Thomas H. Davenport is research director of The International Institute for Analytics, president's distinguished professor of IT and management at Babson College, and a senior advisor to Deloitte Consulting LLP.
Many companies today are aggressively employing analytics—the systematic use of quantitative and statistical decision methods—in their businesses. There are many different application domains for analytics, ranging from marketing to human resources to finance. It is only natural, then, that the next generation of supply chains should incorporate a higher and more sophisticated level of analytics.
Applying analytics in supply chain management is not a new idea. The U.S. military adopted a variety of logistical models in World War II, and companies adopted related approaches in the postwar period. UPS, for example, established a logistical analytics group in 1954. Since then, many companies have successfully employed analytical approaches to distribution networks, inventory optimization, forecasting, demand planning, risk management, and other applications. Large retailers, such as Wal-Mart Stores and Target, have had considerable success with supply chain analytics, often working in collaboration with suppliers. And carriers like UPS, FedEx, and Schneider National wouldn't dream of managing their operations without a variety of analytical models.
Yet supply chain-related analytics activities have plateaued in many organizations in recent years. Other than the occasional re-tuning of supply networks that has principally focused on cost management, companies have not taken advantage of all that supply chain analytics can offer to their businesses. Further, even when analytical tools are available to front-line supply chain personnel, the tools often go unused because of a lack of skills or understanding.
We believe that there will be a set of new frontiers in supply chain analytics that will lead to dramatically higher levels of performance. If companies are to achieve these rewards, however, they will have to be more ambitious in their analytical goals and investments. In this article we describe a number of relatively new domains for supply chain analytics as well as the opportunities and primary obstacles for each. We also describe several ways in which the day-to-day usage of supply chain analytics will change in the future.
Connect demand and supply in real time
One of the most important attributes of next-generation supply chain analytics is that they will address issues beyond the supply chain. To optimize operations, companies need to link their supply chains with metrics and analytics on the demand side. For example, at the simplest level, price changes or promotions for products will change demand and hence the required supply of those products. Similarly, changes in the availability of products and components should be reflected in marketing and sales processes.
This integration of supply and demand was pioneered in the 1990s by Dell Computer, which was able to suggest to call-center customers ways to shorten delivery time or take advantage of excess inventory. This was mostly dependent on human decision making: manufacturing supervisors would track supply levels and notify sales and marketing managers, who would then promote or downplay particular items and configurations based on their availability. But in a real-time, online business environment, companies will need to have analytical models in place that will continuously integrate supply and demand without human intervention. Such models would, for example, automatically extend offers and promotions to customers based on the availability of inventory and components. There has been a shortage of initiatives in this area since Dell's pioneering work, but the direction for future innovations is clear.
The analytics needed for such models are not terribly difficult, though they would require considerable iteration and tuning. The primary obstacle to implementation generally is a lack of collaboration among multiple transaction systems, in a way that allows companies to make informed decisions in real time.
Analyze supplier risk
Many companies recognize that the success of their operations is highly dependent upon their suppliers. Yet supplier risk analytics have hardly moved beyond simple metrics and reports in most organizations. The most sophisticated approaches to supplier risk monitoring and management—used by companies that heavily depend on external suppliers and contract manufacturers, such as Cisco Systems—are only somewhat more analytical.
One example is the creation of a supplier resiliency score based on several variables. The variables are based on logic (for instance, reports of bad weather near suppliers' manufacturing locations). If the variables or the overall resiliency scores suggest a problem, companies can then pursue secondary sourcing or work with existing suppliers to identify alternate locations. These scoring models increasingly incorporate relatively subjective factors, such as perceived economic and political risk. But while supplier risk and resiliency scores are undeniably useful tools, with few exceptions they are not yet based on statistical analysis.
Of particular interest to many companies now is whether critical suppliers that weathered the last economic downturn will be capable of meeting increased demand during an upturn. Analytic tools that incorporate public, third-party data can help companies assess this risk.
As companies accumulate more experience with supplier risk, they can begin to create predictive statistical models that are based on actual supplier failures. This would, of course, require tracking and analyzing a sufficient number of actual supplier failures to allow them to accurately identify attributes associated with failure.
Interestingly, the current leaders in statistically assessing supplier risk generally are not the manufacturers but the firms that insure them against such risk. Because the insurance industry has a strong actuarial tradition, firms such as Aon and Marsh have developed statistical models of the likelihood of supply and supplier risks. The key variables considered in these models are the frequency and severity of those risks.
