Big data analytics in supply chain: Tackling the tidal wave
The amount of supply chain data is growing exponentially, and companies are struggling to make effective use of available information. New research reveals the strategies they're adopting to help them harness the power of big data.
Contributing Editor Toby Gooley is a writer and editor specializing in supply chain, logistics, and material handling, and a lecturer at MIT's Center for Transportation & Logistics. She previously was Senior Editor at DC VELOCITY and Editor of DCV's sister publication, CSCMP's Supply Chain Quarterly. Prior to joining AGiLE Business Media in 2007, she spent 20 years at Logistics Management magazine as Managing Editor and Senior Editor covering international trade and transportation. Prior to that she was an export traffic manager for 10 years. She holds a B.A. in Asian Studies from Cornell University.
Editor's note: This is the first installment of a special two-part report on how companies are using big data in the supply chain. This month, we'll look at how satisfied businesses are with their data, the technologies they're using for data analysis, and how far along they are in their efforts to leverage the data they collect. Next month, we'll examine some of the roadblocks companies encounter when implementing big data analytics in their supply chains, the benefits they've realized to date and expect down the road, and their plans for future investment in the technology.
Technology is making it possible for supply chain organizations to gather enormous amounts of information from an expanding variety of sources. Billions of data points are pouring in from supply chain network nodes, multiple retail channels, the industrial Internet of Things ... the list goes on and continues to grow. The aim, of course, is to analyze that information to gain visibility into opportunities for innovation and improvement. But few companies are actually deriving sustainable value from the supply chain data they are accumulating. Instead, they are struggling with how to ensure the quality of their data, how to analyze it, and how to make practical use of what they learn from it.
New research conducted by CSCMP's Supply Chain Quarterly; Arizona State University; Colorado State University; Competitive Insights LLC, a provider of advanced supply chain analytics solutions; and the consulting firm lharrington group LLC investigated the current state of supply chain data analytics and the strategies organizations are adopting to harness the power of big data. Over time, this annual survey will track, in the aggregate, companies' progress in using big data analytics in supply chain management.
In this article and a companion piece to be published next month, we outline the study's principal findings. Among the topics we'll discuss are respondents' satisfaction level with their data; what technologies companies are using for data analysis; the challenges and benefits associated with managing growing volumes of supply chain data; and finally, where respondents stand now as well as their priorities for near-term investment in data analytics.
SATISFACTION WITH DATA PROVING ELUSIVE
The survey was conducted in June 2017 via an e-mail invitation to readers of CSCMP's Supply Chain Quarterly and subscribers to a newsletter produced by Competitive Insights. A total of 126 fully completed, usable responses were compiled to obtain the survey results. (For more information about the research, see the sidebar.)
There's no question that most companies are seeing a significant increase in the amount of data they are collecting. When asked to characterize the increase over the past three years in the volume of supply chain data available to them, 36 percent said it was moderately high, while 38 percent said it was high or very high. But, as is often the case, quantity does not necessarily equate to quality.
How satisfied are supply chain managers with the data they currently have to run their supply chains? A majority of survey respondents report being at least moderately satisfied with their supply chain data in terms of availability, usability, integrity, and consistency. However, the combined "favorable" numbers (moderately high, high, or very high level of satisfaction) were not overwhelmingly higher than those for the correlating unfavorable scores (Exhibit 1).
Interestingly, only a very few respondents report being very satisfied in all four data attribute areas: availability of data (3 percent); usability (2 percent); integrity (6 percent); and consistency (4 percent).
"Data is the foundation the 'house' sits on," observes Richard Sharpe, chief executive officer of Competitive Insights. "The survey results clearly show that there are cracks in that foundation—cracks in companies' ability to bring data together, integrate it, have confidence in it, and believe that it is consistent. To take advantage of big data analytics, we have to do better in all four categories."
If companies are only partially satisfied with the data they're getting, the logical question to ask is, exactly what software solutions are they currently using to gather that data? Overwhelmingly, the tools in heaviest use are not advanced analytics or business intelligence solutions. Nor are they operational point applications like warehouse management systems. Despite the availability of an array of sophisticated analytics software, the most widely used tool for managing supply chain data today is still Excel spreadsheets (Exhibit 2).
