Similar questions are being asked by supply chain leaders around the world: Do we continue to
invest in conventional processes that have proven effective but offer capped upside, or do we
implement disruptive approaches that offer potential step-function advancement but also carry
risks attendant with the unknown? In this article, we'll explore bimodal supply chain strategies
that integrate elements of each approach.
What does "bimodal" mean?
Bimodal, as the name suggests, is the ability to flexibly operate in two supply chain "modes."
Mode 1 leverages established, empirically proven, but often incremental approaches. Mode 2
explores new, potentially transformative, but often unproven approaches. Think of mode 1 as
progression along a known, mostly linear function. Think of mode 2 as bending the improvement
curve to access exponential gains.
The first mode centers on the delivery of well-established supply chain priorities like
productivity, security, and reliability. Success in this mode requires a strong "microscope"
perspective—near-term, detailed, precise. In mode 1, companies leverage core products, processes,
and systems. An example of mode 1 is statistical process control—analyzing historical data to
identify and reduce sources of variation. With more history comes larger sample sizes, which
result in smaller standard deviation and more certain outcomes. Statistical process control
is in the toolkit of any successful manufacturing organization. By systematically studying
the past, we can consistently improve the present.
In the second mode, companies strive for step-function improvement that is rarely
offered by the status quo, even if there is organized optimization of that status quo.
This approach requires more of a "telescope" perspective—forward-looking, longer-term,
less precise. Mode 2 involves exploring new markets, new processes, new technologies.
The Internet of Things and machine learning, which leverage multiple data streams to
self-adjust in real time, are examples of mode 2. As historical data is less available in
mode 2, we have to look forward. Mode 2 can yield transformative advancements, although
the statistical certainty of outcomes is lower.
Both is better than either
Rather than arguing for one mode over the other, bimodal recognizes the value of having
both modes in your supply chain. The benefits of continuous operational improvement (mode 1)
have been well documented over multiple decades. Likewise, the competitive advantages of
leap-frogging conventional tools (mode 2) are similarly exciting.
Bimodal, however, shouldn't be thought of as a toggle, switching from one mode to the other.
At the core of bimodal is the ability to integrate the two modes, complementing rather than
compartmentalizing. Although modes 1 and 2 do require different perspectives, the real value
of bimodal comes in connecting the modes. Data and digital technologies, further discussed
below, provide one such link. Innovative organization designs provide another. Expanding
one's view of "our" supply chain to include upstream partners and downstream customers is
a third.
A challenge facing organizations looking to go bimodal is how to integrate the two seemingly
disparate thought processes of modes 1 and 2. Many successful supply chains with whom we work—whether
suppliers, partners or customers—translate deep process understanding into predictable advancements
in important value-creation levers like cost reduction, capital efficiency, and service improvement. They
leverage strong "microscope thinking" to mitigate risk while continuously improving, both hallmarks of
mode 1. The "telescope thinking" needed in mode 2 often requires a different mindset—data automation
and self-tuning algorithms replacing manual systems, end-to-end value-stream thinking replacing localized
optimization, production-led team designs replacing traditional organizational structures. Mode 2's new
approaches bring unknowns, while mode 1 is all about reducing uncertainty. This creates an apparent disconnect.
One common denominator between the two modes is data. Data is the language of continuous-improvement processes
common to mode 1: Six Sigma, Lean and hoshin kanri (a method for ensuring that the strategic goals of a
company drive progress and action at every level). Data also fuels the predictive analytics common to mode 2:
cloud sharing, pattern recognition, and artificial intelligence. Digital technologies such as these that manage
data establish a powerful link between the two modes, bringing together the individual strengths of each into a
collectively more powerful union. Digitization, in both modes 1 and 2, builds trust and strengthens the links
between partners by providing end-to-end value chain visibility that in turn benefits customers, suppliers,
and communities.
Bimodal at 3M
3M is a $30 billion enterprise with more than 60,000 unique product offerings. We
manufacture roughly 85 percent of everything we sell. With such a diverse supply chain, we
rely on proven processes that have enabled us to continuously improve for more than a century.
Like many reading this article, 3M is comfortable in mode 1. At the same time, 3M is also a
science-based company. We see technology as a stabilizer rather than a risk. We're routinely
ranked amongst the world's most innovative companies. So, while mode 2 is also new to us, it
is generally familiar territory.
At 3M, we call our approach to bimodal "efficient growth." Mode 1 delivers the productivity
gains that our customers and shareholders demand. Mode 2 builds on this foundation, fueling
the growth necessary to ensure that our next century will be as successful as the last.
There are three key elements to our efficient growth strategy:
Harmonizing global processes to deliver world-class productivity, working-capital management,
and asset utilization (like mode 1).
Accelerating disruptive technologies to deliver even higher quality, lower cost, and
more innovative products to customers (like mode 2).
Regionalizing our supply chains to reduce complexity, amplify operational impact, enhance
customer service, and build high-performing talent (a blend of modes 1 & 2).
Together, these three strategies enable 3M to achieve higher levels of efficiency while
also integrating new technologies necessary for innovation, growth, and customer loyalty.
This helps us contend with the ever-increasing volatility of today while simultaneously
exploring and innovating for tomorrow.
Follow this link for additional work from the analyst group Gartner on the bimodal model.
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."