Companies today are collecting significantly massive amounts of data with the help of digital technologies. Making sense of that data, they need new techniques, enhanced abilities and more effective tools to crunch the numbers, and find the useful understandings that are buried in the data. This situation is elevating the significance of Big Data analytics as a critical business capability.
A current Accenture study, based on a study of more than 1,000 senior executives (mainly from huge global companies), found that while a lot of companies have high expectations for Big Data analytics in their supply chain, many have had difficulty embracing it. In fact, 97 % of executives report having an understanding of how Big Data analytics can benefit their supply chain, however only 17 % stated that they have executed it in at least one supply chain function.
However increased understanding of Big Data analytics is resulting in action, and Big Data analytics is becoming a reality. Our research study exposes that 3 out of 10 executives surveyed have an effort underway to execute analytics in the next six to 12 months, and 37 % remain in severe talks about the role that analytics could play in their supply chain.
What might be helpful to those executing or planning to implement Big Data analytics are the commonness our research study found among a small group of participants that generated a higher return from their investment in Big Data analytics. These leaders reported outstanding results– more powerful than other respondents. 3 key practices distinguished these leading companies from the others– and most likely played a strong function in their results.
1. Leaders made establishing a robust Big Data analytics enterprise-wide strategy a greater priority. Companies more frequently recognized more powerful outcomes when they used an enterprise-wide strategy rather than the process-focused strategy that others executed. For example, 61 % of those who had an enterprise-wide strategy said Big Data analytics helped them reduce their order-to-delivery cycle times, while just 14 % of those using a process-focused strategy saw comparable results. Whether they increased their supply chain efficiency by at least 10 %, improved their customer relationships, or improved their cost to serve, participants more often reported accomplishing outcomes when they took an enterprise-wide technique to Big Data analytics as opposed to a process-focused strategy.
Nevertheless, the application of an enterprise-wide strategy need to be underpinned by a clear view of what will assist the company produce value, distinguish themselves in the market, and get an understanding of how their industry is developing or being disrupted. Then they can use those understandings to chart the business roadmap that can assist them achieve their goals with Big Data analytics.
2. Leaders emphasized embedding Big Data analytics into daily operations to improve choice 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 used on an ad hoc basis. For instance, at companies where Big Data analytics were embedded, executives more often 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 properly. It is important for a company to begin with a clear definition of what it intends to achieve with Big Data analytics, and continue to be concentrated on how it will use the technology to allow specific processes to achieve its objectives.
3. Leaders hired skill with a mix of deep analytics abilities and knowledge of their business and industry. But as with any new capability, skills need to be considered. Big Data analytics count on tools and people with the requisite skills to use them to conduct the analysis. Our research discovered that companies with a team of data researchers tended to get more powerful results than those companies that depend on traditional database personnel, whether it was shorter order-to-delivery cycle times, becoming a more demand-driven operation, or perhaps enhancing the customer-supplier relationship. These individuals have strong mathematics, statistics and econometrics skills and the ability to develop analytical models that are rooted in an understanding of the business.
Browsing the Complexity of Big Data Analytics
Why exists a disparity? Numerous don’t understand where to start. If you’re like me, even the term “Big Data” is at least a little intimidating, if not annoying. Like “cloud” is to “online” and “omnichannel” is to “multi-channel,” “Big Data” is a marketing buzz term that refers to something extremely familiar to everybody supply chain geeks– collections of data records so large that it makes your Excel run and hide. It’s not new to the logistics sector, but it’s on steroids and something needs to be done about it.
Stay in the Present, Not in the Past
The key to Big Data is real-time analytics. This complements the end-to-end visibility of your supply chain and enables you to act quickly enough to prevent profits and earnings loss that can happen at various points in the supply chain. Here are three areas of your supply chain that are ripe for Big Data analytics:
Forecasting: Inaccurate forecasting = trampled success. If you make excessive, you’re losing cash and losing revenue. If you make too little, you’re missing revenue. If you make it at the wrong time, you’re probably getting struck by all 3. By executing fluid need and supply plans that are upgraded in real-time, based upon true demand signals, material accessibility and capacity, your revenue and profit potential is taken full advantage of.
Shipping: By integrating real-time data analytics into your plan, disasters can be mitigated. By tracking products with GPS, you can know exactly where a shipment is in the event it needs to be re-routed or a delivery is lost. New routes can be identified rapidly and effectively. If a port strike occurs as your items are entering port, you’ll already know this due to your tracking technologies. Analytics can help figure out the closest available port and path to take, simulating potential options and preventing your products from sitting stagnant for two weeks at sea.
Warehousing: Analytics on warehouse layout, product inventory and demand can help optimize operations within the warehouse, with simulations preventing physical resources from being expended before the plan is finalized. Once a solution is enacted in the physical warehouse, rules can be applied to ensure management is alerted to depleted inventory or potential roadblocks.
Summary
Creating an one-upmanship 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 attributes of Big Data analytics to their advantage. Companies that get it right have the opportunity to realize remarkable benefits from Big Data analytics with a boost to their business’ bottom-line along with its general operating performance.
