6 Ways Big Data is enhancing the global supply chain

30Aug - by aiuniverse - 3 - In Artificial Intelligence Big Data

Source – logisticsmgmt.com

Defined as the massive volume of structured and unstructured data that can’t possibly be processed using traditional software or database strategies, Big Data is affecting every corner of the business world. It’s no surprise, really, seeing that more data has been created in the past two years than in the entire history of the human race. By 2020, roughly 1.7 megabytes of new information will be created for every second for every human being and, at that point, the digital universe will be 44 zettabytes strong (up from a current 4.4 zettabytes).

As supply chain managers scramble to wrap their arms around the reams of information now at their fingertips, a growing number of software providers are making the task more manageable and useful. In other words, simply having the data at your avail isn’t enough; it’s about taking that information and transforming it into actionable insights that help drive operational efficiencies across the supply chain.

“Supply chains are more complex than ever, and with these complexities come many challenges,” says Shannon Vaillancourt, president at RateLinx. “Big Data allows companies to diagnose the issue so they truly understand what is causing it.” Of course, capturing the data and then using it to make good decisions are two entirely different things. To help fill that “gap,” Vaillancourt says software developers are focusing on the 5 Vs of Big Data: variety, velocity, veracity, volume and value.

Vaillancourt says the final “v” is extremely important and often overlooked. “Companies need to be looking for software that turns all of their data into value—or, actionable,” he points out. “Actionable data is created through analytics; it’s the analytics that tells the user what to do, and ultimately what action to take.”


Top 20 Supply Chain Managment Sofware Suppliers

SCM (SCE, SCP, Procurement) Total Software Revenue

No. Supplier 2015 Revenue 2016 Revenue SCP WMS MES/MRP TMS Procurement Website
1 SAP 2,666.80 2,932.40 x x x x x sap.com
2 Oracle 1,447.80 1,552.90 x x x x x oracle.com
3 JDA Software 467.8 475.9 x x x jda.com
4 Infor Global Solutions 105.5 243.3 x x x x x infor.com
5 Manhattan Associates 209.3 218.8 x x x manh.com
6 Epicor 162.1 191.6 x x x x epicor.com
7 Descartes Systems Group 145.3 159.2 x descartes.com
8 HighJump 129.7 134.9 x x x highjump.com
9 Basware 112.6 122.3 x basware.com
10 Coupa 72.4 114.3 x coupa.com
11 IBM 126.6 112 x x ibm.com
12 PTC 105.8 104.6 x x ptc.com
13 Dassault Systemes 74.9 92.9 x x x 3ds.com
14 BluJay 76.6 85.8 x blujaysolutions.com
15 Jaggaer 82.2 84 x jaggaer.com
16 Kinaxis 66.3 82.8 x kinaxis.com
17 Perfect Commerce 44.5 72 x perfect.com
18 e2open 57.7 69.8 x x x e2open.com
19 Zycus 49.4 65 x zycus.com
20 GEP 55 63.3 x gep.com

Following are six big ways that Big Data is affecting the supply chain and helping companies take the right actions.

1. Get better diagnostic information.

To solve problems and circumvent future challenges, companies need good diagnostic data. Big Datagives them that, according to Vaillancourt, while also ensuring that their future strategies are based on solid historical information. “Big Data can help companies diagnose many issues, which will in turn allow them to develop strategies to solve the issues,” he says, “and then ultimately deploy the strategies successfully.”

For example, the organization that wants to leverage Big Data for track and trace of its products can do so by combining the purchase order (PO) details, shipment information and the carrier’s tracking information. Then, once that data is standardized and cleansed, analytics can be applied to it in a way that truly makes the information actionable. “If the analytics notifies the user about a late shipment before the carrier issues the notification,” Vaillancourt explains, “then that user can enact a contingency plan and get the product faster from an alternate source.”

2. Get a clearer “crystal ball” for the future.

Defined as the data mining, statistics, modeling, machine learning, and artificial intelligence used to analyze current data to make predictions about the future, predictive analytics is the modern-day supply chain manager’s crystal ball. “Predictive analytics makes it possible to analyze data and create assumptions as to what will happen to not only predict the future, but influence it as well,” says Marcell Vollmer, chief digital officer at SAP Ariba.