Take advantage of sensors
One of the primary drivers of analytics in organizations is the availability of extensive data. As their use expands, new sensors—in particular, radio frequency identification (RFID)—will make dramatic amounts of data increasingly available for the next generation of supply chains.
For more than a decade, supply chain managers have been bombarded with warnings that RFID devices and networks will change their lives. Thus far, however, the high price of RFID technology has prevented widespread deployment from taking place. But prices for RFID tags and readers continue to fall, albeit slowly, and the adoption rate is gradually rising. At some point in the next several years, most manufacturers and retailers are expected to deploy some degree of RFID capability. When that happens, a great deal of RFID-generated data will be available for analysis. Initial applications using RFID data will primarily be transactional, but shortly thereafter organizations will want to monitor and optimize the efficiency and effectiveness of their RFID networks. This set of applications will demand the use of sophisticated supply chain analytics.
Some companies have employed RFID analytics for several years. For example, Daisy Brand, a dairy products manufacturer in the United States, began using RFID analytics in 2007 to track how long it takes products to reach the store shelf as well as replenishment rates. Prediction of replenishment rates is particularly important during promotions. In addition to RFID data, Daisy Brand also makes extensive use of Wal- Mart Stores' Retail Link data, which provides suppliers with weekly point-of-sale and inventory information, in its analyses.1
Sensors for more expensive and substantial supply chain assets are already in wide use. Some major carriers, for example, are deploying geographic positioning system (GPS)-based telematics devices in trucks and trains. These devices provide a wide variety of data about driving behavior, speeds under various conditions, traffic, and fuel consumption. Companies such as UPS and Schneider have already employed telematics data to redesign logistical networks in whole or in part. UPS, in fact, is using telematics data to redesign and optimize its entire delivery network for only the third time in its more than 100-year history.
Other types of sensors are likely to lead to a flood of additional data—and opportunities to analyze it. RFID and telematics sensors primarily track location, but so-called ILC (identification, location, condition) sensors can also monitor the condition of goods in the supply chain. ILC sensors monitor such variables as light, temperature, tilt angle, gravitational forces, and whether a package has been opened. They can transfer data in real time via cellular networks. Obviously, the potential to identify supply chain problems in real time and take immediate corrective action is greatly enhanced with this technology. We have only begun to consider how analytics might be used to enhance the value of ILC-derived data.
Improving analytical "literacy"
The next-generation approaches to supply chain analytics involve not only new applications but also new ways to ensure that analytics are used to make strategic and tactical decisions. Unfortunately, better decision making in supply chain management is often hindered by the inability of managers and front-line personnel to understand and apply analytical models.
We have encountered several companies that had considerably upgraded the analytical capabilities of their information systems (for example, by adding advanced planning and optimization modules for enterprise resource planning [ERP] systems) but had made no changes in associated personnel or their analytical skills. As one supply chain manager told us, "We need only half the people to do the work with these new tools, but they need to be twice as smart." For supply chain personnel to become smarter about analytics, they must be educated about analytics and their implications, retrained, or in some situations even replaced.
There are a variety of approaches to achieving the desired level of analytical literacy. The motor carrier Schneider National, for example, has developed a simulation- based game to communicate the importance of analytical thinking in dispatching trucks and trailers. The goal of the game is to minimize variable costs for a given amount of revenue while maximizing the driver's time on the road. Decisions to accept loads or move empty trucks are made by the players, who are aided by decision-support tools. Schneider uses the game to help its own personnel understand the value of analytical decision aids, to communicate the dynamics of the business, and to change the mindset of employees from "order takers" to "profit makers." Some Schneider customers have also played the game.
Another way to facilitate the understanding of supply chain analytics is through simpler applications with narrow functionality. Increasingly referred to as "analytical apps," these tools are similar to the applications found on smartphones. They support a single decision and often are industry-specific. Several business intelligence and analytics software vendors are introducing them, and they promise to make the use of analytics much simpler and available to users who do not have extensive analytical or technological skills. Analytical apps that have already been developed for supply chain functions include tools for supplier evaluation, inventory performance analysis, transportation analytics, and transportation contract compliance. There undoubtedly will be many others over the next several years.
Perhaps the only way to guarantee the use of analytics in supply chain management is to embed them into supply chain-oriented systems and processes. No human would be involved in the decision unless there is an exception. For example, certain supply chain decisions made at least partially on the basis of statistics and probability (such as available-to-promise inventory, or the likelihood that an ordered product will be returned by the customer) could be embedded in an order management system. Vendors of ERP systems expect to have such capabilities in the next several years.
