International site selection for manufacturing plants is a complex proposition. Companies that are seeking to build or lease manufacturing facilities across borders need to investigate many factors to ensure that they make location decisions with the appropriate level of rigor, accuracy, and, ultimately, confidence.
One of the most important site-selection factors—one that sometimes is not fully considered—is the foreign enterprise income tax. This is a corporate income tax that a company is required to pay to federal, state/provincial, and local governments based on the level of taxable income it has generated in a country.
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[Figure 1] Before- and after-tax financial model outputEnlarge this image
In our experience, it is important for international manufacturers to take a holistic approach that considers both before- and after-tax profit when assessing the merits of potential plant locations. There are good reasons to do so. For one thing, direct-investment projects by manufacturing companies, especially those that produce high-margin products, commonly result in a large amount of taxable income and a potentially significant tax liability in the country in which they establish operations. For another, the answer to the question of which is the best location for an investment can differ depending on tax factors.
Framing location trade-offs
Any company that is seeking to establish international manufacturing operations must carefully weigh the impact of operating-cost inputs that will affect the project's financial performance. Examples include labor, transportation, logistics, utility costs, land costs, taxes, and so forth. Performance measures vary depending on the organization, but they often include return on invested capital (ROIC), the project's impact on earnings per share, and pre- and post-tax cost per unit of production.
For many manufacturing companies, labor as well as transportation and logistics are the geographically variable considerations that exert the greatest influence on a project's financials. In high-margin industries that produce large amounts of taxable income, however, foreign enterprise income tax can have an even greater impact on project financials than either of those factors. In those types of industries, therefore, a location decision can be heavily influenced by in-country tax rates and the country's permitted investment structures. Examples of permitted investment structures, which vary from country to country, include wholly owned foreign enterprises and "toll manufacturing." The latter, in which a firm processes raw materials or semi-finished goods for another firm, is an arrangement that can reduce taxable income.
Although manufacturing companies must consider many factors when making site-selection decisions, they often find that a single geographically variable cost input most heavily influences the location decision. For the purposes of this discussion, we refer to this type of critical cost driver as an "investment optimization model." Three common examples include:
Labor optimization model—This model is common to industries or products in which the most significant geographically variable cost input is labor. Such an operation is likely to be labor-intensive, with low levels of automation, low margins, and a shipping profile that typically is characterized by high-volume, low-weight products. Countries that might be favorable locations for a company with this type of profile include India, China, Thailand, and Vietnam, among others.
Logistics optimization model—This type of model is common to industries or products in which the most significant geographically variable cost input is transportation. Such an operation is often characterized by heavy or bulky goods that are costly to transport, a more automated production process (which reduces labor content), low- to moderatemargin products, and a need for production to be proximate to the destination—that is, the revenueproducing—markets. Some examples of countries that currently align with this profile include Mexico (in support of the United States) and Central and Eastern Europe (in support of Western Europe).
Tax optimization model—This model is common to industries in which the most significant geographically variable cost input is direct tax. Such an operation is likely to be a manufacturer with a highly automated manufacturing process producing high-margin products that are regulated in some form. Just two examples of products that fit this profile include onpatent drugs and medical devices. Countries that would be appropriate locations for companies with this operating profile are those that apply a low tax on foreign investors' net income. They include Ireland, with a tax rate of 12 percent, and Singapore, Switzerland, and Puerto Rico, all of which assess tax rates as low as 0 percent.
These are not the only models for manufacturing companies to consider when they are making international site-selection decisions. As noted earlier, in practice, optimizing a directinvestment decision requires companies to consider a complex set of factors. Moreover, some optimization considerations are specific to certain industries; one example is the cost of electricity for the solar manufacturing industry.
Taxes change the cost picture
The only way to capture the true impact of corporate income tax on a location decision is to develop a financial model that shows both the before- and aftertax implications of the proposed investment. One company's experience, outlined below, illustrates how the tax-cost factor can affect the overall cost of a high-margin, direct-investment manufacturing project. (The company cannot be identified, but the siteselection project and the results discussed here reflect its actual experience.)
