Supermodeling allows a company to take an end-to-end view of its supply chain and make adjustments in production, distribution, and inventory practices to meet changing market demands.
The outlook for global supply and demand is constantly changing, particularly under the current economic circumstances. The situation is fluid: Fundamental market dynamics shift, the balances and trade-offs in cost equations change, and solutions such as outsourcing and localized production may lose their value. In response, global companies reassess and reshape their supply chain networks and operations, taking future developments into account. No one can accurately predict the future, of course, but companies can plan ahead by defining potential scenarios, risks, and options, and then assessing the likely outcomes of each.
Manufacturers and logistics service providers often struggle with this complex task. They typically apply ad-hoc spreadsheets and inconsistent data-collection methods rather than tailored analytical tools and standardized procedures for gathering relevant data. The tools most companies currently use to help them analyze changing situations in operations and supply chains are not perfectly suited for the job. Most are either very detailed, making this exercise cumbersome and time-consuming, or they are limited and unable to analyze mid- and long-term scenarios. Moreover, many existing statistical models are based on a regression of historical data. But historical models are inadequate when fundamental supply chain parameters such as demand, markets, products, and cost drivers are facing significant and unprecedented changes.
[Figure 2] Transitional planning of production and distribution alternativesEnlarge this image
Supermodeling offers a solution. This modeling method takes an integrated view of the end-to-end supply chain, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution (see Figure 1). It studies historic and "as is" market and order data as well as "to be" market scenarios and demand forecasts. Such scenario-based simulation leads to better strategic supply and demand balancing because new products, expected price changes, and options for physical network changes are dynamically incorporated into the model. Finally, supermodeling —conceptualized and tailored to companies' specific business conditions —provides a fact-based approach for making difficult but necessary decisions that may encounter political and emotional resistance within the company.
How is supermodeling different?
In general terms, supermodeling provides a computer replica of a real or planned supply chain system — what one might call a "model world." The scope and content of the model —entire value chain, highly detailed breakdown of data, full transparency of feedback loops, and high reliability of options —is more comprehensive yet no more complex than the typical supply chain optimization tools. Supermodeling's broader focus can address a wider range of questions and issues, such as volatile commodity pricing and availability, shifting perceptions of market players, and conflicting trading or purchasing activities, that are not covered by traditional supply chain models.
Other types of supply chain optimization tools improve physical networks by looking at transportation, distribution, and labor costs in isolation —an approach that may produce unexpectedly costly results. Supermodeling, on the other hand, not only examines physical production and distribution costs but also takes into account operations planning aspects such as supply management, manufacturing planning, and delivery management. In other words, it assesses the impact of various cost and value drivers, such as labor, transportation, technology, and productivity, on the entire network. Since supermodeling can alter those parameters to develop different scenarios, it can demonstrate how those changes might affect customer behavior or supply chain performance.
Supermodeling's output is more visual than that of traditional supply chain optimization tools. The level of detail it produces is based on a careful assessment of what is required to respond to both strategic and tactical questions. Outputs typically represent key measures in finance, performance of physical process flows and virtual information flows, capacity utilization, stock levels, and customer service, all in the context of various rules and constraints imposed over time.
Accordingly, the model is able to demonstrate the expected benefits of reducing lead times by streamlining business processes, managing or reducing variance, and improving responsiveness and flexibility. This allows users to compare end-to-end supply chain scenarios —from quote to delivery to cash — with each other and with the current, as-is situation. A "supermodeled" replica of a supply chain thus provides the scope needed to determine the appropriate course of action based on future demand scenarios and trends.
The objectivity of this approach makes it a helpful tool for achieving consensus among stakeholders. Ultimately, supermodeling enables "boardroom experimentation." It allows companies' top management to test hypotheses and see a visualization of the answer right away. They can use the simulations to identify how best to balance demand and supply, examining such options as whether —and when —to open or close factories, move production to a different location, or shift inventories between distribution centers.
For example, a global manufacturer of health care equipment used supermodeling to optimize its order-to-cash process, including the physical network, from sourcing through manufacturing to the customer, as well as the information flows. For this company, supermodeling was particularly powerful at speeding up and simplifying the decision-making process because multiple scenarios could be run with the participation of key stakeholders. They could instantaneously see the impact of proposed changes in the supply chain network and consequently make better, consensus-driven decisions, even when opinions regarding a particular situation had been divided prior to using the supermodel.
To obtain optimal results, a supermodeling exercise should follow a four-step process, as illustrated in Figure 2:
Establish a baseline, simulating the as-is scenario to validate and calibrate the model. Run a base-case simulation, applying projected demand over time by product and by region. Identify areas that will require new supply decisions.