Despite their dependency on spreadsheets, users aren't necessarily happy with it as a data management tool. "Our survey shows that Excel is very negatively associated with user satisfaction in terms of usability, integrity, and consistency of data," Dale Rogers, ON Semiconductor Professor of Business at Arizona State University, reports. "The problem with Excel is that everyone builds their own spreadsheets, so there's no consistency, no single version of the truth that's shared across departments. That makes it very difficult to trust the data sufficiently to make big decisions across departments."
The survey also found that, not unexpectedly, large companies rely on their enterprise resource planning (ERP) systems to run the financials of the business. But for supply chain professionals, ERP has shortcomings.
"Supply chain folks don't really like ERP that much," notes Zac Rogers, assistant professor of supply chain management at Colorado State University. "Many do not think the data that comes out of ERP systems is very useable for their purposes. They find it too rigid. They also lose the granular operational data they used to get with older supply chain point solutions. And as with spreadsheets, they don't necessarily trust the ERP data—at least not to manage their supply chains the way they need to."
When talking about big data analytics, supply chain organizations typically rely on five basic kinds of tools:
Descriptive—tells you what is happening
Diagnostic—tells you why it's happening
Predictive—tells you what will happen
Prescriptive—tells you what should/could be done
Cognitive—uses machine learning to tell you what should be done.
By far the most widely used of these five is descriptive analytics, according to the survey results. Sixty-one percent of respondents report using this type of analytics tool. Furthermore, use of the four other types of analytics tools lags descriptive applications by a significant margin. According to the survey, companies that deploy these tools regularly, frequently, or heavily use them as follows: diagnostic, 42 percent; prescriptive, 36 percent; predictive, 31 percent; and cognitive, 18 percent (Exhibit 3).
Supply chain organizations that limit themselves to descriptive analytics are unlikely to make much progress. "Descriptive data tools are absolutely necessary," Sharpe says. "But they are only good for telling you what has already happened. To get greater insight, companies need to move into the other types of applications."
Adoption of these more advanced analytics tools takes time, however. To that point, how far have companies come in their use of big data analytics in their supply chains? How mature are they not just in implementing the technologies, but in realizing benefits?
The answer is "not very far," as the survey numbers indicate:
28 percent of companies are in the "developing" stage, with one or more big data analytics initiatives under way.
24 percent are in the "early" stage, conducting proof-of-concept testing to determine benefits and drawbacks.
20 percent have not adopted big data analytics in their supply chain.
Only 2 percent rank themselves as mature; that is, in the "transformational" stage of adoption and benefits.
One interesting note on the maturity question: Different industry sectors are at varying stages of not just maturity, but also plans for adoption. On a maturity model scale of 1 to 6, no industry was a 6; in fact, none reached the top two tiers—"advanced" or "transformational." The technology sector ranked highest at 3.7, just short of "somewhat advanced," while the lowest was life sciences, at 2.3 solidly in the "early" stage. Machinery manufacturers ranked themselves just slightly ahead of life sciences, and third-party logistics companies (3PLs) and retailers fell about halfway between "early" and "developing." (Other industries were not represented in significant numbers.)
Commenting on these rankings, Sharpe observes that some industries are more cognizant of the value that can be derived from supply chain data analytics, while some show little interest in moving beyond what they traditionally have done. For example, although life sciences (which also includes healthcare and pharmaceuticals) scored lowest in maturity, respondents in that industry put very high or moderately high priority on investing in big data analytics. "They understand they need to advance quickly, because of how fast their industry is changing, so they're making these investments," he says.
To be continued… Look for the second part of our special report on big data analytics in our February issue. In that article, we'll look at the roadblocks companies encounter when implementing big data analytics in their supply chains, the benefits they've realized to date and expect down the road, and plans for future investment in the technology.
About the survey
The research outlined in this article investigated the current state of supply chain data analytics and the strategies organizations are adopting to harness the power of big data. The research team included Dr. Dale Rogers of Arizona State University, Dr. Zac Rogers of Colorado State University, Richard Sharpe and Tami Kitajima of Competitive Insights LLC, Lisa Harrington of lharrington group LLC, and Toby Gooley of CSCMP's Supply Chain Quarterly, a sister publication to **{DC Velocity.
