How SKF uses a supply chain twin to enable integrated planning
The threat of disruption and a range of market forces drove global bearings manufacturer SKF to think differently about managing its global supply chain. Building a "digital supply chain twin" allowed the company to globalize and automate planning and "futureproof" the business.
At first glance, it might seem like SKF Group has nothing to worry about. With revenues of US$9 billion, we are currently the largest player in the bearings and rotating equipment market. We also have more than 100 years of history in the industry, which would make it hard, you would think, to displace us from the top of the heap. (For more information about the company, see the related sidebar, "Five quick facts about SKF.")
We knew better, however. In 2015, we realized that in spite of all our past success, our supply chain was in need of a transformation. We believed that one of the first steps that we needed to take in order to modernize our supply chain was to move from having a local planning structure that occurred at the manufacturing plant to having a global, integrated planning structure. To accomplish this, we found that we needed to create a "digital twin" of our supply chain that could enable more automation of our planning process. This is the story of our journey.
The threat
Bearings may seem like a relatively simple machine element with a very specific job: reducing friction between moving parts while also constraining relative motion to only a desired amount. But in spite of that perceived simplicity, the bearing market is set for a high growth rate. According to the market research group Stratistics MRC, the global bearings market is expected to grow at a compound annual growth rate (CAGR) of 7.2 percent from 2017 to 2023. The research firm cites multiple factors behind this growth including a rising demand for ceramic ball bearings in electric vehicles and large-scale railway, aerospace, and wind power projects. The top threats cited are cheap and fake products entering the market, along with the growing utilization of used bearings.
With this level of growth potential, the bearings market is set to attract increased competition. In fact, it's not only our traditional competitors that keep me up at night these days; it's the threat of a completely new business model disrupting our market. I look at what happened over the last years in different markets: Airbnb is the world's largest housing provider, yet owns no hotels. Uber is the world's largest taxi company, yet owns no vehicles. And while these companies and many other recent disrupters are now acquiring assets, who's to say such a competitor with new ideas and a different business model won't emerge in our space, significantly disturbing our growth or even threatening our existence?
It can be tough to convince people of the need to change before a crisis actually hits. But the larger and more set in its ways a company becomes, the harder it is and the longer it takes to change. The sudden demise of once great companies, like the photography company Kodak, provides a valuable lesson: Large-scale transformation must begin well before a major threat appears on the horizon.
Fortunately for us, a new CEO, Alrik Danielson, joined SKF in late 2015. He came in to shake up the status quo and asked: "What is our demand chain vision? What is our guiding star for the next 10 to 15 years?" He encouraged us to take inspiration from any source and not be constrained in our thinking by any technical, organizational, or other limitation. "Start with a blank sheet of paper!" So we did.
The vision
In setting our vision, we not only tapped into some of the most experienced brains in our company but also reached out to leaders in manufacturing and other industries like fast-moving consumer goods. In addition, we contracted with the supply chain consulting and applications company Optilon to support us in this journey. Early in 2015, a diverse team of supply chain experts, visionaries, planners, and information technology and data experts worked up a rough sketch of our vision, and it literally fit on a single sheet of paper. That vision was to consolidate and integrate planning on a global basis.
We felt that moving to a global planning structure was our highest priority because we had observed that doing planning at the local level led to operational inefficiencies and friction between internal organizations. An integrated planning process would optimize how we made daily, operational decisionson such matters as production, stocking, and distribution. For example, it could help us better decide how many of a lot of 1,000 items we should ship to Shanghai, and how many we should ship to Mexico. Or, we could use it to decide what product should be produced next week based on the demand we are seeing now from customers around the world and the inventory we currently have in different warehouses. These decisions would no longer be made based on what would be best for a particular location or function but rather based on what would be optimal for the supply chainas a whole.Â
It was just as well that our CEO had asked us to brainstorm without limitations, because we knew this would be a mammoth transition. First off, there was a lot of complexity involved. SKF has grown both organically and through acquisition. Consequently, over the years, we have accumulated many manufacturing sites, distribution centers, and warehouses. These new sites introduced more complexity into the planning process, especially as we were running different enterprise resource planning (ERP) systems in different regions. The transition would also affect many people and their incentives, and it would require many of our employees to develop new skills.
For example, we decided that to do integrated planning effectively we needed to create a new role, the global planner, who would see, manage, and supervise all of the material and information flows associated with our products, including finished goods, components, and raw material. The global planner would have end-to-end responsibility for these flows from analyzing customer demand up to submitting purchase orders to suppliers. Orchestrating all the associated activities, however, is a lot of work. The only way the global planner could do it efficiently was if we achieved a much higher degree of automation throughout our supply chain, which would be enabled by what's known as a "digital twin" of the supply chain.
The digital twin
Our supply chain's large size and complexity means that it's difficult to consistently make optimal trade-offs at the planning level between "cash" (such as safety-stock levels and goods in transit), "cost" (such as staffing and output of production and transportation methods) and "customer" (service levels). To gain the needed visibility to make these trade-offs and feed accurate, timely data into our new planning system, we immediately realized that we needed a "digital twin" of our supply chain, or a digital model that's an exact copy of the entire supply chain. Essentially, it is a cluster of structured tables of master data and operational data.
We needed to have this structured data so that we could feed it into the automation and visibility tools that would improve our operational planning processes. The digital twin would enable us to create visibility for truly fact-based decision making; to link low-level, operational decisions to high-level strategic goals; and perform simulation and "what-if" analysis.