Define and evaluate the major external trends that are likely to have a long-term impact on supply and demand.
Identify potential changes to investigate, with the aim of minimizing costs while maintaining the best balance between supply and demand. Set up scenarios that reflect those options in the model, and then simulate market and supply performance.
Evaluate results by comparing output in terms of key performance indicators. Continue iterations, with multiple runs and "what if" sensitivity testing, until the most effective solution becomes evident.
For a brief look at how one company applied this process in a distribution-network analysis, see the sidebar "Four steps to supermodeling success."
Future scenarios, year by year
Companies that have employed the supermodeling approach have been able to reduce costs and free up working capital. They also generally do a better job of matching supply to demand as it evolves from month to month and from year to year. This modeling approach allowed the health care equipment manufacturer mentioned above to avoid a costly physical supply chain setup in Asia and substitute a more costefficient solution based on insight provided by the supermodel. A change in sourcing and better timing of order fulfillment deadlines allowed it to adopt a leaner physical distribution network, cheaper transportation, and fewer stockholding locations. The model not only delivered tens of millions of euros in potential savings but also developed a solution that the whole business could support.
A more detailed example is the case of VELUX, a manufacturer based in Denmark. A major player in the global building materials sector, its products include roof windows and skylights, many types of decoration and sun screening, roller shutters, installation products, and remote controls and thermal solar panels for roof installation. VELUX has manufacturing suppliers in 11 countries and sales companies in nearly 40 countries.
In 2007 and 2008, a project team consisting of VELUX's supply chain and manufacturing strategy specialists and supply chain consultants (including the authors) developed a dynamic supply chain model to study alternative ways of managing information and physical material flow between production, stockholding points, and markets. The primary objective was to build a supply chain model for strategic planning and evaluation of options that would be specific to VELUX. Through evaluation of different scenarios, the model would support strategic manufacturing initiatives and ongoing sales and operations planning for the six-month to five-year time frame.
Today the VELUX Supply Chain Model (VSCM) is used in the company's windows and flashings group for long-term planning. The model is predicated on a baseline setup; its "basis year" employs actual production and sales data and incorporates future expectations for sales, productivity, and projected costs for raw materials, labor, and transportation. The model provides VELUX with the ability to evaluate different scenarios by showing year-by-year development in capacity utilization, product and/or component flows, and even investment costs.
VSCM has enabled the company to increase both the number of alternative scenarios to study and compare as well as the scope, relevance, and quality of the output. VELUX has used the model to examine several European manufacturing and supply chain scenarios. On the basis of that analysis, the company made important decisions that significantly impact manufacturing, logistics, and financial value drivers, such as optimizing production's environmental footprint and designing a longer-term sales and operations planning process, to name just two.
Capitalize on change
Many companies are content to establish supply chain processes and structures, and then allow them to continue as is until they become obsolete and problems arise. Or they may find that business is developing in such a way that their existing supply chain operations and processes are unfit to exploit market opportunities or meet market challenges. In today's competitive environment, that is no longer a viable way of conducting business.
If a supply chain modeling effort is to provide truly valuable decision support, then it must be based on a deep understanding of a company's specific situation, requirements, and key issues. For this reason, it's necessary to have a collaborative effort among model developers, analysts, the company's own business experts, and other stakeholders throughout the supply chain.
What makes supermodeling an appropriate tool for achieving that objective is that it gives companies that are looking to capitalize on change the ability to re-examine their production and delivery networks by taking into account cost and value drivers in the endto- end supply chain, all within the context of future growth. This approach can make visible to company management the potential payback for selecting a particular course of action.
Four steps to supermodeling success
A successful supermodeling implementation requires working through four basic steps. The following is a reallife example of how one company applied this modeling technique.
The company used to have a decentralized structure with stockholding delivery and service centers (DCs) in 16 European countries. Initial analysis indicated that a more centralized production and distribution process might significantly reduce costs without compromising service.
The primary challenge was to identify how many and which warehouses to close, and what level of service to offer from the remaining locations. That decision would have to be made in tandem with changes in order management processes and stockholding policies. To address those considerations, the project team developed a dynamic supply chain "supermodel" scoped to simulate and study alternative ways of managing information and physical material flow between production, stockholding points, and end customers. The project proceeded as follows:
1. Development of a baseline case. The model was calibrated to simulate one year as-is operation, and a baseline scenario was created. Confidence in the model was established because it could simulate and reproduce history with numbers for total annual l ogistics costs, inventory levels, and delivery performance that approximated what had actually been the case that year.
2. Development of future trend scenarios. By manipulating inputs to the model, several simulated scenarios were produced, along with the associated effects on costs and service. This allowed the team to understand the issues and draw up a short list of realistic potential solutions.
