Machine Learning Archives

“Flesh and Breath” – The Appeal of Delegating to AI and its Limits

Guest Commentary | By Polly Mitchell-Guthrie • 11/19/2024

At the airport for an early flight, returning home after a speaking engagement, I was having a moment, feeling “tweepy” (my new word for tiredness that makes me weepy) as I stood in line to order breakfast. The trip had been a busy several days on top of a difficult year. The woman taking my order looked directly at me, smiled, and wished me a nice day. Her words were not remarkable but delivered with such utter sincerity, with light in her eyes, that they touched me just enough to lift my spirits. After eating I returned to the counter to thank her for making a difference. This may seem like an insignificant moment, but most of life is a series of small moments punctuated by a few big ones, some stupendous, others shattering. In a year heavy with crushing moments I’ve gained appreciation for the gifts of kindness, and this small exchange shone a light for me on why they matter, what I want to delegate to AI, and what remains uniquely human.

In my last commentary I argued that AI lacks what I call the 3C’s: context, collaboration and conscience. The challenges I’ve faced in the last year have reinforced the importance of two additional fundamentally human capabilities AI lacks: connection and compassion. As appetite for AI continues to grow at an astonishing pace, navigating the boundaries of what to delegate to AI and what to preserve for humans is essential.

The Appeal of Delegating to AI

AI is a tool designed by humans to do things it can do better or we cannot or do not want to do. Automation powered by AI takes over tedious grunt work involved in areas like supply chain procurement, with chatbots negotiating routine contracts with suppliers, saving money and freeing up time for professionals to handle more complex deals.

AI thrives on large volumes of data, so it can scale far beyond our cognitive capacity to crunch through reams of information quickly and synthesize the results. Googling now often places a generative AI result at the top of the page, a single answer from searching and summarizing the most relevant information. It can find and learn from patterns in big data sets to make predictions, such as when a machine is likely to fail in a factory, which external signals will most impact a demand forecast, or what actual lead times for parts will be.

AI has potential to reduce bias, since people can make inconsistent and subjective decisions based on personal opinions, so there is promise for its application in areas like hiring, lending, and medical diagnosis. But these same areas of AI promise also show risk. Since bias is primarily a problem of the underlying data reflecting existing human bias, efforts must be made to leverage AI’s strengths and mitigate its pitfalls. AI fairness is thorny, but I am hopeful that combining human and AI strengths for some of these decisions can make them more consistent and objective.

In addition to these applications of AI’s strengths, it doesn’t get tired (or weepy), irritable, bored or overwhelmed. On a different trip during another tweepy moment, exacerbated by a cascading series of flight cancellations and reroutings preventing me from getting home that night, I had to make a decision between a variety of unattractive flight and hotel options. I would have loved to simply delegate to AI finding me a room and getting me home.

The appeal of delegating to AI is borne out by data. The US National Bureau of Economic Research reported last month that Generative AI is already on track to outpace the speed of adoption of the internet and PCs. ARK Investment Management found that every four months the cost of operating AI models drops by half, beating the famous Moore’s Law on chip costs by a factor of 4-6 times. New research by Morgan Stanley finds that 50% of AI projects are delivering and 40% exceeding expected ROI. In short, delegating to AI is moving fast and making an impact.

Never Delegate Understanding – the Limits of AI

Charles and Ray Eames designed some of the most iconic furniture of the 20th century through a deep study of an object’s purpose, a process that led to their famous adage, “Never delegate understanding.” Their philosophy was grounded in foregoing assumptions about how things worked in favor of learning for themselves. We delegate understanding when we expect tools and technology to solve all our problems and surrender our own expertise. As researchers have found, we still don’t fully understand the boundaries of AI’s capabilities, a phenomenon they call the jagged technological frontier. Their experiments showed how blind trust in AI’s results was a delegation of understanding that actually led to a 19% dip in performance.