The survey was conducted in June 2017 via an e-mail invitation to readers of CSCMP's Supply Chain Quarterly and subscribers to a newsletter produced by Competitive Insights. A total of 126 usable responses were compiled to obtain the survey results.
The great majority of respondents (84 percent) were located in North America (U.S., Canada, or Mexico), while the rest were split among Europe, Central/South America, South Asia, and Asia-Pacific. They represented a wide range of industries, with the most common including third-party logistics; retail; technology (computers, software, electronics); machinery and industrial equipment; food, beverage, and grocery; and life sciences, healthcare, and pharmaceuticals.
Supply chain management (26 percent) was most often cited as respondents' primary functional responsibility, followed by logistics (19 percent), warehousing and distribution (15 percent), and corporate management (13 percent). Sixty-six percent of respondents said their companies have annual gross revenues of less than US$1 billion, 24 percent reported revenues between $1 billion and $15 billion, and 10 percent said their revenues ran to more than $15 billion.
As for titles, the largest contingents were manager/supervisor, with 41 percent, and senior manager/director, with 31 percent. A small number identified as vice president/senior vice president and corporate officer/president (both 7 percent); the rest included analysts, engineers, and other titles.
Over time, this annual survey will track, in the aggregate, companies' progress in using big data analytics in supply chain management. The research team encourages this year's respondents to continue their participation and is seeking additional participants for the 2018 survey. For more information about how to participate, please contact Dr. Zac Rogers at Zac.Rogers@colostate.edu.
Economic activity in the logistics industry expanded in January, growing at its fastest clip in more than two years, according to the latest Logistics Managers’ Index (LMI) report, released this week.
The LMI jumped nearly five points from December to a reading of 62, reflecting continued steady growth in the U.S. economy along with faster-than-expected inventory growth across the sector as retailers, wholesalers, and manufacturers attempted to manage the uncertainty of tariffs and a changing regulatory environment. The January reading represented the fastest rate of expansion since June 2022, the LMI researchers said.
An LMI reading above 50 indicates growth across warehousing and transportation markets, and a reading below 50 indicates contraction. The LMI has remained in the mid- to high 50s range for most of the past year, indicating moderate, consistent growth in logistics markets.
Inventory levels rose 8.5 points from December, driven by downstream retailers stocking up ahead of the Trump administration’s potential tariffs on imports from Mexico, Canada, and China. Those increases led to higher costs throughout the industry: inventory costs, warehousing prices, and transportation prices all expanded to readings above 70, indicating strong growth. This occurred alongside slowing growth in warehousing and transportation capacity, suggesting that prices are up due to demand rather than other factors, such as inflation, according to the LMI researchers.
The LMI is a monthly survey of logistics managers from across the country. It tracks industry growth overall and across eight areas: inventory levels and costs; warehousing capacity, utilization, and prices; and transportation capacity, utilization, and prices. The report is released monthly by researchers from Arizona State University, Colorado State University, Rochester Institute of Technology, Rutgers University, and the University of Nevada, Reno, in conjunction with the Council of Supply Chain Management Professionals (CSCMP).
As commodities go, furniture presents its share of manufacturing and distribution challenges. For one thing, it's bulky. Second, its main components—wood and cloth—are easily damaged in transit. Third, much of it is manufactured overseas, making for some very long supply chains with all the associated risks. And finally, completed pieces can sit on the showroom floor for weeks or months, tying up inventory dollars and valuable retail space.
In other words, the furniture market is ripe for disruption. And John "Jay" Rogers wants to be the catalyst. In 2022, he cofounded a company that takes a whole new approach to furniture manufacturing—one that leverages the power of 3D printing and robotics. Rogers serves as CEO of that company, Haddy, which essentially aims to transform how furniture—and all elements of the "built environment"—are designed, manufactured, distributed, and, ultimately, recycled.
Rogers graduated from Princeton University and went to work for a medical device startup in China before moving to a hedge fund company, where he became a Chartered Financial Analyst (CFA). After that, he joined the U.S. Marine Corps, serving eight years in the infantry. Following two combat tours, he earned an MBA from the Harvard Business School and became a consultant for McKinsey & Co.