3. Evaluation of options. With 16 DCs in the "as is" situation, a simple optimization exercise was infeasible. Complicating matters was the fact that all of the country directors were against closing "their" warehouses. It was important, therefore, to help them objectively evaluate and compare alternatives.
The model run proposed closing down various groups of DCs and simulated the outcomes. The model was tested for all kinds of "what ifs"; it proved and visualized how stock and transportation costs could be balanced more effectively, without compromising service to local customers. The solution that proved best —cutting back from 16 DCs to three —would allow the company to cover all of its markets in Europe while enjoying a 20-percent saving in logistics costs.
4. Evaluation of results. The model scenarios and options were developed by the company's wider European logistics organization, and members of the group jointly selected the best solution. The modeling approach helped to establish consensus among the stakeholders, avoiding the dangerous route of making decisions based on political and emotional resistance to change.
Companies in every sector are converting assets from fossil fuel to electric power in their push to reach net-zero energy targets and to reduce costs along the way, but to truly accelerate those efforts, they also need to improve electric energy efficiency, according to a study from technology consulting firm ABI Research.
In fact, boosting that efficiency could contribute fully 25% of the emissions reductions needed to reach net zero. And the pursuit of that goal will drive aggregated global investments in energy efficiency technologies to grow from $106 Billion in 2024 to $153 Billion in 2030, ABI said today in a report titled “The Role of Energy Efficiency in Reaching Net Zero Targets for Enterprises and Industries.”
ABI’s report divided the range of energy-efficiency-enhancing technologies and equipment into three industrial categories:
Commercial Buildings – Network Lighting Control (NLC) and occupancy sensing for automated lighting and heating; Artificial Intelligence (AI)-based energy management; heat-pumps and energy-efficient HVAC equipment; insulation technologies
Manufacturing Plants – Energy digital twins, factory automation, manufacturing process design and optimization software (PLM, MES, simulation); Electric Arc Furnaces (EAFs); energy efficient electric motors (compressors, fans, pumps)
“Both the International Energy Agency (IEA) and the United Nations Climate Change Conference (COP) continue to insist on the importance of energy efficiency,” Dominique Bonte, VP of End Markets and Verticals at ABI Research, said in a release. “At COP 29 in Dubai, it was agreed to commit to collectively double the global average annual rate of energy efficiency improvements from around 2% to over 4% every year until 2030, following recommendations from the IEA. This complements the EU’s Energy Efficiency First (EE1) Framework and the U.S. 2022 Inflation Reduction Act in which US$86 billion was earmarked for energy efficiency actions.”
Economic activity in the logistics industry expanded in November, continuing a steady growth pattern that began earlier this year and signaling a return to seasonality after several years of fluctuating conditions, according to the latest Logistics Managers’ Index report (LMI), released today.
The November LMI registered 58.4, down slightly from October’s reading of 58.9, which was the highest level in two years. The LMI is a monthly gauge of business conditions across warehousing and logistics markets; a reading above 50 indicates growth and a reading below 50 indicates contraction.
“The overall index has been very consistent in the past three months, with readings of 58.6, 58.9, and 58.4,” LMI analyst Zac Rogers, associate professor of supply chain management at Colorado State University, wrote in the November LMI report. “This plateau is slightly higher than a similar plateau of consistency earlier in the year when May to August saw four readings between 55.3 and 56.4. Seasonally speaking, it is consistent that this later year run of readings would be the highest all year.”
Separately, Rogers said the end-of-year growth reflects the return to a healthy holiday peak, which started when inventory levels expanded in late summer and early fall as retailers began stocking up to meet consumer demand. Pandemic-driven shifts in consumer buying behavior, inflation, and economic uncertainty contributed to volatile peak season conditions over the past four years, with the LMI swinging from record-high growth in late 2020 and 2021 to slower growth in 2022 and contraction in 2023.
“The LMI contracted at this time a year ago, so basically [there was] no peak season,” Rogers said, citing inflation as a drag on demand. “To have a normal November … [really] for the first time in five years, justifies what we’ve seen all these companies doing—building up inventory in a sustainable, seasonal way.
“Based on what we’re seeing, a lot of supply chains called it right and were ready for healthy holiday season, so far.”
The LMI has remained in the mid to high 50s range since January—with the exception of April, when the index dipped to 52.9—signaling strong and consistent demand for warehousing and transportation services.
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).
