How do supply chains use Big Data?

March 1, 2017
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Big Data may be behind a fair share of disruptions, but supply chain management is not one of them.

That’s not to say supply chains have not shifted. How we collect and analyze data changes the way the chain communicates. In fact, supply chains have changed so much that Deloitte published a report abolishing the linear chain, claiming technology disruptions led to “The rise of the digital supply network.”

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Big Data may be behind a fair share of disruptions, but supply chain management is not one of them.

That’s not to say supply chains have not shifted. How we collect and analyze data changes the way the chain communicates. In fact, supply chains have changed so much that Deloitte published a report abolishing the linear chain, claiming technology disruptions led to “The rise of the digital supply network.”

Alongside these shifts, supply chain managers have adapted to do their jobs differently. Just as Excel changed the way supply chain reporting occurred, Big Data platforms are allowing professionals to focus on bigger-picture items instead of rote tasks.

The fact that tasks and objects of the job have changed does not indicate a change in supply chain management. On the contrary, supply chain management – as it always has existed – has only risen in profile and importance with the rise of Big Data. As the world’s connectivity increases, companies in retail, manufacturing and logistics alike need a manager capable of adapting to change.

The universal supply chain manager is invaluable not because of the tasks they can perform, but a vision which Big Data has merely enabled: to increase efficiency, reduce risk and improve customer service.

Different chains, similar shifts

“We found that in the supply chain world of today, [there] are many, many use cases – we track about 400 or so of those right now,” Adam Mussomeli, told Supply Chain Dive. “But there’s six major classifications that supply chain folks are trying to do within, or really across any industry to some degree.”

The first is visibility, according to Mussomeli. One type is logistics visibility, to be able to track and know when supplies come in and products exit a factory. Just as important, however, is multi-tier visibility, wherein a supply chain manager is able to see a problem at their supplier’s factory, or beyond, and be able to address the issue immediately.

“The second big one is getting some better form of demand and supply synchronization,” he added. “There is a pretty long history of people building things in the hopes that it’s what the market wants, but it turns out the market does not.” Alternatively, many executives will push projects to receive product and point-of-sale data that may help them adjust production to better meet demand.

Third, managers are looking to optimize their use of fulfillment channels – a task enabled by consumer and logistics data. Based on this data, supply chain managers can adjust the type of transportation, pickup location or point of sale for specific products. An apple, for example, has different sales and transportation strategies than a refrigerator.

 

The latter three use case categories seek to improve efficiency through production tools.

Supply chain managers may seek (fourth) to build a “smart, connected product,” Mussomeli says. Manufacturers, warehouse managers and retailers alike may now benefit from sensor-integrated products that could “phone home” with its condition, and alert a manager of a pending change or need for replenishment. Similarly, supply chains benefit from increased asset intelligence (fifth), where connected machines or racks can produce data to alert managers of changes in condition.

“Then the last one is this whole notion of worker safety and productivity,” he said. “An example of that being using augmented reality to help picking in a warehouse, or some other form of augmented reality in a shop floor to tell somebody a bin needs to be refilled somewhere.”

Driving insights at each stage of the chain

Regardless of the use case category, each Big Data project seeks to provide the supply chain manager with insight, rather than information. In other words, a Big Data project is not just about collecting data, but being able to do something with it.

At the retail level, RFID tagging is taking off as a way to store product data and improve replenishment capacity. Target recently linked the product data benefits of the tags to better inventory management capabilities and increased online sales, and a recent study shows 96% of apparel retailers are on track to do the same.

“If you think about what happens at a particular retail store, and I think about needing to replenish inventory at that store, I want to know all of the items that sold through that one location,” Karin Bursa, executive vice president of marketing at Logility told Supply Chain Dive. “That’s the only way I’m going to be able to replenish and make sure that they have available the inventory we’ve planned for.”

“As supply chain professionals, we want [Big Data] because it starts creating dependencies and correlations between the products,” she added. “And it helps us look for patterns and improve the predictability of future product needs.”

Logistics providers, too, benefit from Big Data. The 2017 Third Party Logistics Study found 98% of surveyed 3PLs believed data-driven decision-making will become essential to future supply chains, while another 86% believed it would become a core competency. Early adopters abound too, as the drive for end-to-end visibility provides a premium for trucking companies with telematicsports with efficient communications, and even shipping lines with real-time monitoring.

While manufacturing case studies are typically not publicly available, the industry is among the largest beneficiaries from Big Data. After all, as logistics providers scale up their visibility and retailers increase the product data, manufacturers can use that data to come closer to the demand-driven supply chain.

In a recent case study, Software AG reported a $70 billion family of consumer packaged goods, healthcare and pharmaceutical manufacturing was able to launch 1,800 products 75% faster and achieve visibility across more than 4,000 logistics provider with its platform. “Ingredients of any product— from a pain reliever to baby lotion—can be easily traced in minutes,” the case study found. “Consumers can even check products information online, satisfying their need for instant answers and reinforcing their trust in the company’s brands.”

Regardless of whether it is a retailer, 3PL or manufacturer, Big Data allows supply chains to improve service and efficiency by syncing product and external data with business decisions. In addition, increased visibility permits companies to identify and adjust for risk upon adverse circumstances.

How do you stack up?

Yet, the efficacy of a Big Data project may not depend on the use case, goal or problem being solved, but on the company’s digital capacity.

“[Data projects] can be a challenge sometimes, especially when you get into the transportation side,” Sean Riley, Software AG’s global industry director for supply chain & manufacturing  told Supply Chain Dive. “Not everyone is going to be a J.B. Hunt or a Swift or one of the larger carriers that has advanced telematics coming off of their trucks.”

As a result, supply chain managers are forced to balance various different technological capabilities – even internally – while engaging in a new data project. For that reason, Deloitte Consulting created a “digital stack” to help visualize the various stages at which data can be applied for insight.

 

 

 

Deloitte separates a company’s digital capabilities into two sections: the digital core and the digital stack. The core is the basic infrastructure necessary to handle a Big Data project – being able to receive networked data, translate it into a usable format, and process it independently. The stack, meanwhile, refers to the different levels of insights that can be derived from a Big Data project.

Each layer builds upon the other – so the more data is networked, the greater the connectivity and potential for automation at the core level. Similarly, the greater a company’s supply chain visibility, the more likely they will be able to use data for decision support and thereby make strategic decisions.

The ways supply chains can benefit from Big Data projects are significant, but in general, the reasons and methods supply chain managers use to start a new project are the same.


This post originally appears on our sister publication, Supply Chain Dive. Our mission is to provide busy professionals like you with a bird’s-eye-view of the Supply Chain industry in 60 seconds. To subscribe to our daily newsletter click here.