In Kansas City, for instance, a local police department is using data to stop crime before it happens by identifying “hot spots,” patrolling those areas more aggressively and then more closely monitoring the activities of recent parolees. In the business world, predictive analytics is allowing firms to more clearly understand customer needs and adapt their business to accommodate them. Take pricing, for example. Using predictive analytics, companies can predict equilibrium before releasing a new product, thus maximizing the revenue of the solution out of the gate while also understanding future demand. “Data is the new currency,” Vollmer adds, “and predictive analytics is the key to collecting the dividends it pays.”

3. Manage external factors that are beyond your control.

External factors can have a substantial impact on supply chains, yet in many cases these outside forces are hard to control and even detect. “From weather to oil prices to consumer demand, supply chain executives who can quantify and anticipate such impact can better plan their materials and inventory,” says Rich Wagner, CEO at Prevedere. He says retailers are particularly well positioned to leverage this advantage, namely because they’re operating in a dynamic environment where consumers expect quick, accurate deliveries. “If a product is unavailable, manufacturers and retailers alike risk not only losing a customer forever, but also a digital media backlash,” Wagner points out. How can Big Data help? By helping firms better predict demand, and therefore better plan their inventory to mitigate against shortages. The same benefits apply on a global scale, where both supply chains and operations are becoming more interconnected and, subsequently, more impacted by world events. “By coupling Big Data with predictive analytics,” Wagner says, “it’s quite possible to keep a handle on numerous economic and consumer behavior metrics to be better prepared for what’s coming next.”

4. Make more profitable supply chain demand forecasts.

Access to global data, combined with the power of Cloud computing, is giving technology more power to tackle even the toughest supply chain challenges. “With today’s advancements in machine learning, companies can use technology to constantly monitor those external forces,” says Wagner, “and get a real-time view of what’s ahead.” He sees this as a fundamental change in demand planning—compared to traditional forecasts built on past performance with the assumption of stable economic conditions. “Executives know that they can’t rely on precedence and they need insights to make decisions about the future with certainty,” says Wagner. “This desire to be immediately notified of shifts in momentum is now a reality.” For example, one global beverage manufacturer saved about $9 million by improving product distribution through predictive demand models. “The manufacturer realized that external factors (e.g., the architectural billings index) were leading indicators of performance,” says Wagner, “so it adapted its supply chain planning across 400 brands and 21 distributors.”

5. Reduce demand variability and cycle times.

Big Data is turning supply chain managers into “mind readers,” allowing them to predict and react to buyer behaviors in new ways. On the demand side, for instance, Big Data helps companies gain better understanding over consumer behaviors, foot traffic, buyer preferences and the actions that their competitors are taking. “This gives companies a solid offensive footing,” says Dennis Groseclose, president and CEO at TransVoyant, “and allows them to fuse external data and demand patterns to more effectively reduce demand variability.” Having actionable data also helps companies better manage lead times, variability and capacity. This, in turn, helps them better understand manufacturer and carrier behaviors. “With this information in hand, companies can squish planning cycle times down to one month vs. five months,” says Groseclose, “or to one week vs. five weeks.”

6. Prepare for the “SNEW” wave.

Here’s a buzzword you may not have heard of yet: SNEW, or social media, news, event and weather data, is the next acronym that’s either going to make supply chain managers sit up and take notice, or make them roll their eyes and groan. Either way, SNEW data promises to help improve supply chain capabilities and give companies even more data to sift through, digest and make sense of. An existing forecast, for example, can be adjusted accordingly when accurate weather predictions are factored into the equation.

Driven by the Internet of Things (IoT), SNEW uses a “combination of data feeds to determine the best routing, risk management, and other supply chain decisions,” according to Steve Banker, vice president, supply chain management at ARC Advisory Services, who sees SNEW as a potential player in the future of supply chain visibility and risk avoidance (or mitigation).

“This is a new solution to the market, and it’s being driven by the emergence of new technological capabilities,” Banker notes. The integration of social media, news, event and weather data into the manufacturing and distribution process is also getting a boost from the ongoing digitization of the supply chain.

“What we’re looking at is a series of technologies that are either rapidly emerging or already further along in terms of emergence,” says Banker, noting that while IoT is a bit further along in terms of maturity, concepts like SNEW and blockchain (i.e., a digital ledger where transactions made in bitcoin are recorded chronologically and publicly) are still nascent. “Over time,” he concludes, “these innovations will continue to generate Big Data that companies will use for decision making.”

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