"After several years of mitigating inflation, disruption, supply shocks, conflicts, and uncertainty, we are currently in a relative period of calm," John Paitek, vice president, GEP, said in a release. "But it is very much the calm before the coming storm. This report provides procurement and supply chain leaders with a prescriptive guide to weathering the gale force headwinds of protectionism, tariffs, trade wars, regulatory pressures, uncertainty, and the AI revolution that we will face in 2025."
A report from the company released today offers predictions and strategies for the upcoming year, organized into six major predictions in GEP’s “Outlook 2025: Procurement & Supply Chain.”
Advanced AI agents will play a key role in demand forecasting, risk monitoring, and supply chain optimization, shifting procurement's mandate from tactical to strategic. Companies should invest in the technology now to to streamline processes and enhance decision-making.
Expanded value metrics will drive decisions, as success will be measured by resilience, sustainability, and compliance… not just cost efficiency. Companies should communicate value beyond cost savings to stakeholders, and develop new KPIs.
Increasing regulatory demands will necessitate heightened supply chain transparency and accountability. So companies should strengthen supplier audits, adopt ESG tracking tools, and integrate compliance into strategic procurement decisions.
Widening tariffs and trade restrictions will force companies to reassess total cost of ownership (TCO) metrics to include geopolitical and environmental risks, as nearshoring and friendshoring attempt to balance resilience with cost.
Rising energy costs and regulatory demands will accelerate the shift to sustainable operations, pushing companies to invest in renewable energy and redesign supply chains to align with ESG commitments.
New tariffs could drive prices higher, just as inflation has come under control and interest rates are returning to near-zero levels. That means companies must continue to secure cost savings as their primary responsibility.
Specifically, 48% of respondents identified rising tariffs and trade barriers as their top concern, followed by supply chain disruptions at 45% and geopolitical instability at 41%. Moreover, tariffs and trade barriers ranked as the priority issue regardless of company size, as respondents at companies with less than 250 employees, 251-500, 501-1,000, 1,001-50,000 and 50,000+ employees all cited it as the most significant issue they are currently facing.
“Evolving tariffs and trade policies are one of a number of complex issues requiring organizations to build more resilience into their supply chains through compliance, technology and strategic planning,” Jackson Wood, Director, Industry Strategy at Descartes, said in a release. “With the potential for the incoming U.S. administration to impose new and additional tariffs on a wide variety of goods and countries of origin, U.S. importers may need to significantly re-engineer their sourcing strategies to mitigate potentially higher costs.”
Freight transportation providers and maritime port operators are bracing for rough business impacts if the incoming Trump Administration follows through on its pledge to impose a 25% tariff on Mexico and Canada and an additional 10% tariff on China, analysts say.
Industry contacts say they fear that such heavy fees could prompt importers to “pull forward” a massive surge of goods before the new administration is seated on January 20, and then quickly cut back again once the hefty new fees are instituted, according to a report from TD Cowen.
As a measure of the potential economic impact of that uncertain scenario, transport company stocks were mostly trading down yesterday following Donald Trump’s social media post on Monday night announcing the proposed new policy, TD Cowen said in a note to investors.
But an alternative impact of the tariff jump could be that it doesn’t happen at all, but is merely a threat intended to force other nations to the table to strike new deals on trade, immigration, or drug smuggling. “Trump is perfectly comfortable being a policy paradox and pushing competing policies (and people); this ‘chaos premium’ only increases his leverage in negotiations,” the firm said.
However, if that truly is the new administration’s strategy, it could backfire by sparking a tit-for-tat trade war that includes retaliatory tariffs by other countries on U.S. exports, other analysts said. “The additional tariffs on China that the incoming US administration plans to impose will add to restrictions on China-made products, driving up their prices and fueling an already-under-way surge in efforts to beat the tariffs by importing products before the inauguration,” Andrei Quinn-Barabanov, Senior Director – Supplier Risk Management solutions at Moody’s, said in a statement. “The Mexico and Canada tariffs may be an invitation to negotiations with the U.S. on immigration and other issues. If implemented, they would also be challenging to maintain, because the two nations can threaten the U.S. with significant retaliation and because of a likely pressure from the American business community that would be greatly affected by the costs and supply chain obstacles resulting from the tariffs.”
New tariffs could also damage sensitive supply chains by triggering unintended consequences, according to a report by Matt Lekstutis, Director at Efficio, a global procurement and supply chain procurement consultancy. “While ultimate tariff policy will likely be implemented to achieve specific US re-industrialization and other political objectives, the responses of various nations, companies and trading partners is not easily predicted and companies that even have little or no exposure to Mexico, China or Canada could be impacted. New tariffs may disrupt supply chains dependent on just in time deliveries as they adjust to new trade flows. This could affect all industries dependent on distribution and logistics providers and result in supply shortages,” Lekstutis said.