Supply Chain Analytics using Big Data Explained | ATC

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Big Data Analytics in Supply Chain: Hype or Here to Stay?

Kelsey Meyer

Published January 23, 2019

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Using supply chain data analytics offers many benefits to businesses effectively using them.

Are you using big data in your supply chain?

It would make sense if you weren’t.

Because while 97% of executives understand how big data can benefit their business, only 17% have implemented it in their supply chain functions according to Global Operations Megatrends, a research study conducted by Accenture.

They surveyed over 1,000 senior executives from large global companies to understand their thoughts on the importance, benefits, and progress of big data analytics in the supply chain.

This post will summarize their findings and highlight the key points from their research because what they’ve found is highly relevant to supply chains of all sizes.

Digital technologies allow businesses to collect tremendous amounts of data, but data alone is meaningless. That’s why we see the emergence of big data analytics.

And as we pointed out above, big data is being woefully underutilized. We’ll take a look at why and what’s coming next for companies keen on using this technology in the future.

Before we do that, let’s look at the respondents of the survey to understand where these insights are coming from.

Overview of the Participants in the Study

Accenture surveyed 1,014 senior executives who mostly worked for large global companies.

Here’s a breakdown of the respondents who participated:

  • 56% of respondents held C-level titles, including Chief Supply Chain Officer, Chief Procurement Officer, Chief Sourcing Officer, Chief Operations Officer and Chief Operating Officer (Figure 12). The other 44% were senior-level supply chain, procurement or operations executives.
  • Just under half (48 percent) of participating companies had revenues of greater than US$5 billion, with 18 percent reporting more than $10 billion in revenue (Figure 13).
  • Companies represented a wide range of industries (Figure 14). The headquarters location of participating companies was evenly split across North America, Europe, and Asia Pacific.
supply chain data analytics figure 12

The participants in Accenture’s study have varying executive titles.

supply chain data analytics figure 13

The majority of respondents worked for companies raking in $1 billion or more annually.

supply chain data analytics figure 14

The biggest industries represented in this study are electronics and consumer goods.

Key Insights from Research

One of the glaring issues Accenture’s research brought to light is that big data analytics in the supply chain isn’t used by many companies, it’s certainly not well-coordinated, and there isn’t a consensus about how to create, organize, and implement the capabilities of big data that are key to success.

Simultaneously, many of these companies are ready to make investments to develop an advanced big data analytics capability.

  • More than one-third of executives reported being engaged in serious conversations to implement analytics in the supply chain.
  • And 3 out of 10 already have an initiative in place to implement analytics.

Now, for the small group of companies who have implemented big data analytics in their supply chain, they’ve seen high returns. And there are 3 factors making a difference in their results:

  1. A strong focus on developing an enterprise-wide analytics strategy.
  2. Embedding big data analytics in supply chain operations.
  3. And hiring people with a unique mix of analytics skills and knowledge of the business.

We’ll continue to dig deeper into Accenture’s research in the following sections, but before we dive into the nitty-gritty details, let’s define big data analytics.

What is Big Data Analytics?

In the past, analytics were very particular and situation specific, relying on simple tools that couldn’t process the large amount of data companies are collecting today.

As these tools became more sophisticated over the years, they’ve taken a central role in companies’ day-to-day operations.

Big data has two essential dimensions. The first one is the need to process data with these three qualities:

  • Velocity: in real-time or close to real-time
  • Variety: the data varies in time and in context, and is not a fixed data model
  • Volume: the volumes are significant and require unique approaches

The second one is the ability to resolve an issue or capitalize on an opportunity using insights about data gained from simulations, statistics, or even econometrics.

How Do Companies Think About Big Data Analytics?

Here’s how the respondents replied when Accenture asked them about their objectives:

  • 48% of executives expect to have the ability to react quickly to changes throughout the organization.
  • 45% expect to receive major insights about the future from big data analytics
  • And 43% expect to improve their supply chain performance through a cross-functional view of the supply chain itself.

And from what Accenture has seen, there’s no reason not to expect those things. In fact, they point to a case of an original equipment manufacturer (OEM) of industrial equipment as an example.

The OEM implemented analytics to find out how to best respond to warranty claims and to gain a deeper understanding of system quality issues suggest by the claims.

The big data analytics allowed the OEM to examine all the claims and identify patterns that can be handed off to research and development along with manufacturing to prevent future defects in products.

Use cases like this highlight why almost 7 out of 10 companies in Accenture’s survey are actively implementing analytics over the next 6-12 months or are seriously discussing using analytics in their supply chain (Figure 1).

Of course, what’s underlying these responses is a focus on discussion over action when it comes to making analytics a core part of their processes.  

supply chain data analytics figure 1

Many companies are talking about big data analytics but few are using it.

How Companies are Currently Using Supply Chain Big Data Analytics

It’s clear, companies are not adopting big data analytics as much as they should be, given their interest in it.

There are a number of reasons for this.

67% of executives in the study said they worry about the high cost of the investment required to deploy the technology, while 64% cited security issues as the second biggest obstacle to implementing big data analytics (Figure 2).

But those are far from the only reasons for not fully adopting analytics in their supply chain. Respondents are also concerned about:

  • Privacy issues.
  • Lack of a business case.
  • Little executive support.
  • And no capacity for implementing an analytics initiative.

However, Accenture’s research reveals that weak points in a handful of specific areas are likely preventing companies from recognizing the benefits of big data analytics. For example, only 4 in 10 companies have an enterprise-wide strategy (which includes the supply chain) to use analytics to drive business value (Figure 3).

supply chain data analytics figure 2

The top concerns for implementing big data analytics are price and security.

supply chain data analytics figure 3

The majority of companies surveyed do have some kind of analytics strategy in place.