The future of supply chain analytics
The use of such tools as ERP systems, the Internet, RFID, and telematics is becoming more common, and more organizations are generating considerable amounts of high-quality data. Now that companies have more and better data than ever before, it is only natural that they would begin to use it to analyze, optimize, and make predictions about their supply chains.
The most common analytical activities thus far have been descriptive—straightforward reports about what has happened in the past. But in future supply chains, we expect to see more prediction and even prescription—that is, optimization and testing models that tell supply chain managers what they should do to improve performance.
Employing emerging supply chain technologies and process improvements has always been an important path to competitive advantage. We believe the next major approach to supply chain-based competition will involve the extensive use of analytics.
Endnote:
1. Claire Swedberg, "Daisy Brand Benefits From RFID Analytics," RFID Journal, January 8, 2008.
Business software vendor Cleo has acquired DataTrans Solutions, a cloud-based procurement automation and EDI solutions provider, saying the move enhances Cleo’s supply chain orchestration with new procurement automation capabilities.
According to Chicago-based Cleo, the acquisition comes as companies increasingly look to digitalize their procurement processes, instead of relying on inefficient and expensive manual approaches.
By buying Texas-based DataTrans, Cleo said it will gain an expanded ability to help businesses streamline procurement, optimize working capital, and strengthen supplier relationships. Specifically, by integrating DTS’s procurement automation capabilities, Cleo will be able to provide businesses with solutions including: a supplier EDI & testing portal; web EDI & PDF digitization; and supplier scorecarding & performance tracking.
“Cleo’s vision is to deliver true supply chain orchestration by bridging the gap between planning and execution,” Cleo President and CEO Mahesh Rajasekharan said in a release. “With DTS’s technology embedded into CIC, we’re empowering procurement teams to reduce costs, improve efficiency, and minimize supply chain risks—all through automation.”
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.
Even as a last-minute deal today appeared to delay the tariff on Mexico, that deal is set to last only one month, and tariffs on the other two countries are still set to go into effect at midnight tonight.
Once new U.S. tariffs go into effect, those other countries are widely expected to respond with retaliatory tariffs of their own on U.S. exports, that would reduce demand for U.S. and manufacturing goods. In the context of that unpredictable business landscape, many U.S. business groups have been pressuring the White House to pull back from the new policy.
Here is a sampling of the reaction to the tariff plan by the U.S. business community:
American Association of Port Authorities (AAPA)
“Tariffs are taxes,” AAPA President and CEO Cary Davis said in a release. “Though the port industry supports President Trump’s efforts to combat the flow of illicit drugs, tariffs will slow down our supply chains, tax American businesses, and increase costs for hard-working citizens. Instead, we call on the Administration and Congress to thoughtfully pursue alternatives to achieving these policy goals and exempt items critical to national security from tariffs, including port equipment.”
Retail Industry Leaders Association (RILA)
“We understand the president is working toward an agreement. The leaders of all four nations should come together and work to reach a deal before Feb. 4 because enacting broad-based tariffs will be disruptive to the U.S. economy,” Michael Hanson, RILA’s Senior Executive Vice President of Public Affairs, said in a release. “The American people are counting on President Trump to grow the U.S. economy and lower inflation, and broad-based tariffs will put that at risk.”
National Association of Manufacturers (NAM)
“Manufacturers understand the need to deal with any sort of crisis that involves illicit drugs crossing our border, and we hope the three countries can come together quickly to confront this challenge,” NAM President and CEO Jay Timmons said in a release. “However, with essential tax reforms left on the cutting room floor by the last Congress and the Biden administration, manufacturers are already facing mounting cost pressures. A 25% tariff on Canada and Mexico threatens to upend the very supply chains that have made U.S. manufacturing more competitive globally. The ripple effects will be severe, particularly for small and medium-sized manufacturers that lack the flexibility and capital to rapidly find alternative suppliers or absorb skyrocketing energy costs. These businesses—employing millions of American workers—will face significant disruptions. Ultimately, manufacturers will bear the brunt of these tariffs, undermining our ability to sell our products at a competitive price and putting American jobs at risk.”
American Apparel & Footwear Association (AAFA)
“Widespread tariff actions on Mexico, Canada, and China announced this evening will inject massive costs into our inflation-weary economy while exposing us to a damaging tit-for-tat tariff war that will harm key export markets that U.S. farmers and manufacturers need,” Steve Lamar, AAFA’s president and CEO, said in a release. “We should be forging deeper collaboration with our free trade agreement partners, not taking actions that call into question the very foundation of that partnership."