The problem is that as dazzling as generative AI can be, it doesn’t “understand,” it is a probabalistic sentence completion machine. It responds to queries based on AI models, not comprehension. Language has structure and rules, but human emotion is far less predictable. AI techniques like sentiment analysis can identify the emotions in language to provide insights for certain purposes like customer service or targeted marketing, but these methods don’t achieve true emotional intelligence, they only barely scratch the realm of human feelings, which defy rationality.

The Importance of “Flesh and Breath”

My father spent the last year and a half of his life in a skilled nursing facility, and while visiting him I was saddened to observe so many elder adults languishing alone, because research is clear that we are wired for connection. This exposure piqued my interest in so-called social or care robots that can mimic pet therapy, visit residents, facilitate social interactions, offer tailored suggestions for healthy behaviors like exercise, and more. While I in no way see these devices as substitutes for humans, I’m open to anything that might plug the dike of what former US Surgeon General has called “an epidemic of loneliness.”

And yet I understand the response of a friend who spent many years working with the elderly – she is adamant that stemming loneliness requires “flesh and breath,” not electronic devices. Her reaction points to the limits of AI – given enough data, it can analyze facial expressions and voices to detect emotions and even respond, but it doesn’t understand, because it doesn’t feel. And the ability to feel, in spite of the inevitable heartache, is what fuels connection and compassion.

The Power of Compassion and Connection

My circumstances over the last year forced me to discuss very personal details with strangers as well as colleagues. The kindness I’ve received in response has been astounding. People I hardly knew checked in to ask how I was doing. A colleague on maternity leave sent regular doses of baby photos. One man I know professionally but have never even met in person offered to host me at his house in New Hampshire so I could hike, knowing it is both a hobby and solace. My compassion cup has been overflowing.

When I opened to the door to my own experience, people walked in to share their own stories of heartbreaking challenges, some past, some present. I heard tales of fire, death of a parent, loss of work, sexual assault, mental illness, addiction, degenerative disease. People shared understanding of my pain and a common message that I will make it, no matter how hard it may seem now. They didn’t delegate understanding but created it by listening, greeting me with compassion, and walking alongside me in sharing their own stories, creating connection we can’t delegate to AI.

Call centers are heavily studied in part because of the abundance of data, and one area of research is how to improve customer service through analyzing emotions callers express in order to offer employees guidance in responding more effectively. I’m all for anything that can make these calls better, but the reason my small moment in the airport warmed my heart is because this woman’s customer service was exemplary, not based on a script but borne of authenticity and compassion, brimming over and creating connection. AI is impressive in its abilities to help manage crew schedules, design optimal flight paths, detect plane safety issues, predict parts needed for maintenance and reduce emissions from fuel, but in my tweepy airport moment I was grateful for compassion and connection delivered via flesh and breath.

Polly Mitchell-Guthrie is the VP of Industry Outreach and Thought Leadership at Kinaxis, the leader in empowering people to make confident supply chain decisions. Previously she served in roles as director of Analytical Consulting Services at the University of North Carolina Health Care System, senior manager of the Advanced Analytics Customer Liaison Group in SAS’ Research and Development Division, and Director of the SAS Global Academic Program.

Mitchell-Guthrie has an MBA from the Kenan-Flagler Business School of the University of North Carolina at Chapel Hill, where she also received her BA in political science as a Morehead Scholar. She has been active in many roles within INFORMS (the Institute for Operations Research and Management Sciences), including serving as the chair and vice chair of the Analytics Certification Board and secretary of the Analytics Society.

5 Principles for Navigating the Digital Fog Around AI in Supply Chain

Artificial Intelligence | By Polly Mitchell-Guthrie • 08/17/2023

The scariest drive of my life took place on a summer’s night in the North Carolina mountains returning from a longer-than-expected hike. The Blue Ridge Parkway is usually a magical drive in the clouds, but it turned frightening as dusk and fog descended, reducing visibility to ten feet. I was torn between twin impulses – speeding up so we could descend out of the fog and slowing down to keep from careening off the mountain to the left or into a ditch on the right. It took intense concentration and a white-knuckle grip on the wheel to emerge safely. CSCOs feel similarly overwhelmed navigating the intense digital fog surrounding AI in supply chain, so I offer five principles for navigation.

