5 ways Big Data will shake up supply chain systems

Big Data is the buzzword we can’t avoid, but for good reason: it’s changing the way a lot of businesses operate, and will continue to do so, especially within the supply chain world.

Sixty-four percent of supply chain executives consider Big Data analytics a disruptive and important technology, according to SCM World, even though it’s still a relatively new application of the technology.

“Most large Fortune 500 companies have been using Big Data for one to three years,” says Jeff Saltz, associate professor at Syracuse University’s School of Information Studies. “They’re experimenting some and getting significant value.”

But companies like Amazon and Apple have budgets beyond the moon. What about companies that don’t fall within the Fortune 500, let alone the 1000 or 2000?

Here are five ways Big Data will change supply chain as we know it, and how to get your company there — even if you’re met with a chorus of “What we have now works!” because it probably won’t for the business climate 10, 15, 20 years ahead.

1. Boosting visibility

Big Data analytics can provide visibility into where things are, where they should be, and what’s gumming up the system – assuming the data is the right stuff.

This can save money at every point in the process, especially when it comes to finding errors and fixing them before they become multi-million dollar headaches. “If we make the assumption that something happens very early in the supply chain, that will have a ripple effect,” says Barry Pellas, CTO and chief business technologist at IT consulting firm PointSource.

Big Data can also help companies with the planning and execution parts of that supply chain, says Melanie Nuce, vice president of corporate development at standards organization GS1 US, by letting companies know where their stuff is, and where it should be.

A lot of that right now is in RFID tagging, which cuts down on human error. “I can walk around my store with a smartphone [that] is collecting inventory for my store for me. I no longer have to deploy employees to very laborious inventory,” she says. Collecting data from RFID tags is also faster than a more manual approach.

Big Data can also tell you where that inventory should go “at a very granular level by channel, by customer, by size and color,” she says. Visibility also means that companies can share information with consumers who want to know more about the supply chain that led to the production of that product. For example, if a consumer is interested in buying a shirt, “I want to know that all the components came from good sources and no inappropriate labor [was] involved,” Nuce says.

2. Giving more weight to social media

As if Facebook and Twitter and Instagram (and whatever the next hot thing will be) weren’t pervasive enough in our lives, they will play a big role in the expansion of Big Data within supply chain. Already, IoT means that more products are throwing out data about consumers, but many of us already hand off that kind of data for free through social media.

“Social media influences the perception of a product or a service, and that drives my expectation of how I’m going to sell this service,” says Saltz. Big Data tied to social media can do a lot, from taking the temperature of an advertising campaign’s effectiveness to understanding the impact when a celebrity tweets about a product.

3. Pushing out legacy systems

Although it won’t erase those legacy systems overnight. According to that PointSource’s 2017 Supply Chain Digital Transformation Report, 89 percent of supply chain organizations rely on legacy systems, while just 40 percent say that they have the internal resources to maintain and improve on their organization’s infrastructure.

Nuce says “supply chain tends to be more observer” than early adopter, which can create pushback on investing money and resources into replacing legacy systems. “If you try to go in and replace entire systems for the entire infrastructure, it’s just never going to work. You’ll never finish and you’ll be way over budget,” says Pellas.

When a company wants to start making this transition, Pellas receommends that they “determine one of the highest value replacements.” Then create a road map on how to implement change over time.

“The other important thing is you can’t forget that your systems still need to operate while this change is going on,” he says. “The strategy has to include the ability to slowly roll out the changes but also work with the legacy system until the full switchover can occur.”

4. Speeding things up – for better or worse

Big Data is called that because there’s a lot of it, but not all of it will lead to a perfect supply chain.

“All automation does is make ‘garbage in’ and ‘garbage out’ a lot faster,” says Nuce. “If I’m an executive and I’m advocating for this initiative, I need to be able to address what are my data governance policies.”

That’s why taking the time to make this transition correctly is important. You want to make sure that the money spent is worth it and won’t sour the company on Big Data in the supply chain.

“If you don’t start with quality data, leveraging a Big Data initiative would not really be beneficial,” Nuce adds.