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
Ron Marotta of Yusen Logistics listens to Rick DiMaio of Ace Hardware talk about the steps Ace is taking to keep its store stocked after Hurricane Helene and during the East and Gulf Coast Port Strike.
The East and Gulf Coast port strike was the top discussion point during a panel discussion of shippers and logistics providers at the Council of Supply Chain Management Professionals (CSCMP) annual EDGE Conference this morning. The session, which was supposed to be focused on providing an update to CSCMP’s “2024 State of Logistics Report,” quickly shifted to addressing the effect that the strike by nearly 50,000 dockworker at 36 ports in the Eastern half of the U.S. could have on supply chains.
“The seriousness of this action cannot to be taken lightly,” said Ron Marotta, vice president of the freight forwarder and supply chain service provider Yusen Logistics (America). “It has not happened since 1977. Our lives depend on sustaining a smooth global supply chain.”
Marotta warned that for every day that the ports were not open, it would take four to five days to recover from the impact. One added concern is how the port closures would affect recovery efforts for Hurricane Helene. “There’s a huge amount of item that would normally be replenished by importers and retailers,” Marotta said.
Rick DiMaio, executive vice president and chief supply chain officer, for Ace Hardware Corp., commented that the hardware retail cooperative was doing okay for now keeping stores in stock, although he did expect the company would be “chasing generators for awhile.” “But in this recovery phase [from the hurricane], we certainly don’t need a strike right now,” he said.
The port closure will also have a knock-on effect on other transportation modes. For example, Andy Moses, senior vice president of sales and solutions for logistics services provider Penske Logistics, expects to see some companies turn to air freight as a result of the strike. This will, in turn, cause air freight capacity to tighten up and rates to rise. Furthermore, the longer the ports are closed, the more likely inflation is to rise again, according to Moses.
Nor will the effects of the strike stop at the U.S. border, according to Marotta. Many Caribbean Island nations depend on food import from the U.S. that move through East Coast ports. Additionally, some medical supplies typically are exported through the ports to Europe.
On a positive note, however, many companies took actions earlier in the year to prepare themselves for a potential strike. Ammie McAsey, senior vice president of customer distribution experience for the pharmaceutical distributor McKesson, said the pharmaceutical industry has brought in enough extra inventory that there will not be a short-term impact on the U.S. health care system due to the strike.
Government intervention?
Marotta hopes that the U.S. government takes the step of invoking the Taft-Hartley Act to stop the strike and send the International Longshoremen’s Association (ILA) and the port management group, United States Maritime Alliance (USMX) back to the negotiation table. In 2002, for example, President George W. Bush used the Taft-Hartley Act to end an 11-day lockout of union workers at West Coast ports. President Joe Biden, however, told reporters on Sunday that he would not do this.
“I hope that cooler heads prevail and that the executive branch realizes that it’s not just a labor issue, it’s also a humanitarian issue,” Marotta said.
Confronted with the closed ports, most companies can either route their imports to standard East Coast destinations and wait for the strike to clear, or else re-route those containers to West Coast sites, incurring a three week delay for extra sailing time plus another week required to truck those goods back east, Ron said in an interview at the Council of Supply Chain Management Professionals (CSCMP)’s EDGE Conference in Nashville.
However, Uber Freight says its latest platform updates offer a series of mitigation options, including alternative routings, pre-booked allocation and volume during peak season, and providing daily visibility reports on shipments impacted by routings via U.S. east and gulf coast ports. And Ron said the company can also leverage its pool of some 2.3 million truck drivers who have downloaded its smartphone app, targeting them with freight hauling opportunities in the affected regions by pricing those loads “appropriately” through its surge-pricing model.
“If this [strike] continues a month, we will see severe disruptions,” Ron said. “So we can offer them alternatives. We say, if one door is closed, we can open another door? But even with that, there are no magic solutions.”
Turning around a failing warehouse operation demands a similar methodology to how emergency room doctors triage troubled patients at the hospital, a speaker said today in a session at the Council of Supply Chain Management Professionals (CSCMP)’s EDGE Conference in Nashville.
