Apparently it does. Companies that match their supply chains to the demand aspects of their products enjoy a higher market capitalization than those without a good supply chain fit.
Whether it's on Wall Street or Fleet Street, investors take notice of companies that have effective supply chains. Manufacturers with well-run supply chains command a higher valuation because they have mastered the match between demand and supply for their product. Companies that have not achieved this alignment, on the other hand, experience delivery delays, quality issues, and excessively high inbound logistics costs, all of which have a negative effect on their financial performance. In short, successful supply chain management equates to the ability to create shareholder value.
One of the key factors for achieving effective supply chain management (and therefore financial success) is having the right "fit" between the demand aspects of a product and the design of its underlying supply chain. For example, innovative products with unpredictable demand are best served by a responsive supply chain that is able to meet quick turnaround times and make the most of short product lifecycles. Functional products with predictable levels of demand, by contrast, are best served by an efficient supply chain that focuses on minimizing costs.
And yet, many companies still fail to adjust their supply chain strategies to match the underlying product.1 Granted, it's not easy. Most companies deliver a number of products in parallel, which complicates the alignment of supply chains with product portfolios. Additionally, companies must continually reformulate their supply chain fit as they adopt new product lines, enter new markets, build new warehouses and production plants, and lose the protection of traditional industry barriers.
It makes sense, then, that achieving supply chain fit would have a positive impact on a company's financial position. To test this hypothesis and determine to what degree supply chain fit affects financial success, we surveyed the largest manufacturing companies in the United States and Europe. Our financial analysis of 259 U.S. and European manufacturers shows that those companies demonstrating a good supply chain fit have a market capitalization (or the total market value of all of a company's outstanding shares) that is approximately 19 percent higher than that of counterparts that do not have a good supply chain fit. This article explores that critical link between supply chain fit and corporate performance in terms of market capitalization. Specifically, we demonstrate how companies that have achieved a supply chain fit outperform Standard & Poor's S&P 500 index, an index of stock performance of 500 leading U.S. companies in a number of industries.
Do you have a good fit?
Our concept of supply chain fit is based on a framework developed by Marshall Fisher in his seminal 1997 Harvard Business Review article, "What is the right supply chain for your product?" and further developed by Sunil Chopra and Peter Meindl in their book Supply Chain Management: Strategy, Planning, and Operation.
Top companies achieve supply chain fit by understanding the demand aspects of their products, building a supply chain with the capabilities needed to satisfy its targeted customer segments, and aligning the supply chain strategy to the overall competitive strategy of the company. To achieve supply chain fit, then, supply chain managers must take the following steps:
1. Understand the product's demand and supply uncertainty levels. To devise the right supply chain strategy for a product, you must first understand where it lies on the "uncertainty spectrum"—in other words, how unpredictable demand and supply for that product is. Is it a functional product with a predictable level of demand, an innovative product with unpredictable demand, or something in between?
It is important to understand customers' needs for each targeted segment and the uncertainty that the supply chain faces in satisfying those needs. Next, combine demand and supply uncertainty for the underlying product and map the results on the implied uncertainty spectrum. This helps to identify the level of demand unpredictability, disruption, and delay that the supply chain must be prepared to handle. (The implied uncertainty spectrum is shown along the x-axis in Figure 1.)
2. Assess your supply chain capabilities. Assess what type of supply chain you have: Is it a responsive supply chain or an efficient supply chain? A highly responsive supply chain is able to create innovative products, handle large varieties of products, fill a wide range of product quantities, and meet requests for very tight lead times and high service levels.
Unfortunately, responsiveness is not free. For every strategic choice to increase responsiveness, additional costs are incurred and efficiency declines. A more efficient supply chain, on the other hand, would focus on ways to cut costs in the supply chain at the expense of some responsiveness. (The responsiveness spectrum is shown along the y-axis in Figure 1.)
3. Match the level of responsiveness to the level of uncertainty. Next, you need to ensure that the degree of supply chain responsiveness is consistent with the implied uncertainty level. The goal is high responsiveness for a supply chain facing high implied uncertainty and efficiency for a supply chain facing low implied uncertainty,2 as shown in Figure 1.