Similar to the number of companies with a strategy, a mere 37% of companies said they have big data analytics actually embedded into key supply chain processes. Interestingly, the same amount of companies said they use analytics for the same thing, but on an ad hoc basis.

That last statistic may tell us that while companies recognize the value of supply chain data analytics, they don’t quite know how to make it work organization-wide.

And that point comes into clear focus when you consider the following:

Only 34% of companies have a dedicated team of data scientists focused entirely on big data analysis.

Almost half of the respondents said they have limited in-house capability for analytics. That usually entails one person in the supply chain or IT team using sophisticated software to generate insights (Figure 5).

Either A) these companies can’t find or attract the right talent or B) they still don’t consider supply chain data analytics a high priority.

It certainly seems like option B when you consider that companies seem to view analytics as a point solution rather than a total solution. 44% of respondents said they use one or more tools that use big data and only 43% of respondents said they have an enterprise-wide big data analytics implementation (Figure 6).

supply chain data analytics figure 4

An equal amount of companies use analytics fully and partially in their supply chain.

supply chain data analytics figure 5

Almost half of the companies in the study have in-house capability for big data analytics.

supply chain data analytics figure 6

Most companies use a variety of tools to leverage big data in their supply chain.

How to Enhance the ROI of Big Data Analytics Using 3 Key Practices

We’ve seen how the adoption of supply chain data analytics varies across the companies surveyed. Well, the same is true about the benefits and results of using big data.

For some companies, big data has helped them improve customer service, demand fulfillment, reaction times to supply chain issues, and supply chain efficiency (Figure 7).

Unfortunately for the rest of them, big data has been a big letdown.

But why?

Could there be a difference in how these two groups executed big data analytics to explain the disparity in their results?

Accenture thought so, and they found 3 key practices that separate the leading companies from everyone else.

supply chain data analytics figure 7

The biggest result of implementing big data analytics was improved customer service and demand fulfillment.

Key Practice #1: Leaders Make It a High Priority to Develop an Enterprise-Wide Strategy for Big Data Analytics

You can see it clearly in Accenture’s research:

Success with big data analytics is strongly correlated with an enterprise-wide strategy, including the supply chain (Figure 8).

With that said, a big data strategy that is focused purely on the supply chain is the second best option. It doesn’t correlate as strongly with the benefits granted to an enterprise-wide strategy, but it’s much better than a loose and random strategy focused on a few processes.

For example, companies with an enterprise-wide strategy are more likely to have:

  • Shortened order to delivery cycle times.
  • A more effective sales and operations process and decision making.
  • And improved cost to serve.

Accenture recommends that when you’re developing your supply chain data analytics strategy, start with an understanding of will drive value and differentiation.

supply chain data analytics figure 8

Shortened order-to-delivery cycle times were the biggest benefit to companies with an enterprise-wide analytics strategy.

Key Practice #2: Leaders Emphasize Embedding Big Data Analytics into Operations to Improve Decision-Making

If you want substantial returns then big data analytics needs to be operationalized.

Companies with analytics embedded in their day-to-day supply chain operations receive greater benefits than those using analytics on an ad-hoc basis (Figure 9), such as:

  • Shortened order-to-delivery cycle times (63 percent versus 12 percent),
  • Improvement in demand-driven operations (58 percent versus 15 percent),
  • Better customer and supplier relationships (52 percent versus 19 percent),
  • More effective S&OP and decision making (51 percent versus 13 percent),
  • Faster and more effective reaction time to supply chain issues (47 percent versus 18 percent),
  • And optimized inventory and asset productivity (45 percent versus 19 percent).
supply chain data analytics figure 9

Once again, shorter order-to-delivery times are the benefit of big data analytics in your operations.

As you implement big data solutions, you need to think about your own unique requirements (such as industry, market, business model, capabilities, etc.) as well as your culture to come up with the most effective approach.

There’s no right way to engineer a big data analytics capability, which is shown in Accenture’s research:

  • 34% of respondents said the best approach is an internally managed “big bang” implementation.
  • 57% opt for a proof-of-concept pilot centered on a specific supply chain issue, run by external or internal resources.
  • And 9% said a big bang, supply chain-wide implementation through external resources is best.
supply chain data analytics figure 10

Most companies want to see a proof of concept to implement big data analytics.

Key Practice #3: Leaders Hire Talent with a Mix of Deep Analytics Skills and Knowledge of the Business and Industry

The third practice that correlates with big data success is hiring an independent team of data scientists dedicated to analyzing your data on an ongoing basis (Figure 11).

If you can’t assemble that type of team, then having a group or person in-house who can use the right tools to generate the insights you want, can get you closer to the benefits of having an outside data scientist team.

Teams with data scientists are more likely to have:

  • Shortened order-to-delivery cycle times (54 percent versus 9 percent),
  • Improvement in demand driven operations (50 percent versus 9 percent),
  • Better customer and supplier relationships (44 percent versus 13 percent),
  • And more effective S&OP process and decision making (44 percent versus 11 percent).

These findings should tell you an important point:

Big data analytics and its tools are useless if a company has the wrong people with the wrong skills conducting the analysis.

Companies need people with both analytical skills like statistics, mathematics, and econometrics along with a deep understanding of their business and its industry.

supply chain data analytics figure 11

When companies have a dedicated team of data scientists, most of their business improves.

Final Thoughts

Big data analytics can offer businesses massive benefits: financial, operational, and otherwise.

But it’s a major investment that requires tremendous forethought and planning regarding your strategy and the outcomes you’re pursuing.

But if you go through that process, you’ll be better equipped to select the right implementation to generate the highest ROI.

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