During this time, he founded Local Motors, a next-generation vehicle manufacturer that launched the world's first 3D-printed car, the Strati, in 2014. In 2021, he brought the technology to the furniture industry to launch Haddy. The father of four boys, Rogers is also a director of the RBR Foundation, a philanthropic organization focused on education and health care.
Rogers spoke recently with DC Velocity Group Editorial Director David Maloney on an episode of the "Logistics Matters" podcast.
Q: Could you tell us about Haddy and how this unique company came to be?
A: Absolutely. We have believed in the future of distributed digital manufacturing for a long time. The world has gone from being heavily globalized to one where lengthy supply chains are a liability—thanks to factors like the growing risk of terrorist attacks and the threat of tariffs. At the same time, there are more capabilities to produce things locally. Haddy is an outgrowth of those general trends.
Adoption of the technologies used in 3D printing has been decidedly uneven, although we do hear about applications like tissue bioprinting and food printing as well as the printing of trays for dental aligners. At Haddy, we saw an opportunity to take advantage of large-scale structural printing to approach the furniture and furnishings industry. The technology and software that make this possible are already here.
Q: Furniture is a very mature market. Why did you see this as a market that was ripe for disruption?
A:The furniture market has actually been disrupted many times in the last 200 years. The manufacturing of furniture for U.S. consumption originally took place in England. It then moved to Boston and from there to New Amsterdam, the Midwest, and North Carolina. Eventually, it went to Taiwan, then China, and now Vietnam, Indonesia, and Thailand. And each of those moves brought some type of disruption.
Other disruptions have been based on design. You can look at things like the advent of glue-laminated wood with Herman Miller, MillerKnoll, and the Eames [furniture design and manufacturing] movement. And you can look at changes in the way manufacturing is powered—the move from manual operations to machine-driven operations powered by steam and electricity. So the furniture industry has been continuously disrupted, sometimes by labor markets and sometimes by machines and methods.
What's happening now is that we're seeing changes in the way that labor is applied in furniture manufacturing. Furniture has traditionally been put together by human hands. But today, we have an opportunity to reassign those hands to processes that take place around the edges of furniture production. The hands are now directing robotics through programming and design; they're not actually making the furniture.
And so, we see this mature market as being one that's been continuously disrupted during the last 200 years. And this disruption now has a lot to do with changing the way that labor interacts with the making of furniture.
Q: How do your 3D printers actually create the furniture?
A:All 3D printing is not the same. The 3D printers we use are so-called "hybrid" systems. When we say hybrid, what we mean is that they're not just printers—they are holders, printers, polishers, and cutters, and they also do milling and things like that. We measure things and then print things, which is the additive portion. Then we can do subtractive and polishing work—re-measuring, moving, and printing parts again. And so, these hybrid systems are the actual makers of the furniture.
Q: What types of products are you making?
A: We've started with hardline or case goods, as they're sometimes known, for both residential and commercial use—cabinets, wall bookshelves, freestanding bookshelves, tables, rigid chairs, planters, and the like. Basically, we've been concentrating on products that don't have upholstery.
It's not that upholstery isn't necessary in furniture, as it is used in many pieces. But right now, we have found that digital furniture manufacturing becomes analog again when you have to factor in the sewing process. And so, to move quickly and fully leverage the advantages of digital manufacturing, we're sticking to the hardline groups, except for a couple of pieces that we have debuted that have 3D-printed cushions, which are super cool.
Q: Of course, 3D printers create objects in layers. What types of materials are you running through your 3D printers to create this furniture?
A: We use recycled materials, primarily polymer composites—a bio-compostable polymer or a synthetic polymer. We look for either recycled or bio-compostable [materials], which we then reinforce with fibers and fillers, and that's what makes them composites. To create the bio-compostables, we marry them with bio-fibers, such as hemp or bamboo. For synthetic materials, we marry them with things like glass or carbon fibers.
Q: Does producing goods via 3D printing allow you to customize products easily?
A: Absolutely. The real problem in the furniture and furnishings industries is that when you tool up to make something with a jig, a fixture, or a mold, you tend to be less creative because you now feel you have to make and sell a lot of that item to justify the investment.
One of the great promises of 3D printing is that it doesn't have a mold and doesn't require tooling. It exists in the digital realm before it becomes physical, and so customization is part and parcel of the process.
