process of identifying, verifying, and correcting bad data in operational systems so decisions and workflows run on accurate information. In practice, it means removing duplicates, fixing incorrect records, updating outdated entries, and addressing missing or corrupted files. The goal is to keep supply chain data reliable without disrupting day-to-day operations.
Why does dirty data cause problems in supply chain operations?
Dirty data causes problems because it can distort forecasts, interrupt workflows, and lead to poor decisions across the supply chain. Missing, duplicate, incorrect, outdated, or corrupted records can also create issues inside ERP systems, where conflicting rules may spread bad information quickly. Over time, that weakens operational accuracy and makes clean execution harder.
How should you approach a supply chain data cleanse without disrupting operations?
A supply chain data cleanse should be handled in stages, not as a sudden full overhaul. The safest approach is to break the work into smaller tasks, prioritize the most important data sets, and assign them to the right staff. That keeps daily operations moving while reducing stress on IT and supply chain teams.
Who should be involved in cleaning supply chain data?
Multiple teams should be involved, especially IT staff who understand the ERP and the systems that feed it. Smaller, manageable cleanup tasks are more effective than assigning one person or one team to fix everything at once. A coordinated approach helps reduce strain, speeds up progress, and lowers the chance of introducing new errors.
How do algorithms and ERP systems make dirty data worse?
Algorithms and ERP systems can spread dirty data faster when incorrect information is entered or handled manually. If human intervention is too broad, it increases the chance of corruption and duplicate records. Verifying that software processes are maintained properly and scrubbing bad inputs helps algorithms work as intended with correct customer, destination, and agreement data.
Is supply chain data cleansing a one-time project or an ongoing process?
Supply chain data cleansing is an ongoing process, not a one-time project. Regular verification, careful recordkeeping, and simple checks like confirming spelling and inventory counts help prevent bad data from piling up. When data quality is maintained consistently, there is less need for a large-scale purge later.
How can AI help with supply chain data cleansing?
AI can reduce routine manual data cleansing by organizing information into intelligent categories and making decisions with little ongoing human input after the initial setup. Larger companies use AI and analytics to support supply chain operations and improve how data is handled. When applied properly, it can reduce repetitive cleanup work and improve data use.
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