There are many reasons that a warehouse might start to miss its targets, such as a sudden volume increase or a new IT system implementation gone wrong, said Adri McCaskill, general manager for iPlan’s Warehouse Management business unit. But whatever the cause, the basic rescue strategy is the same: “Just like medicine, you do triage,” she said. “The most life-threatening problem we try to solve first. And only then, once we’ve stopped the bleeding, we can move on.”
In McCaskill’s comparison, just as a doctor might have to break some ribs through energetic CPR to get a patient’s heart beating again, a failing warehouse might need to recover by “breaking some ribs” in a business sense, such as making management changes or stock write-downs.
Once the business has made some stopgap solutions to “stop the bleeding,” it can proceed to a disciplined recovery, she said. And to reach their final goal, managers can use the classic tools of people, process, and technology to improve what she called the three most important key performance indicators (KPIs): on time in full (OTIF), inventory accuracy, and staff turnover.
CSCMP EDGE attendees gathered Tuesday afternoon for an update and outlook on the truckload (TL) market, which is on the upswing following the longest down cycle in recorded history. Kevin Adamik of RXO (formerly Coyote Logistics), offered an overview of truckload market cycles, highlighting major trends from the recent freight recession and providing an update on where the TL cycle is now.
EDGE 2024, sponsored by the Council of Supply Chain Management Professionals (CSCMP), is taking place this week in Nashville.
Citing data from the Coyote Curve index (which measures year-over-year changes in spot market rates) and other sources, Adamik outlined the dynamics of the TL market. He explained that the last cycle—which lasted from about 2019 to 2024—was longer than the typical three to four-year market cycle, marked by volatile conditions spurred by the Covid-19 pandemic. That cycle is behind us now, he said, adding that the market has reached equilibrium and is headed toward an inflationary environment.
Adamik also told attendees that he expects the new TL cycle to be marked by far less volatility, with a return to more typical conditions. And he offered a slate of supply and demand trends to note as the industry moves into the new cycle.
Supply trends include:
Carrier operating authorities are declining;
Employment in the trucking industry is declining;
Private fleets have expanded, but the expansion has stopped;
Truckload orders are falling.
Demand trends include:
Consumer spending is stable, but is still more service-centric and less goods-intensive;
After a steep decline, imports are on the rise;
Freight volumes have been sluggish but are showing signs of life.
CSCMP EDGE runs through Wednesday, October 2, at Nashville’s Gaylord Opryland Hotel & Resort.
The relationship between shippers and third-party logistics services providers (3PLs) is at the core of successful supply chain management—so getting that relationship right is vital. A panel of industry experts from both sides of the aisle weighed in on what it takes to create strong 3PL/shipper partnerships on day two of the CSCMP EDGE conference, being held this week in Nashville.
Trust, empathy, and transparency ranked high on the list of key elements required for success in all aspects of the partnership, but there are some specifics for each step of the journey. The panel recommended a handful of actions that should take place early on, including:
Establish relationships.
For 3PLs, understand and get to the heart of the shipper’s data.
Also for 3PLs: Understand the shipper’s reason for outsourcing to a 3PL, along with the shipper’s ultimate goals.
Understand company cultures and be sure they align.
Nurture long-term relationships with good communication.
For shippers, be transparent so that the 3PL fully understands your business.
And there are also some “non-negotiables” when it comes to managing the relationship:
3PLs must demonstrate their commitment to engaging with the shipper’s personnel.
3PLs must also demonstrate their commitment to process discipline, continuous improvement, and innovation.
Shippers should ensure that they understand the 3PL’s demonstrated implementation capabilities—ask to visit established clients.
Trust—which takes longer to establish than both sides may expect.
EDGE 2024 is sponsored by the Council of Supply Chain Management Professionals (CSCMP) and runs through Wednesday, October 2, at the Gaylord Opryland Resort & Convention Center in Nashville.