Unlocking the 3 V's of Big Data in the Digital Supply Chain

Dustin Caudell
Wed, Sep 13, 2017
Data collection and integration tools underpin big data innovation.
Data collection and integration tools underpin big data innovation.

Big data has been growing more important in the supply chain sector as businesses look to eke out every bit of value they can through greater operational insights.

The problem is that big data is a transformational technology and many companies lack the supporting systems they need to get value from it. Because of this, the supply chain sector is going through a period of digital disruption in which organizations are working to digitize more elements of the supply chain to better create, document, integrate and use data across all phases of operations.

Logistics Management reported that big data is still posing challenges to businesses in the supply chain because organizations are still working to identify the best ways to make data actionable within their operations. Industry expert Shannon Vaillancourt told the news source the complicated nature of the supply chain is making big data particularly valuable because the analytics strategy helps companies gain insights into underlying problems.

"Supply chains are more complex than ever, and with these complexities come many challenges," Vaillancourt told Logistics Management. "Big data allows companies to diagnose the issue so they truly understand what is causing it."

Putting Big Data Into Practice
For many companies, the big data problem is as simple as not being able to complete the transformation needed to support the analytics plan. A Supply Chain Dive report pointed out that big data requires organizations to enact a multi-step process in which they take on a huge influx of new information and develop strategies to use it. In response, businesses will often set forth with extremely ambitious plans and push ahead without taking the time to build out supporting systems that make big data programs successful.

According to the news source, businesses often end up unable to really leverage the final benefits of big data because they lack the processes and procedures needed to help users interact with information within their everyday tasks.

This is where innovation is truly needed. Organizations that want to set their sites on big data in the supply chain need to empower their workers to gather and use information more readily without having to step too far outside their usual workflows. Employees should have to hop between devices or jump through bureaucratic hoops to access key data, and they don't have to in today's connected, mobile-enabled supply chain climate. Companies hoping to get more value from big data should look at the three v's of the analytics tactic and consider how underlying technology improvements in the supply chain can make data more valuable in each.

1. Volume
As its name implies, big data is largely about handling a huge quantity of information. The theory is simple - if a business is able to gather more data than its competitors, it can use that information to learn things about the market that its competitors won't necessarily understand. As big data has evolved, that strategic focus has come out in many forms. In the supply chain, organizations can use large quantities of historic data to identify patterns in picking productivity relative to where items are stored. Suddenly, strategies that were anecdotal based on managerial experience and preferences could be explored based on real-world results.

However, these types of gains are only possible if businesses are collecting the amount of data they need, integrating across siloed software systems into centralized platforms and updating it frequently to identify new changes and patterns that emerge over time. 

The problem with maintaining a large volume of data is that information needs to be constantly updated and improved over time. Trends in the relationship between a shipping process and asset shrinkage, for example, can shift due to a diverse range of causes, and organizations can't rely on old data to make their decisions. Solutions such as ERP integration systems that bring together data from across lines of business and make it available in a central platform are critical in keeping up with the volume demands of big data. A one-time project isn't going to be enough to derive ongoing value from analytics. Companies must be intentional about constantly growing and refining their data sets.

2. Velocity
In some situations, organizations can have data sets that are a few days, or even weeks out of data to inform their strategic decision-making. However, big data is at its most valuable when companies can use analytics to derive value in near real time. 

Velocity is a particularly tricky issue in the supply chain sector. Consider the following hypothetical situation:

An organization works with two primary warehouses to support a small manufacturing operation. One facility is located close to production and features parts, tools and similar equipment. The warehouse is fully connected and data gathered within the facility is almost immediately sent to relevant management systems. If part inventories drop to a problematic level, chances are managers and administrators will be alerted in moments.

The second warehouse, on the other hand, resides at the edge of the organization's campus because it contains hazardous raw and refined materials that are best stored and managed away from production until they are needed or disposed of. This warehouse could handle anything from materials that are used in products to special cleaning chemicals that are associated with seasonal maintenance tasks or chemical waste created during production and shipped out disposal specialists. This warehouse needs to be just as connected to operations as the first, but its location on the edge of the campus makes the cost of running a network link to the facility prohibitive. Wireless signal is inconsistent, and the warehouse regularly goes dark. Once the connection drops, users are forced to log data in paper systems and re-enter it digitally when the network becomes available. 

Click here to learn how to solve the remote warehouse dilemma.

This type of remote facility management problem can limit big data velocity, undermining value potential. Dedicated remote management systems combined with mobile data collection devices can be set to log data and automatically synchronize with other systems once the connection is restored. Essentially, work goes on as normal at all times, the big data system is fed new data efficiently and employees don't have to worry about connectivity in a remote warehouse.

3. Variety
Using big data to identify underlying patterns in operations hinges on gathering a wide enough range of data that companies can unearth unexpected insights. Like volume and velocity, finding success with variety depends on getting information from every life of the business. In the supply chain, this means giving users mobile data collection tools so they can make updates immediately. From there, ERP integration brings key information together so it can be analyzed for strategic gains.

Volume, velocity and variety are inherently linked. A technology that helps with one will likely be useful for another. As organizations explore how big data can transform their supply chain, they must begin by considering how they need to digitize data across the supply chain to take advantage of the analytics strategy. At RFgen, our mobile supply chain solutions provide the data collection and management infrastructure businesses need to set a solid foundation for big data and sustain innovation beyond the initial investment. Analytics tools are only as valuable as the system that feed them data. Don't ignore backend processes when diving into the big data deep end.

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