By achieving supply chain fit, a company ensures that its supply chain strategy is sufficiently linked to its overall competitive strategy and that its supply chain capabilities help it satisfy the company's target customers. Any misalignment between strategic vision (or the strategy for a product) and execution (or the strategy for the product's supply chain) presents a significant improvement opportunity for a company.
To succeed, however, companies will need to develop a new set of strategic managerial competencies. Managers must be able to view the company holistically with a thorough understanding of the linkages among functions. This will not be easy; in many companies, different departments devise different competitive and functional strategies. Without proper information sharing between departments and coordination by executives, companies are not likely to achieve supply chain fit.
Calculating supply chain fit
To find out whether achieving supply chain fit affected a company's financial position, we contacted 1,834 supply chain, logistics, and purchasing executives at the 1,000 largest manufacturing companies in the United States, the United Kingdom, Germany, Austria, Switzerland, and France. We received 259 responses. The respondents have a very good knowledge of their companies' main product lines, supply chain structure, and supplier base. On average, they have worked in the fields of procurement, logistics, supply chain, production, or related fields for 13.2 years. They have held their current positions for 3.9 years and have worked for their current employers for 9.9 years. Figure 2 summarizes the respondents' characteristics. (For a more detailed breakdown of respondents by title, function, company size, and industry sector, see Figure 3.)
We asked respondents a series of questions that helped them assign their companies a score for product innovativeness (demand uncertainty) and a score for supply chain responsiveness. Product innovativeness was measured in terms of product lifecycle; number of available variants; average forecast error; number of sales locations; and frequency of order changes in terms of content, size, delivery time, or other patterns. As outlined in Figure 4, supply chain responsiveness was measured in terms of delivery reliability, buffer inventory of parts or finished goods, buffer capacity in manufacturing, quick response to unpredictable demand, and frequency of new product introductions.3
Companies achieve a high degree of fit when the degree of supply chain responsiveness matches the degree of product innovativeness. Supply chain fit can therefore be calculated by measuring the difference between those two factors. Accordingly, we computed a supply chain fit (SCF) index for each company as follows:
SCF = |PI - SCR|
where PI is the standardized score for product innovativeness (the degree of demand uncertainty for the product) and SCR is the standardized score for supply chain responsiveness. The ideal supply chain fit score would be 0, indicating that the supply chain responsiveness exactly fit the level of product innovativeness (demand uncertainty). Any deviation from zero indicates the degree of misfit.4 (For a simplified example of the computation for two similar companies, see the above sidebar "A sample fit assessment.")
Impact of supply chain fit
To differentiate between companies with and without a supply chain fit, the data sample was split into two groups: "supply chain fit companies," whose supply chains meet their products' requirements, and "supply chain misfit companies," whose supply chains do not meet their products' requirements. Supply chain fit companies comprised all cases with +/- one standard deviation (0.61) around the arithmetic mean (N = 163). Supply chain misfit companies constituted the remaining cases (N = 96). Figure 5 includes a list of the 10 companies from our survey with the best supply chain fit.
In order to investigate the financial impact of supply chain fit, we analyzed whether the 163 companies with a supply chain fit outperformed the S&P 500 Index. We developed a market-capitalization index consisting of daily share prices of supply chain fit companies, and then measured it against the S&P 500 Index between Quarter 1 of 2005 and Quarter 4 of 2008. Our results indicate that market capitalization of supply chain fit companies outperforms the S&P 500 Index on average by 18.9 percent—and by as much as 44.5 percent (see Figure 6).
This finding fits very well with previous research,5 which indicated that companies that adapt their supply chains to the demand aspects of their products achieve superior profitability—up to 100-percent higher profits in terms of sales growth, earnings before interest and tax (EBIT) margins, return on assets (ROA), and return on capital employed (ROCE).
A call to action
This research shows that the impact of supply chain management on a company's financial success is much greater than classic logistics key performance indicators (KPIs) may suggest. A well-run supply chain helps companies to not only reduce costs but also to improve profitability. Indeed, the concept of supply chain fit can be used to identify key supply chain metrics that tie directly to the three key components of economic value added (EVA)—revenue, costs, and assets. As a consequence, the concept of supply chain fit can also be used to show how supply chain initiatives can help improve a company's market capitalization.