The company, a manufacturer of medical devices, needed a new location for a manufacturing plant. The project's objective was to establish the operation in a location that would be globally cost-competitive over 10- and 15-year analysis periods. The project's leadership was charged with determining whether a taxadvantaged, low-operating-cost, or customer-proximate location represented the best option.
Figure 1 illustrates the influence of income tax on project financials and the extent to which it affected the relative attractiveness of the locations under consideration. This graphic clearly illustrates the potential risk in developing a location strategy without considering income tax.
The following key observations emerge from the before- and after-tax assessment:
Location A (tax-advantaged location): This was the highest-cost option before tax. But when tax was incorporated into the financial analysis, Location A's low corporate income tax made it competitive.
Location B (tax-incentivized location): Despite having higher before-tax costs, significant tax incentives— zero income tax for a period of 10 years, in this case—made Location B the most cost-competitive of the four locations.
Location C (low-operating-cost location): The lowest-cost location before tax, Location C became less competitive due to its burdensome corporate income-tax structure.
The United States: The United States was the second-lowest-cost location before tax, but it became the most expensive site candidate after taxes were factored in.
As you might imagine, modeling the tax impact of these sorts of international site-selection projects is not easy. Complicating matters is the fact that the modeling tools that many companies use to help them select facility locations commonly focus on pre-tax operating costs and do not consider the impact of direct taxes. To compensate for this shortcoming, companies can (and should) assemble an internal team of professionals from their supply chain, procurement, tax, finance, sales and marketing, engineering, real estate, and human resources organizations to provide the subject-matter expertise that will be needed to develop a complete view of an investment's financials.
Consider the cost consequences
For industries producing high-margin products, it is critical to incorporate corporate income tax into any financial assessment of potential manufacturing locations. Failure to do so can result in the selection of a financially disadvantaged location. But this advice is not limited to manufacturers of high-margin products. It is also prudent for industries producing lower-margin products to include tax analysis in their location strategies. This is because many countries offer incentives that have the potential to reduce investors' tax liability for an extended period and, as a result, could change the desirability of a candidate location for manufacturing.
The launch is based on “Amazon Nova,” the company’s new generation of foundation models, the company said in a blog post. Data scientists use foundation models (FMs) to develop machine learning (ML) platforms more quickly than starting from scratch, allowing them to create artificial intelligence applications capable of performing a wide variety of general tasks, since they were trained on a broad spectrum of generalized data, Amazon says.
The new models are integrated with Amazon Bedrock, a managed service that makes FMs from AI companies and Amazon available for use through a single API. Using Amazon Bedrock, customers can experiment with and evaluate Amazon Nova models, as well as other FMs, to determine the best model for an application.
Calling the launch “the next step in our AI journey,” the company says Amazon Nova has the ability to process text, image, and video as prompts, so customers can use Amazon Nova-powered generative AI applications to understand videos, charts, and documents, or to generate videos and other multimedia content.
“Inside Amazon, we have about 1,000 Gen AI applications in motion, and we’ve had a bird’s-eye view of what application builders are still grappling with,” Rohit Prasad, SVP of Amazon Artificial General Intelligence, said in a release. “Our new Amazon Nova models are intended to help with these challenges for internal and external builders, and provide compelling intelligence and content generation while also delivering meaningful progress on latency, cost-effectiveness, customization, information grounding, and agentic capabilities.”
The new Amazon Nova models available in Amazon Bedrock include:
Amazon Nova Micro, a text-only model that delivers the lowest latency responses at very low cost.
Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs.
Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
Amazon Nova Premier, the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models
Amazon Nova Canvas, a state-of-the-art image generation model.
Amazon Nova Reel, a state-of-the-art video generation model that can transform a single image input into a brief video with the prompt: dolly forward.
