Data Integrity Means a Stronger Supply Chain

Real-time decision-making in your supply chain is a hit-and-miss affair when the data it is based on is unreliable, error-ridden, or just plain wrong.   The bigger the database, the more the mistakes are multiplied.   Missing or incorrect product codes, lack of units of measure, and currency issues can create major problems. Supplier and customer name-and-addresses need to be in a defined format, if they are not, management reports will be of minimal use. A classic example is IBM, it can be loaded on the supplier master in 5 different styles!   Time spent on scratching for information and redoing data analyses is expensive and a drain on productivity. Bad decisions not only create delays, they lose you customers.

Data accuracy

Accurate means correct.  Companies that place a high value on data integrity have a competitive edge.  Accurate inventory reports are crucial.   If an item is out-of-stock but shown as available to customers, this is worse than just a recording error.  Customers will be unhappy; sales and reputations will suffer.  In an omnichannel business where inventory must be tracked in real-time, accuracy is non-negotiable.

 Product descriptions and definitions must be standard across the entire business and with supply chain partners to achieve maximum benefit from any sale.  This needs constant surveillance, even promotions cause variations in product codes.  Weights and dimensions are often misconstrued if they are not clearly defined and shared.  Vehicle size and height restrictions at customers’ premises is front of mind for transport schedulers, a dispatch supervisor may not realize its importance.  Dun & Bradstreet says supply chain managers who fail to update transporter and shipper databases are at risk of losing money this year. According to their new report, “The Past, Present, and Future of Data, almost 20 per cent of businesses have lost a customer due to using incomplete or inaccurate information about them.

           Accurate data is everyone’s responsibility: your data is my data. 

Consistency of data    

Consistent means everyone is working to the same standards.  What does 03.06.21 mean to you?   It could also mean 6th March to others.  Using commonly agreed naming conventions is important.  Are we talking about pants, slacks, or trousers?  Does Invoice Date refer to the date the supplier put on the invoice, or the date the invoice was received?  A customised data “dictionary”, defining the terminology to be used should be shared with suppliers, logistics partners and others.  This will eliminate confusion and minimise problems with data analysis and reporting.

Implementing new technology

Raw or uncleansed data is a perennial problem for all supply chain operations from procurement through to final logistics.   Attempting to implement new software solutions where the data is not reliable causes delays and incurs additional costs.  According to a recent PwC survey, one-third of the companies say that “dirty data” forced them to delay or scrap a new system.

Failing to validate and prepare the data properly before committing to a new system (or upgrade to an existing one) has major repercussions.  It is especially important if advanced digital applications such as machine learning or blockchain are embedded in the proposed automated solution.  To assume that the system vendor will tackle the mini-project of cleansing your data may be unwise.  Even if the service is offered it will come at a cost that you have not budgeted for.  If it is discovered that data integrity is a problem during implementation, then the project may be set up to fail due to a lack of commitment.  Costs have escalated, it is all taking too long, and energy levels are low. Projects are often abandoned right here. 

Steps to improving the quality of your data 

  1. Establish the correct processes and rules to prevent bad data from entering the system. Start with key suppliers and partners whose data is critical to smooth supply chain operations.
  2. Provide clarity on data ownership. Who manages what? 
  3. Clean up the current data based on 1 above. Focus on the most important data categories especially relating to customers.
  4. Set up a validation method to make sure that humans entering new data into the system do it correctly.
  5. Continuously monitor the data to ensure it stays clean. There are no short cuts.

It may seem like a daunting task to ensure that only quality data enters your systems but tackling it upfront saves money later.  The global economy is still struggling. To meet customer expectations and manage our suppliers and partners successfully we need to collaborate on improving data quality.

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