Despite the clear benefits of achieving supply chain fit, 37 percent of companies have not yet achieved that goal. Many companies, therefore, have a significant opportunity to boost their financial performance by improving their supply chain fit.
There are several important steps companies can take to move in that direction. First, supply chain management should be represented in the highest echelons of management. This will help to ensure that corporate management understands how supply chain performance impacts market capitalization. Second, everyone who is responsible for managing supply chain activities must be aware of the company's financial performance metrics, so that decisions made at the operational level are tied to expected outcomes. Third, executives have to understand how supply chain fit is achieved, maintained, and continuously adapted. And finally, a process must be established to educate those in operational roles on the impact of their daily actions on the company's overall performance.
It's important to bear in mind, however, that supply chain fit is a dynamic concept. Because customer preferences—and thus the demand aspects of products—are always in flux, any supply chain fit can only be temporary. Therefore, a manufacturing company must always be adapting and aligning its competitive strategy (and resulting implied uncertainty) and supply chain strategy (and resulting responsiveness) as closely as possible.
Endnotes: 1. See, for example, D. Li and C. O'Brien, "A quantitative analysis of relationships between product types and supply chain strategies," International Journal of Production Economics, vol. 73, no. 1 (2001): pp. 29-39; G.N. Stock, N.P. Greis, and J.D. Kasarda, "Enterprise logistics and supply chain structure: The role of fit," Journal of Operations Management, vol. 18, no. 5 (2000): pp. 531-547; and D.H. Doty, W.H. Glick, and G.P. Huber, "Fit, equifinality, and organizational effectiveness: A test of two configurational theories," Academy of Management Journal, vol. 36, no. 6 (1993): pp. 1196-1250. 2. S. Chopra and P. Meindl, Supply Chain Management—Strategy, Planning, and Operation, 4th edition, (Upper Saddle River, New Jersey: Pearson Education, 2010). 3. The criteria that we used to assess innovativeness and supply chain responsiveness are suggested by Marshall Fisher in his Harvard Business Review article, "What is the right supply chain for your product?" Vol. 75, no. 2 (1997): pp. 105-116. 4. Similar "fit" procedures have been applied in the literature. For example, see C. Gresov, "Exploring fit and misfit with multiple contingencies," Administrative Science Quarterly, vol. 34, no. 3 (1989): pp. 431-453; N. Venkatraman and J.E. Prescott, "Environment-strategy coalignment: An empirical test of its performance implications," Strategic Management Journal, vol. 11, no. 1 (1990): pp. 1-23; J.A. Siguaw, G. Brown, and R.E. Widing II, "The influence of market orientation of the firm on sales force behavior and attitudes," Journal of Marketing Research, vol. 31, no. 1 (1994): pp. 106-116; and D. Miller, "Stale in the saddle: CEO tenure and the match between organization and environment," Management Science, vol. 37, no. 1 (1991): pp. 34-52. 5. P.T. Grosse-Ruyken, S.M. Wagner, and F. Erhun, "The bottom line impact of supply chain management: The impact of a fit in the supply chain on a firm's financial success," working paper, Zurich: Swiss Federal Institute of Technology Zurich, 2009.
A sample fit assessment
To demonstrate how we assess supply chain fit, let's look at a simplified example involving two electronics manufacturers. Both manufacture a DVD player, which is a standardized product. However, manufacturer A achieves a supply chain fit, and manufacturer B does not. Why? After gathering empirical data from both electronics manufacturers about their supply chain responsiveness and product innovativeness, we assessed those factors on five-point scales (see chart).
In this example, electronics manufacturer A has a deviation from the ideal profile (0, or perfect alignment of product and supply chain) of only 0.2. This represents a supply chain fit degree of 95 percent [(100% - (0.2/(5 - 1))]. In other words, electronics manufacturer A has achieved a supply chain fit.
