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Why Supply Chain Leaders Are Using Big Data Analytics

Oct. 10, 2014
Manufacturers achieve the best results when they take an enterprise-wide approach to Big Data analytics rather than a process-focused strategy.

Companies today are collecting increasingly massive amounts of data with the help of digital technologies. To make sense of that data, they need new strategies, improved skills and more powerful tools to crunch the numbers, and find the useful insights that are buried in the data. This situation is elevating the importance of Big Data analytics as a critical business capability.

A recent Accenture study, based on a survey of more than 1,000 senior executives (mostly from large global companies), found that while most companies have high expectations for Big Data analytics in their supply chain, many have had difficulty adopting it. In fact, 97% of executives report having an understanding of how Big Data analytics can benefit their supply chain, but only 17% said that they have implemented it in at least one supply chain function.

But increased understanding of Big Data analytics is leading to action, and Big Data analytics is becoming a reality. Our research reveals that 3 out of 10 executives surveyed have an initiative underway to implement analytics in the next six to 12 months, and 37% are in serious talks about the role that analytics could play in their supply chain.

What may be helpful to those implementing or planning to implement Big Data analytics are the commonalities our study found among a small group of respondents that generated a higher return from their investment in Big Data analytics. These leaders reported impressive results – stronger than other respondents. Three key practices distinguished these leading companies from the others—and likely played a strong role in their results.

1. Leaders made developing a robust Big Data analytics enterprise-wide strategy a higher priority.

Companies more frequently realized stronger results when they applied an enterprise-wide strategy as opposed to the process-focused strategy that others implemented. For instance, 61% of those who had an enterprise-wide strategy said Big Data analytics helped them shorten their order-to-delivery cycle times, while only 14% of those using a process-focused strategy saw similar results. Whether they increased their supply chain efficiency by at least 10%, improved their customer relationships, or improved their cost to serve, respondents more frequently reported achieving results when they took an enterprise-wide approach to Big Data analytics as opposed to a process-focused strategy.

However, the application of an enterprise-wide strategy should be underpinned by a clear view of what will help the company create value, differentiate themselves in the market, and gain an understanding of how their industry is evolving or being disrupted. Then they can use those insights to chart the business roadmap that can help them achieve their goals with Big Data analytics.

2. Leaders emphasized embedding Big Data analytics into daily operations to improve decision making.

How Big Data analytics is operationalized is important. Our research has found that embedding analytics into the daily operations can generate significant, far-reaching benefits—more so than when it is applied on an ad hoc basis. For instance, at companies where Big Data analytics were embedded, executives more frequently reported that they’d been able to shorten their order-to-delivery times, increase their supply chain efficiency by at least 10% and even lower their cost to serve.

Operationalizing analytics in this way requires deploying the right tools to support the right processes in the right way. It is important for a company to begin with a clear definition of what it hopes to achieve with Big Data analytics, and remain focused on how it will use the technology to enable specific processes to attain its goals.

3. Leaders hired talent with a mix of deep analytics skills and knowledge of their business and industry.

But as with any new capability, skills need to be considered. Big Data analytics rely on tools and people with the requisite skills to use them to conduct the analysis. Our research found that companies with a team of data scientists tended to get stronger results than those companies that relied on traditional database personnel, whether it was shorter order-to-delivery cycle times, becoming a more demand-driven operation, or even improving the customer-supplier relationship. These people have strong mathematics, statistics and econometrics skills and the ability to create analytical models that are rooted in an understanding of the business.

Creating a competitive edge with Big Data analytics requires a thoughtful approach that melds the technology with the front-end business strategy to help a company sharpen its focus and use the potentially disruptive characteristics of Big Data analytics to their advantage. Companies that get it right have the opportunity to realize tremendous benefits from Big Data analytics with a boost to their business’ bottom-line as well as its overall operating performance.

Mark H. Pearson is the senior managing director of Accenture Strategy’s Operations practice. Pearson, who has more than 26 years of experience with Accenture, focuses on helping clients develop dynamic supply chain and service operations capabilities to more rapidly respond to changing customer demands and market opportunities as they occur. He has degrees in business administration and German from Aston University, Birmingham, UK. He is based in Munich, Germany.

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