How Bad Supplier Data Can Cost You

For companies striving for growth, the choice to deploy AI/ML tools that can find actual information on suppliers is no longer a nice-to-have. It's a part of the business.

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Many supply chain experts predict there's no end in sight for the logistical disruptions that have gripped businesses since the beginning of the pandemic. In that time, countless business and procurement leaders have learned the hard way that their procurement process is ill-equipped for extended interruptions and delays.

Some leaders in the procurement world believe that all organizations — regardless of their preparedness — will face possibly insurmountable supply chain issues next year. In fact, 77% of sourcing and procurement leaders are worried their supplier data intelligence has remained the same since before the pandemic. Yet improved supplier data intelligence should be a top priority for all industries. After all, if the past two years have taught leaders anything, it’s the importance of agility. On top of this concern, inflation has taken a serious toll on the procurement industry. In October 2022, the consumer price index increased by 7.7%, compared to 0.4% the year before.

The procurement process has become significantly more complex over the last decade. Pair this fact with recent geopolitical events, including multiple industry shutdowns, the global pandemic, a risky economy and faulty data that has no place in the supply chain world — how does a company manage the risks involved within the supply chain industry? First, they will need to step back and acknowledge where their procurement software is failing. Now more than ever, data ingestion, processing and analysis must be accurate and thorough.

Using defective supplier data for sourcing has many adverse outcomes. Research by Wakefield cites the following as the top consequences companies face after using poor supplier data.

●     56% of managers have faced delays in projects

●      50% of delays have forced budgets to be exceeded

●      43% of delays have resulted in companies failing to meet customer demand

Every surveyed procurement manager reported dealing with at least one of these outcomes due to turmoil in the supplier sourcing and procurement process.

If these issues are not addressed soon, many organizations will be negatively impacted, and the supply chain will continue to suffer under industry headwinds. Think of it like this: searching for suppliers is similar to using a phone book. Finding someone's number with a phonebook takes longer than finding it on a smartphone. And just like faulty, outdated supplier data, there's no guarantee the phonebook even has the right number. But fortunately, finding new suppliers efficiently is possible and more accessible than ever before, as long as procurement leaders are willing to upgrade their tech stack.

Performance impacts of inaccurate data

Inaccurate data has a far-reaching and negative effect on business performance. Supply chain managers spend an astounding amount of time mining for new and up-to-date supplier information yet 82% of procurement and sourcing executives are not confident that their supplier data is accurate. The average supply chain manager spends 45 minutes per day searching for updated supplier information. Over time, that's hours of time wasted that could have been allotted for work that would positively impact the company. Add to that the possibility of project delay based on the upfront costs of searching through manual data, and procurement leaders are left with a less-than ideal situation, to say the least.

Meanwhile, 41% of companies have lost out on new business or projects because of inefficient data processes. These disruptions have shined a spotlight on the supply chain industry, with consumers now becoming more aware of supply chain inefficiencies.

That's why it's more important than ever before to prioritize robust supplier data and ensure that it is accurate and up-to-date. If not, there will be tremendous losses, both customer-wise and financially.

Boosting business growth and efficiency with trusted data

There are a few ways poor supplier data can surface. The most common example of problematic supplier data is outdated information, which can lead to erroneous spend decisions and supply delays. Unfortunately, manually updated data is often outdated, as the onus for updating rests solely with suppliers. Lack of visibility prevents procurement professionals from determining whether they’re operating on correct data. This has a profoundly negative impact on the industry as a whole, as well as the integrity that customers expect from their suppliers.

Thankfully, there is a way to fix the inaccurate supplier information problem. The key is artificial intelligence (AI) and machine learning (ML) tools that expedite the sourcing for supplier processes. AI and ML tools extract supplier data from millions of available sources and compile it within one platform. The same tools that collect the data can also update supplier information in near real-time to ensure the data is up-to-date and accurate, all while integrating with existing source-to-pay (S2P) systems. These integrations drastically cut down the amount of time and effort supply chain managers spend searching for and managing information. AI and ML tools also allow procurement and supply chain management teams to make changes to their suppliers quickly to keep up with the unstable state of the supply chain.

With the myriad changes that companies have had to make over the past few years, the likelihood that supplier data is robust and accurate is low. For companies striving for growth, the choice to deploy AI/ML tools that can find actual information on suppliers is no longer a nice-to-have. It's a part of the business. Decision makers within the procurement and supply chain world need to understand the potential for time and cost savings that these tools provide. Organizations that take the step to adopt AI/ML supplier data tools will be one step ahead of their competitors who spend countless hours and resources combing through the manual data phonebook.