It is evident that the product and supply chain of electronics manufacturer B are not adapted to each other, nor are they sufficiently aligned. For its DVD player, manufacturer B has a low level of product innovativeness but a high level of supply chain responsiveness. Thus, manufacturer B achieves a fit degree of only 45 percent [(100% - (2.2/(5 - 1))] and fails to achieve a supply chain fit.
Specifically, the new global average robot density has reached a record 162 units per 10,000 employees in 2023, which is more than double the mark of 74 units measured seven years ago.
Broken into geographical regions, the European Union has a robot density of 219 units per 10,000 employees, an increase of 5.2%, with Germany, Sweden, Denmark and Slovenia in the global top ten. Next, North America’s robot density is 197 units per 10,000 employees – up 4.2%. And Asia has a robot density of 182 units per 10,000 persons employed in manufacturing - an increase of 7.6%. The economies of Korea, Singapore, mainland China and Japan are among the top ten most automated countries.
Broken into individual countries, the U.S. ranked in 10th place in 2023, with a robot density of 295 units. Higher up on the list, the top five are:
The Republic of Korea, with 1,012 robot units, showing a 5% increase on average each year since 2018 thanks to its strong electronics and automotive industries.
Singapore had 770 robot units, in part because it is a small country with a very low number of employees in the manufacturing industry, so it can reach a high robot density with a relatively small operational stock.
China took third place in 2023, surpassing Germany and Japan with a mark of 470 robot units as the nation has managed to double its robot density within four years.
Germany ranks fourth with 429 robot units for a 5% CAGR since 2018.
Japan is in fifth place with 419 robot units, showing growth of 7% on average each year from 2018 to 2023.
Progress in generative AI (GenAI) is poised to impact business procurement processes through advancements in three areas—agentic reasoning, multimodality, and AI agents—according to Gartner Inc.
Those functions will redefine how procurement operates and significantly impact the agendas of chief procurement officers (CPOs). And 72% of procurement leaders are already prioritizing the integration of GenAI into their strategies, thus highlighting the recognition of its potential to drive significant improvements in efficiency and effectiveness, Gartner found in a survey conducted in July, 2024, with 258 global respondents.
Gartner defined the new functions as follows:
Agentic reasoning in GenAI allows for advanced decision-making processes that mimic human-like cognition. This capability will enable procurement functions to leverage GenAI to analyze complex scenarios and make informed decisions with greater accuracy and speed.
Multimodality refers to the ability of GenAI to process and integrate multiple forms of data, such as text, images, and audio. This will make GenAI more intuitively consumable to users and enhance procurement's ability to gather and analyze diverse information sources, leading to more comprehensive insights and better-informed strategies.
AI agents are autonomous systems that can perform tasks and make decisions on behalf of human operators. In procurement, these agents will automate procurement tasks and activities, freeing up human resources to focus on strategic initiatives, complex problem-solving and edge cases.
As CPOs look to maximize the value of GenAI in procurement, the study recommended three starting points: double down on data governance, develop and incorporate privacy standards into contracts, and increase procurement thresholds.
“These advancements will usher procurement into an era where the distance between ideas, insights, and actions will shorten rapidly,” Ryan Polk, senior director analyst in Gartner’s Supply Chain practice, said in a release. "Procurement leaders who build their foundation now through a focus on data quality, privacy and risk management have the potential to reap new levels of productivity and strategic value from the technology."
Businesses are cautiously optimistic as peak holiday shipping season draws near, with many anticipating year-over-year sales increases as they continue to battle challenging supply chain conditions.
That’s according to the DHL 2024 Peak Season Shipping Survey, released today by express shipping service provider DHL Express U.S. The company surveyed small and medium-sized enterprises (SMEs) to gauge their holiday business outlook compared to last year and found that a mix of optimism and “strategic caution” prevail ahead of this year’s peak.
Nearly half (48%) of the SMEs surveyed said they expect higher holiday sales compared to 2023, while 44% said they expect sales to remain on par with last year, and just 8% said they foresee a decline. Respondents said the main challenges to hitting those goals are supply chain problems (35%), inflation and fluctuating consumer demand (34%), staffing (16%), and inventory challenges (14%).