I would also add that people aren't necessarily looking for one-off furniture. Just because we can customize doesn't mean we're telling customers that once we've delivered a product, we break the digital mold, so to speak. We still feel that people like styles and trends created by designers, but the customization really allows enterprise clients—like businesses, retailers, and architects—to think more freely.
Customization is most useful in allowing people to "iterate" quickly. Our designers can do something digitally first without having to build a tool, which frees them to be more creative. Plus, because our material is fully recyclable, if we print something for the first time and find it doesn't work, we can just recycle it. So there's really no penalty for a failed first printing—in fact, those failures bring their own rewards in the form of lessons we can apply in future digital and physical iterations.
Q: You currently produce your furniture in an automated microfactory in Florida, with plans to set up several more. Could you talk a little about what your microfactory looks like and how you distribute the finished goods?
A: Our microfactory is a 30,000-square-foot box that mainly contains the robots that make our furniture along with shipping docks. But we don't intend for our microfactories to be storage warehouses and trans-shipment facilities like the kind you'd typically see in the furniture industry—all of the trappings of a global supply chain. Instead, a microfactory is meant to be a site where you print the product, put it on a dock, and then ship it out. So a microfactory is essentially an enabler of regional manufacturing and distribution.
Q: Do you manufacture your products on a print-to-order basis as opposed to a print-to-stock model?
A: No. We may someday get to the point where we receive an order digitally, print it, and then send it out on a truck the next day. But right now, we aren't set up to do a mini-delivery to one customer out of a microfactory.
We are an enterprise company that partners with architects, designers, builders, and retailers, who then distribute our furnishings to their customers. We are not trying to go direct-to-consumer at this stage. It's not the way a microfactory is set up to distribute goods.
Q: You've mentioned your company's use of recycled materials. Could you talk a little bit about other ways you're looking to reduce waste and help support a circular economy?
A: Yes. Sustainability and a circular economy are really something that you have to plan for. In our case, our plans call for moving toward a distributed digital manufacturing model, where we establish microfactories in various regions around the world to serve customers within a 10-hour driving radius of the factory. That is a pretty large area, so we could cover the United States with just four or five microfactories.
That also means that we can credibly build our recycling network as part of our microfactory setup. As I mentioned, we use recycled polymer stock in our production, so we're keeping that material out of a landfill. And then we tell our enterprise customers that while the furniture they're buying is extremely durable, when they're ready to run a special and offer customers a credit for turning in their used furniture, we'll buy back the material. Buying back that material actually reduces our costs because it's already been composited and created and recaptured. So our microfactory network is well designed for circularity in concert with our enterprise customers.
Generative AI (GenAI) is being deployed by 72% of supply chain organizations, but most are experiencing just middling results for productivity and ROI, according to a survey by Gartner, Inc.
That’s because productivity gains from the use of GenAI for individual, desk-based workers are not translating to greater team-level productivity. Additionally, the deployment of GenAI tools is increasing anxiety among many employees, providing a dampening effect on their productivity, Gartner found.
To solve those problems, chief supply chain officers (CSCOs) deploying GenAI need to shift from a sole focus on efficiency to a strategy that incorporates full organizational productivity. This strategy must better incorporate frontline workers, assuage growing employee anxieties from the use of GenAI tools, and focus on use-cases that promote creativity and innovation, rather than only on saving time.
"Early GenAI deployments within supply chain reveal a productivity paradox," Sam Berndt, Senior Director in Gartner’s Supply Chain practice, said in the report. "While its use has enhanced individual productivity for desk-based roles, these gains are not cascading through the rest of the function and are actually making the overall working environment worse for many employees. CSCOs need to retool their deployment strategies to address these negative outcomes.”
As part of the research, Gartner surveyed 265 global respondents in August 2024 to assess the impact of GenAI in supply chain organizations. In addition to the survey, Gartner conducted 75 qualitative interviews with supply chain leaders to gain deeper insights into the deployment and impact of GenAI on productivity, ROI, and employee experience, focusing on both desk-based and frontline workers.
