The High Cost of Bad Data in Supply Chain Management

Supply chain management relies on data for everything from managing vendors to forecasting revenue. Unfortunately, the sheer volume of available data coupled with disconnected, disparate systems, makes it extremely difficult for logistics management teams and senior supply chain leadership. It is near impossible to compare, contrast, and make informed, timely decisions. 

Growing bodies of unstructured and structured data have amplified an existing problem: many organizations don’t have healthy or hygienic data management processes in the first place. When there’s more data to deal with, what was an inefficiency could become a catastrophe.

The Data Warehousing Institute estimates that bad, inaccurate, dirty or missing data costs companies more than $600 billion a year. In 2021, supply chain disruptions made regular headlines, amounting to a $228 million loss in the United States alone. Clean, collected, and normalized data in one central location makes it possible to navigate the challenges of shortages, bottlenecks, seasonal demands, and more. This has the positive outcome of mitigating risk and avoiding loss. 

All of this knowledge is contained in data, and, often, the people in charge of supply chain management — and even more so in transportation logistics management — don’t have access to it, or can’t make sense of it. There is too much data spread across disparate and disconnected systems, buried in spreadsheets or email chains, and locked up in black boxes: it can feel like an insurmountable problem. 

Solutions exist, but the most pressing point to come to terms with is that bad data in supply chain management has a high cost, and if businesses don’t adapt, they are paying the cost in terms of performance and unnecessary real cost.

Supply Chain Management

Effective supply chain management is crucial to reducing inefficiencies, streamlining workflow, and optimizing processes to control and reduce the total cost to serve. There are inherent challenges to working with multiple vendors and data management systems, but most often, problems with data have evolved over time.

Often, companies begin with one system to meet a specific need, then grow to have dozens of Enterprise Resource Planning (ERP) tools, several order management systems or transportation management systems, and even homegrown accounting systems. The bolt-on approach to get solutions for an immediate demand is a habit supply chain management teams need to shed, and fast.

Carrier Data Management

While there are many types of data that supply chain teams need to wrangle, carrier data is at the top of the list. 

Researchers from HICX surveyed senior procurement professionals at leading companies from around the world. Their take? 89% reported that they did not have total oversight over vendors or carrier data. Similarly, a Deloitte report uncovered the reality that 65% of procurement leaders have little to no visibility beyond Tier-1 suppliers. This creates hidden, slow-drip costs, because suppliers generate a high number of transactions over long periods of time. 

Two other important points can be made concerning vendor management and data:

1. Carrier data is typically subservient to ERP, a system which may not effectively or accurately integrate or deliver data. This can lead to obfuscation or complete misses of vital information.
2. Carrier data comes from different departments, is used in different contexts, varies in format, and could be distorted or damaged as it is passed around. This amounts to both internal and external costs in building, managing, or correcting databases as well as investing in outsourced data audits or cleansing.

If a team spends 20-50% of their time finding errors, confirming data, or filtering through unstructured data, that is a significant use of man hours and productivity. In freight audit and payment (FAP), we find that upwards of all transportation invoices are wrong, and most of the time that is related to bad data. Even if data management systems are operable, they often aren’t fully connected enough to leverage for efficiency, cost reduction, and data-driven insights. 

While the risks and cost are clear, ours is an industry in which time-tested tactics often win out. In other words, if the idea of implementing better technology for managing data requires change, leaders in our space will want to evaluate whether it’s worth it.

Supply Chain Data

It is worth it. 

Data quality is inarguably critical to the supply chain function. A study by NC State University found that “siloed data, lack of standards, and lack of skills. . . are at the root of poor data quality, [a problem which] suggests that data and analytics roles are being starved of the resources needed to fix them.” 

Overwhelmingly, data governance is a key performance indicator in the marketplace, with companies that make a strategic investment in data management winning out.

The High Value of Data in the Supply Chain

What’s at stake and what bad data is costing companies is crystal clear, and a point of concern. 

It’s equally important to understand the possibilities unlocked by better data management, which include:

• Creating visibility that makes teams more agile and drives accurate, real time decision-making
• Accelerating time to market by diminishing slow, manual processes
• Mitigating risk through enhanced transparency and assurance of alignment with governance standards
• Improving customer loyalty and trust by providing on-demand, tangible proof of corporate responsibility and sustainable sourcing or practices
• Having solid data management enables companies to scale as they grow operations globally.

Data quality, standardization, and governance reduce some of the biggest challenges to businesses, and yield measurable dividends.

So, this must be done. But how?

How to Improve Data Management

Supply chain analysis turns data of any kind into actionable insights. The full impact of data is unleashed when you address two primary factors:

1. Capabilities
2. Process

Capabilities

Supply chain managers are often not equipped with the technical skills of data scientists. The good news is, they don’t need to be. It is possible to use freight data management resources to overcome capability restrictions. Tools like those offered by Trax empower supply chain managers and their teams with centralized information, making it possible to see more and do more in less time.

Process

Having a structured process for data management is also important. Analytics use data as well as quantitative assessments and methods. The goal is to expand analysis beyond ERP or supply chain management (SCM) systems, leveraging the power of technology to more quickly get to “the point” of what all of the data is saying, and make strategic decisions based on it. 

An elite data management process can identify opportunities, support supplier risk management, inform inventory projections and scenario planning, provide forecasting accuracy, and so much more. 

Trax Technology for Better Supply Chain Data Management

Freight data management is the starting block in the race toward better supply chain data management. Optimizing freight audit and payment processes is an essential first step in creating a ready bank of easy-to-find and easy-to-interpret data.

From there, businesses are on a journey toward Transportation Spend Maturity. This is achieved by ongoing data normalization and cleansing, which are key elements of continuous improvement and enhanced data management.

Ready to clean out the clutter and create better data management processes for your supply chain management team? Contact Trax to learn more.