Fortunately, we already had an "embryo" for this new approach because we had previously worked with our partner Optilon to establish a tactical multi-echelon inventory optimization (MEIO) process. MEIO optimizes complex distribution networks like ours by using simulation and what-if scenarios to determine the right level of inventory to keep in each location, by time period, in order to best serve the customer. Having a simulation-based process in place meant our extended team was already in the right mindset for the cross-functional groundwork needed to build and maintain the digital twin. For example, team members already understood how powerful it was to have structured data and how important it was to optimize end-to-end.
Here are the steps we took to build the digital twin and then apply it to our planning process:
Step 1: Create a supply chain network map. The first step to representing our global supply chain in the digital twin was building what we call a supply chain network map. This is essentially a view of how our items are manufactured, stored, moved, and sold around the world. A key part of this step is preparing data (including cost, service-level targets, lead times, and bill-of-material information) for our roughly 500,000 stock-keeping units (SKUs). This involves pulling in data from about 40 instances of five different ERP systems, then "normalizing" the data (cleaning and making it more consistent), before moving it into a central repository. Taking the time to normalize the data is important. An item might be produced in one facility, stocked in 20 warehouses, and sold through 40 sales operations. Normalizing SKU data ensures that it will be interpreted in a consistent way across the entire network.
This map is not a fancy graphical representation of the supply chain but simply a relationship database model. However, it really made our hearts sing to now have supply chain information at the lowest level of detail readily available at our fingertips.
Step 2: Bring the digital twin to life. Once the map was designed and the SKU data normalized, the next step was bringing the supply chain network map to life by adding operational data: things like open customer orders, goods-in-transit, and inventory levels. This alive, digital twin provides visibility to answer questions like: What's the total number of external customer orders for one item across all our global sales operations right now? How much inventory do we hold of that item in all our locations? How many of those items are currently in transit in all our transportation lanes? These are just a few examples of simple questions that the digital twin can answer about the present; its value really increases when we apply it to plan for the future.
Step 3: Use the digital twin in planning. The next step was feeding the data from our central relational database repository into our supply chain planning software, ToolsGroup's SO99+. We had already been using part of this software for the aforementioned MEIO process to set safety-stock levels. Now we started using it for daily operational planning. SO99+ enables us to optimize demand, inventory, and planning for all the SKUs in all locations in our supply chain. We can now more easily sense demand deviations and address them more proactively. Crucially the software lets us optimize inventory levels against desired service levels so that we are able to compete effectively in our industry without incurring excessive storage, transportation costs, and obsolescence. For example, when we face supply shortages, we are now able to make informed decisions about which warehouses to prioritize sending inventory to, minimizing the impact on customer service levels. The planning tool was in place, but the journey to transform the whole of SKF had just begun.
One production line at a time
To ease SKF through this ambitious transformation, we are introducing our new planning process—and all the associated changes for adjacent departments like customer service, manufacturing, controlling, and human resources—one production line at a time. Today we have two factories—one in Steyr, Austria, and one in St. Cyr, France—where global planners now supervise the worldwide distribution of their product's assortment and balance the supply and demand for it. These planners use SO99+ to focus on handling exceptions rather than planning manually. They are globally responsible for all inventories and customer service levels for their share of SKF's products, which are often stocked in more than 20 warehouses stretched around the world from Latin America and the United States to Europe; Dubai, United Arab Emirates; Russia; Shanghai, China; and Singapore as well as smaller warehouses in places like Thailand and New Zealand.
Using the data from the digital twin, SO99+ automatically calculates each item's external demand forecast, factors in lead times and actual stock levels throughout the supply network, calculates safety-stock levels and net forecasts for every warehouse, and creates replenishment plans to satisfy future customer demand to set service levels.
SO99+ also alerts our planners before any potential problems reach a critical point. For example, if we are in danger of not being able to supply an item to one of our warehouses, the planner is alerted and can then act on this exception before it becomes a crisis. Or, if we observe unusual customer demand, the planner is alerted and able to proactively investigate and manage the suspected deviation.
SCOR-ing well
Soon each product will have one global forecast, one planning method, one person responsible, and true end-to-end accountability. This means we will align much better to the Supply Chain Council's Supply Chain Operations Reference (SCOR) model, a diagnostic tool for benchmarking performance. Before we started, our organizations and people were taking on multiple roles and struggling to perform them all effectively. Now our planners in factories have the global planning mandate. In essence, we have consolidated the Source, Plan, and Make functions of the SCOR model into one organization. This makes end-to-end optimization possible, empowers individuals, and reduces internal competition.
With integrated planning in place, we are looking forward to the next stage in our transformation. It is a big change we are driving, but in the grand scheme of things, we have just laid the foundation. We are already exploring multiple options to extend the horizon of planning upstream and downstream. This might include doing planning for and on the behalf of customers. This planning would be based not only on customer orders but also on signals from SKF sensors in customers' machineries and other demand signals. All of these signals would be connected to our core planning procedures. We expect these changes will allow us to reach greater service levels, operational efficiency, and waste reduction.
After devoting my entire career to SKF, I will probably always find some reason to stay up at night, thinking about how to improve our supply chain. At least now that we have embarked on a journey to transform ourselves, the threat of a major industry disruption taking us by surprise is considerably less likely.
Five quick facts about SKF
SKF is a global supplier of bearings, seals, mechatronics, and lubrications systems.
The company also provides services such as technical support, maintenance, engineering consulting, and training.Â
SKF is present in more than 130 countries and has around 17,000 distributor locations worldwide.
Annual sales in 2017 were 77.9 million Swedish krona (or approximately US$8.7 million).
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."