Gartner’s data showed an increase in productivity from GenAI for desk-based workers, with GenAI tools saving 4.11 hours of time weekly for these employees. The time saved also correlated to increased output and higher quality work. However, these gains decreased when assessing team-level productivity. The amount of time saved declined to 1.5 hours per team member weekly, and there was no correlation to either improved output or higher quality of work.
Additional negative organizational impacts of GenAI deployments include:
Frontline workers have failed to make similar productivity gains as their desk-based counterparts, despite recording a similar amount of time savings from the use of GenAI tools.
Employees report higher levels of anxiety as they are exposed to a growing number of GenAI tools at work, with the average supply chain employee now utilizing 3.6 GenAI tools on average.
Higher anxiety among employees correlates to lower levels of overall productivity.
“In their pursuit of efficiency and time savings, CSCOs may be inadvertently creating a productivity ‘doom loop,’ whereby they continuously pilot new GenAI tools, increasing employee anxiety, which leads to lower levels of productivity,” said Berndt. “Rather than introducing even more GenAI tools into the work environment, CSCOs need to reexamine their overall strategy.”
According to Gartner, three ways to better boost organizational productivity through GenAI are: find creativity-based GenAI use cases to unlock benefits beyond mere time savings; train employees how to make use of the time they are saving from the use GenAI tools; and shift the focus from measuring automation to measuring innovation.
According to Arvato, it made the move in order to better serve the U.S. e-commerce sector, which has experienced high growth rates in recent years and is expected to grow year-on-year by 5% within the next five years.
The two acquisitions follow Arvato’s purchase three months ago of ATC Computer Transport & Logistics, an Irish firm that specializes in high-security transport and technical services in the data center industry. Following the latest deals, Arvato will have a total U.S. network of 16 warehouses with about seven million square feet of space.
Terms of the deal were not disclosed.
Carbel is a Florida-based 3PL with a strong focus on fashion and retail. It offers custom warehousing, distribution, storage, and transportation services, operating out of six facilities in the U.S., with a footprint of 1.6 million square feet of warehouse space in Florida (2), Pennsylvania (2), California, and New York.
Florida-based United Customs Services offers import and export solutions, specializing in remote location filing across the U.S., customs clearance, and trade compliance. CTPAT-certified since 2007, United Customs Services says it is known for simplifying global trade processes that help streamline operations for clients in international markets.
“With deep expertise in retail and apparel logistics services, Carbel and United Customs Services are the perfect partners to strengthen our ability to provide even more tailored solutions to our clients. Our combined knowledge and our joint commitment to excellence will drive our growth within the US and open new opportunities,” Arvato CEO Frank Schirrmeister said in a release.
And many of them will have a budget to do it, since 51% of supply chain professionals with existing innovation budgets saw an increase earmarked for 2025, suggesting an even greater emphasis on investing in new technologies to meet rising demand, Kenco said in its “2025 Supply Chain Innovation” survey.
One of the biggest targets for innovation spending will artificial intelligence, as supply chain leaders look to use AI to automate time-consuming tasks. The survey showed that 41% are making AI a key part of their innovation strategy, with a third already leveraging it for data visibility, 29% for quality control, and 26% for labor optimization.
Still, lingering concerns around how to effectively and securely implement AI are leading some companies to sidestep the technology altogether. More than a third – 35% – said they’re largely prevented from using AI because of company policy, leaving an opportunity to streamline operations on the table.
“Avoiding AI entirely is no longer an option. Implementing it strategically can give supply chain-focused companies a serious competitive advantage,” Kristi Montgomery, Vice President, Innovation, Research & Development at Kenco, said in a release. “Now’s the time for organizations to explore and experiment with the tech, especially for automating data-heavy operations such as demand planning, shipping, and receiving to optimize your operations and unlock true efficiency.”
Among the survey’s other top findings:
there was essentially three-way tie for which physical automation tools professionals are looking to adopt in the coming year: robotics (43%), sensors and automatic identification (40%), and 3D printing (40%).
professionals tend to select a proven developer for providing supply chain innovation, but many also pick start-ups. Forty-five percent said they work with a mix of new and established developers, compared to 39% who work with established technologies only.
there’s room to grow in partnering with 3PLs for innovation: only 13% said their 3PL identified a need for innovation, and just 8% partnered with a 3PL to bring a technology to life.