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).
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.”
Grocers and retailers are struggling to get their systems back online just before the winter holiday peak, following a software hack that hit the supply chain software provider Blue Yonder this week.
The ransomware attack is snarling inventory distribution patterns because of its impact on systems such as the employee scheduling system for coffee stalwart Starbucks, according to a published report. Scottsdale, Arizona-based Blue Yonder provides a wide range of supply chain software, including warehouse management system (WMS), transportation management system (TMS), order management and commerce, network and control tower, returns management, and others.
Blue Yonder today acknowledged the disruptions, saying they were the result of a ransomware incident affecting its managed services hosted environment. The company has established a dedicated cybersecurity incident update webpage to communicate its recovery progress, but it had not been updated for nearly two days as of Tuesday afternoon. “Since learning of the incident, the Blue Yonder team has been working diligently together with external cybersecurity firms to make progress in their recovery process. We have implemented several defensive and forensic protocols,” a Blue Yonder spokesperson said in an email.
The timing of the attack suggests that hackers may have targeted Blue Yonder in a calculated attack based on the upcoming Thanksgiving break, since many U.S. organizations downsize their security staffing on holidays and weekends, according to a statement from Dan Lattimer, VP of Semperis, a New Jersey-based computer and network security firm.
“While details on the specifics of the Blue Yonder attack are scant, it is yet another reminder how damaging supply chain disruptions become when suppliers are taken offline. Kudos to Blue Yonder for dealing with this cyberattack head on but we still don’t know how far reaching the business disruptions will be in the UK, U.S. and other countries,” Lattimer said. “Now is time for organizations to fight back against threat actors. Deciding whether or not to pay a ransom is a personal decision that each company has to make, but paying emboldens threat actors and throws more fuel onto an already burning inferno. Simply, it doesn’t pay-to-pay,” he said.
The incident closely followed an unrelated cybersecurity issue at the grocery giant Ahold Delhaize, which has been recovering from impacts to the Stop & Shop chain that it across the U.S. Northeast region. In a statement apologizing to customers for the inconvenience of the cybersecurity issue, Netherlands-based Ahold Delhaize said its top priority is the security of its customers, associates and partners, and that the company’s internal IT security staff was working with external cybersecurity experts and law enforcement to speed recovery. “Our teams are taking steps to assess and mitigate the issue. This includes taking some systems offline to help protect them. This issue and subsequent mitigating actions have affected certain Ahold Delhaize USA brands and services including a number of pharmacies and certain e-commerce operations,” the company said.
Editor's note:This article was revised on November 27 to indicate that the cybersecurity issue at Ahold Delhaize was unrelated to the Blue Yonder hack.
The new funding brings Amazon's total investment in Anthropic to $8 billion, while maintaining the e-commerce giant’s position as a minority investor, according to Anthropic. The partnership was launched in 2023, when Amazon invested its first $4 billion round in the firm.
Anthropic’s “Claude” family of AI assistant models is available on AWS’s Amazon Bedrock, which is a cloud-based managed service that lets companies build specialized generative AI applications by choosing from an array of foundation models (FMs) developed by AI providers like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself.
According to Amazon, tens of thousands of customers, from startups to enterprises and government institutions, are currently running their generative AI workloads using Anthropic’s models in the AWS cloud. Those GenAI tools are powering tasks such as customer service chatbots, coding assistants, translation applications, drug discovery, engineering design, and complex business processes.
"The response from AWS customers who are developing generative AI applications powered by Anthropic in Amazon Bedrock has been remarkable," Matt Garman, AWS CEO, said in a release. "By continuing to deploy Anthropic models in Amazon Bedrock and collaborating with Anthropic on the development of our custom Trainium chips, we’ll keep pushing the boundaries of what customers can achieve with generative AI technologies. We’ve been impressed by Anthropic’s pace of innovation and commitment to responsible development of generative AI, and look forward to deepening our collaboration."