But respondents said they have strategies in place to tackle those issues. Many said they began preparing for holiday season earlier this year—with 45% saying they started planning in Q2 or earlier, up from 39% last year. Other strategies include expanding into international markets (35%) and leveraging holiday discounts (32%).
Sixty percent of respondents said they will prioritize personalized customer service as a way to enhance customer interactions and loyalty this year. Still others said they will invest in enhanced web and mobile experiences (23%) and eco-friendly practices (13%) to draw customers this holiday season.
The practice consists of 5,000 professionals from Accenture and from Avanade—the consulting firm’s joint venture with Microsoft. They will be supported by Microsoft product specialists who will work closely with the Accenture Center for Advanced AI. Together, that group will collaborate on AI and Copilot agent templates, extensions, plugins, and connectors to help organizations leverage their data and gen AI to reduce costs, improve efficiencies and drive growth, they said on Thursday.
Accenture and Avanade say they have already developed some AI tools for these applications. For example, a supplier discovery and risk agent can deliver real-time market insights, agile supply chain responses, and better vendor selection, which could result in up to 15% cost savings. And a procure-to-pay agent could improve efficiency by up to 40% and enhance vendor relations and satisfaction by addressing urgent payment requirements and avoiding disruptions of key services
Likewise, they have also built solutions for clients using Microsoft 365 Copilot technology. For example, they have created Copilots for a variety of industries and functions including finance, manufacturing, supply chain, retail, and consumer goods and healthcare.
Another part of the new practice will be educating clients how to use the technology, using an “Azure Generative AI Engineer Nanodegree program” to teach users how to design, build, and operationalize AI-driven applications on Azure, Microsoft’s cloud computing platform. The online classes will teach learners how to use AI models to solve real-world problems through automation, data insights, and generative AI solutions, the firms said.
“We are pleased to deepen our collaboration with Accenture to help our mutual customers develop AI-first business processes responsibly and securely, while helping them drive market differentiation,” Judson Althoff, executive vice president and chief commercial officer at Microsoft, said in a release. “By bringing together Copilots and human ambition, paired with the autonomous capabilities of an agent, we can accelerate AI transformation for organizations across industries and help them realize successful business outcomes through pragmatic innovation.”
That challenge is one of the reasons that fewer shoppers overall are satisfied with their shopping experiences lately, Lincolnshire, Illinois-based Zebra said in its “17th Annual Global Shopper Study.” While 85% of shoppers last year were satisfied with both the in-store and online experiences, only 81% in 2024 are satisfied with the in-store experience and just 79% with online shopping.
In response, most retailers (78%) say they are investing in technology tools that can help both frontline workers and those watching operations from behind the scenes to minimize theft and loss, Zebra said.
Just 38% of retailers currently use artificial intelligence-based prescriptive analytics for loss prevention, but a much larger 50% say they plan to use it in the next one to three years. Retailers also said they plan to invest in self-checkout cameras and sensors (45%), computer vision (46%), and RFID tags and readers (42%) within the next three years to help with loss prevention.
Those strategies could help improve the brick-and-mortar shopping experience, as 78% of shoppers say it’s annoying when products are locked up or secured within cases. Part of that frustration, according to consumers, is fueled by the extra time it takes to find an associate to them unlock those cases. Seventy percent of consumers say they have trouble finding sales associates to help them during in-store shopping. In response, some just walk out; one in five shoppers has left a store without getting what they needed because a retail associate wasn’t available to help, an increase over the past two years.
Additional areas of frustrations identified by retailers and associates include:
The difficulty of implementing "click and collect" or in-story returns, despite high shopper demand for them;
The struggle to confirm current inventory and pricing;
Lingering labor shortages; and
Increasing loss incidents.
“Many retailers are laying the groundwork to build a modern store experience,” Matt Guiste, Global Retail Technology Strategist, Zebra Technologies, said in a release. “They are investing in mobile and intelligent automation technologies to help inform operational decisions and enable associates to do the things that keep shoppers happy.”
The survey was administered online by Azure Knowledge Corporation and included 4,200 adult shoppers (age 18+), decision-makers, and associates, who replied to questions about the topics of shopper experience, device and technology usage, and delivery and fulfillment in store and online.