Healthcare Distribution Alliance (HDA)
“We are concerned that placing tariffs on generic drug products produced outside the U.S. will put additional pressure on an industry that is already experiencing financial distress. Distributors and generic manufacturers and cannot absorb the rising costs of broad tariffs. It is worth noting that distributors operate on low profit margins — 0.3 percent. As a result, the U.S. will likely see new and worsened shortages of important medications and the costs will be passed down to payers and patients, including those in the Medicare and Medicaid programs,” the group said in a statement.
National Retail Federation (NRF)
“We support the Trump administration’s goal of strengthening trade relationships and creating fair and favorable terms for America,” NRF Executive Vice President of Government Relations David French said in a release. “But imposing steep tariffs on three of our closest trading partners is a serious step. We strongly encourage all parties to continue negotiating to find solutions that will strengthen trade relationships and avoid shifting the costs of shared policy failures onto the backs of American families, workers and small businesses.”
In a statement, DCA airport officials said they would open the facility again today for flights after planes were grounded for more than 12 hours. “Reagan National airport will resume flight operations at 11:00am. All airport roads and terminals are open. Some flights have been delayed or cancelled, so passengers are encouraged to check with their airline for specific flight information,” the facility said in a social media post.
An investigation into the cause of the crash is now underway, being led by the National Transportation Safety Board (NTSB) and assisted by the Federal Aviation Administration (FAA). Neither agency had released additional information yet today.
First responders say nearly 70 people may have died in the crash, including all 60 passengers and four crew on the American Airlines flight and three soldiers in the military helicopter after both aircraft appeared to explode upon impact and fall into the Potomac River.
Editor's note:This article was revised on February 3.
GE Vernova today said it plans to invest nearly $600 million in its U.S. factories and facilities over the next two years to support its energy businesses, which make equipment for generating electricity through gas power, grid, nuclear, and onshore wind.
The company was created just nine months ago as a spin-off from its parent corporation, General Electric, with a mission to meet surging global electricity demands. That move created a company with some 18,000 workers across 50 states in the U.S., with 18 U.S. manufacturing facilities and its global headquarters located in Massachusetts. GE Vernova’s technology helps produce approximately 25% of the world’s energy and is currently deployed in more than 140 countries.
The new investments – expected to create approximately 1,500 new U.S. jobs – will help drive U.S. energy affordability, national security, and competitiveness, and enable the American manufacturing footprint needed to support expanding global exports, the company said. They follow more than $167 million in funding in 2024 across a range of GE Vernova sites, helping create more than 1,120 jobs. And following a forecast that worldwide energy needs are on pace to double, GE Vernova is also planning a $9 billion cumulative global capex and R&D investment plan through 2028.
The new investments include:
almost $300 million in support of its Gas Power business and build-out of capacity to make heavy duty gas turbines, for facilities in Greenville, SC, Schenectady, NY, Parsippany, NJ, and Bangor, ME.
nearly $20 million to expand capacity at its Grid Solutions facilities in Charleroi, PA, which manufactures switchgear, and Clearwater, FL, which produces capacitors and instrument transformers.
more than $50 million to enhance safety, quality and productivity at its Wilmington, NC-based GE Hitachi nuclear business and to launch its next generation nuclear fuel design.
nearly $100 million in its manufacturing facilities at U.S. onshore wind factories in Pensacola, FL, Schenectady, NY and Grand Forks, ND, and its remanufacturing facilities in Amarillo, TX.
more than $10 million in its Pittsburgh, PA facility to expand capabilities across its Electrification segment, adding U.S. manufacturing capacity to support the U.S. grid, and demand for solar and energy storage
almost $100 million for its energy innovation research hub, the Advanced Research Center in Niskayuna, NY, to strengthen the center’s electrification and carbon efforts, enable continued recruitment of top-tier talent, and push forward innovative technologies, including $15 million for Generative Artificial Intelligence (AI) work.
“These investments represent our serious commitment and responsibility as the leading energy manufacturer in the United States to help meet America’s and the world’s accelerating energy demand,” Scott Strazik, CEO of GE Vernova, said in a release. “These strategic investments and the jobs they create aim to both help our customers meet the doubling of demand and accelerate American innovation and technology development to boost the country’s energy security and global competitiveness.”