Many companies are excited about the possibility that artificial intelligence could improve the accuracy of their demand plans. However, there are several significant hurdles that they must overcome first.
Artificial intelligence (AI) continues to draw a lot of attention as companies and technology vendors look at how machine learning could improve supply chain operations. In particular demand planning, understood here as the process of developing forecasts that will drive operational supply chain decisions, is being touted as the next potential field for innovation. Technology giants like Amazon and Microsoft have announced AI tools for improving demand planning, and several consulting companies are promoting their skills to bring AI to companies' demand planning processes. In fact, a recent survey by the Institute of Business Forecasting and Planning (IBF) identified AI as the technology that will have the largest impact on demand planning in the next seven years.1
It's not hard to see the fit between AI and demand planning. Demand planning involves lots of number crunching and data analytics, and it is repeated cycle after cycle. Given the nature of the activity, it is tempting to imagine that a self-learning AI application could do at least as good a job as a human planner at forecasting demand.
A closer look, however, reveals that there are some serious challenges to AI successfully penetrating the demand planning market. These challenges are not so much technical as they are managerial. Even if AI does not become a significant contributor to demand planning accuracy, addressing these challenges can only improve a company's demand planning performance.
The need for data and digital savviness
The most striking challenge that companies face as they apply AI to demand planning is the availability and accuracy of data. The more data that is provided to an AI application, the more robust the resulting conclusions are, making data availability an essential foundation to a successful AI implementation. Internally, companies already struggle to maintain accurate data, even for the most basic of elements such as product code. Ever-accelerating product launches and shrinking product lifecycles mean more product churn than ever. One corporate head of planning that we spoke to said: "Let's show we can correctly link product codes in substitutions (where one product transitions into replacing another) before thinking about AI."
In addition to internal data, a good demand plan also requires external data in the form of market intelligence, such as competitor actions, customer behaviors, and trade disruptions like price changes and sell-out data.
Furthermore, all of this data needs to be interpreted correctly. For example, in order to build a correct demand plan, an accurate baseline for demand must be established. One-off events, such as service issues and one-time promotions, have to be identified and accounted for, otherwise they may skew the planner's understanding of the underlying demand. This initial step of cleansing the data for statistical treatment is often a critical source of error, as it requires a clean view of the history of past activity of the product. One demand planning expert we spoke to claimed that from his experience this step of trying to define a clean baseline accounts for 60 percent of demand planning errors. In order for an AI application to learn from these one-off events they would need to be fully understood and coded, which is no small effort.
In addition to these data challenges, many companies today struggle with their digital culture and level of savviness. We spoke to many large multinationals that have made serious investments in demand planning tools, and almost all of them face the same struggle: Their planners prefer to build demand plans in Excel first, and then upload them into the expensive, integrated demand planning tools they must use to propagate their demand plans. The usual explanation for this resistance is that the tools don't have enough of the internal and external contextual data to build pertinent statistical plans.
A recent survey by Arizona State University, Colorado State University, and Competitive Insights revealed that Excel is by far the most common analytical tool used by supply chain planners, with advanced tools like supply chain control towers used by about 60 percent of companies.2 This matches our anecdotal observation that only about half of companies use an advanced planning system (APS).
The absence of data, resistance to using the existing suite of statistical tools, and low level of digital savviness represent non-negligible challenges to the deployment of AI-enabled demand planning.
The need for one set of numbers
Demand planning is a critical activity in the sales and operations planning (S&OP) process. The objective of S&OP is to obtain alignment from all actors in the company, ideally ensuring that operations mobilizes its resources to supply what the business needs to meet its financial goals, while also ensuring that the financial goals account for the current operational constraints.
A fundamental pillar of the S&OP process is the notion of "one set of numbers," which means that operations and finance are working off a shared understanding of the forward planned activity for the business. The primary drivers for this goal are that no market opportunities are missed due to asupply/demand imbalance, and operations is focused on the true business needs rather than on an inflated demand that acts as a supplemental safety stock.
When a company ties its financial plan to the operational plan, general managers are driven to involve themselves—along with their commercial and marketing teams—in the demand planning process in order to have the most viable demand plan possible. Their involvement in the planning process is critical, as they can provide the demand planners with the valuable external market intelligence mentioned earlier. Just as importantly, from a managerial perspective, having one set of numbers means that any effort by general managers to manipulate the demand plan would also change the financial pan, which they are loath to do as it constitutes their commitment to executive leadership.
When AI is used to generate a demand plan, that demand plan becomes part of the "one set of numbers." Otherwise general managers would be tempted to return to old reflexes such as considering the demand plan outside their sphere of interest, not being as committed to providing demand planners with the necessary external data and market intelligence, and perhaps once again adjusting the numbers to their subjective tastes. But maintaining the tie between the AI-generated demand plan and the financial plan would require asking general managers to allow their financial projections to be generated by the AI application. This would be a consequential management hurdle for supply chains to overcome.
That's because the introduction of AI-generated demand plans would bring with it what is termed the "explainability problem" of AI.3 This term describes the reluctance managers have to using AI applications that seem like a "black box," where the reasoning and logic used to obtain the results are difficult to explain, even if they are of high quality. The explainability problem is currently a tangible hurdle for successful AI deployments and is even driving some AI proponents to suggest solutions that may be less accurate but more explainable to the target business community.4
Our research suggests very few companies today have truly achieved a "one set of numbers" in practice.5 Having a more accurate, AI-enabled demand plan at the expense of placing a serious obstacle to implementing S&OP (due to the explainability problem) does not seem like a necessarily winning trade off. In other words, are companies better off having a (perhaps) highly accurate AI-generated demand plan that does not reflect the true business activity due to lack of alignment, or a slightly less accurate non-AI generated one that is aligned with the business ambitions?
The explainability problem doesn't preclude the use of AI for demand planning, but it does suggest that it be considered only for companies that have achieved very high S&OP maturity and integration between the operational and financial planning activities. This maturity would likely correspond with both more digitally savvy demand planners and a higher confidence of general managers in the ability of the demand planners to provide an AI-generated demand plan that represents the most accurate view of the forward business activity.
AI as accelerator
The challenges to applying AI to demand planning shouldn't be seen as insurmountable hurdles. Rather, AI could be an accelerator that pushes companies to confront these data and managerial issues head-on. Indeed, even if AI does not become a significant contributor to demand planning accuracy, addressing these challenges—data availability and accuracy and a willingness to use sophisticated analytics tools—can only improve a company's demand planning performance.
A sound, participatory S&OP process that assembles and leverages robust and accurate internal and external data to reach a consensus number for both operations and finance should be the target for all companies. If the IBF survey prediction is correct that the coming years will see deep contributions from AI in demand planning, these fundamentals of data management of managerial processes will have made it possible.
5. Richard Markoff, "Who's in charge?: Sales and operations planning governance and alignment in the supply chain management of multinational industrial companies," https://www.theses.fr/2017PA01E015
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.”