The arrival of ChatGPT at the end of November 2022 similarly turned a drive in the clouds into thick fog. Demands to speed up abound. An IBM Institute for Business Value study found that 66% of CEOs feel pressured by their boards to accelerate AI adoption. A Workday study of decision-makers found that 80% feel AI is essential to remaining competitive, so 94% are investing in it.

The pressure is propelled by opportunity – firms that embrace technology experience productivity growth as much as 40% higher than those who don’t. But this opportunity comes with risks; as BCG found, up to 70% of digital transformations fail, so no wonder some are scared at the undertaking. Compounding pressure and risk is low visibility in the fog of hype surrounding complex, rapidly evolving technology. And as AI appears more human-like in its capabilities, emotions ratchet up. Privately leaders admit to feeling upset and overwhelmed, because they don’t understand what AI can do and how to steer through the fog. The impulse to slow down is understandable.

Defining AI in Supply Chain

A definition is a first step in clearing the digital fog. AI is the science of computers mimicking human intelligence to solve problems. This science encompasses many disciplines to improve speed, precision and elegance in decision-making by finding patterns in enormous volumes of data. Examples of the fields are machine learning (including deep learning), optimization, genetic algorithms, robotic process automation, generative AI, and decision management. What can this science do? AI can generate recommendations, predict and surface insights, provide speed and scale, automate processes, and enhance productivity, all capabilities we can apply across supply chains. A definition is a start, but we also need guardrails, so here are five principles for succeeding with AI in supply chain.

First principle: AI should augment humans, not replace them

The capability boundaries AI keeps crossing are nothing short of amazing, from producing creative marketing copy, to complex legal research, to songs, paintings and more. These wonders are possible due to the ability to process data and learn from patterns far beyond the cognitive capacity of humans. These achievements make it easy to forget what machines cannot provide, which I call the 3 C’s: context, collaboration and conscience. Models cannot derive meaning from context, critical in so many areas, like what Zero100 thought leader Kevin O’Marah has termed “machine whispering.” Nor can they work together to solve problems, including addressing issues like sustainability or human rights in supply chains.

This complementarity is why AI should augment humans, not replace them. The most powerful combination is for humans and AI to work together, a belief reflected in a Workday survey of decision-makers, 93% of whom believe in the importance of keeping the human in the loop when AI is making significant decisions.

Second principle: The expert fusion of AI, heuristics and optimization is key

AI can also model problems at scale to produce more precise recommendations, such as greater demand forecast accuracy or better predictions of on-time delivery. Precision is also a benefit of optimization, a field of AI familiar to many in supply chains for its ability to make the best use of resources within constraints to accomplish an optimal solution with a specific objective, such as minimizing costs. But here scale can be a challenge: optimizing a supply network can involve 200 million interdependent variables, slowing even the fastest optimization solver down by hours. Instead some turn to heuristics, a problem-solving model that utilizes a practical solution, or best practice, to produce a quick and feasible course of action good enough for the situation.

So these various mathematical models can offer speed, precision, and elegance, but with trade-offs. However, the newest, fanciest math isn’t necessarily better, no matter what you might hear in the fog. Generative AI is not the right approach to most of our classic supply chain problems, for which heuristics offer unparalleled agility. Deep learning has very specialized applications perfect for the “right” problem but not most, particularly given its greater complexity.

A fusion of methods, like machine learning and heuristics, can “warm start” an optimization model and speed up the ability to solve it, creatively combining the strengths of each approach to achieve an equilibrium of speed, precision and elegance. Keep your hands on the wheel in the fog and remember that the most elegant solution is one that uses the right model for the right problem at the right time, no more, no less.

Third principle: Concurrency amplified by AI is a breakthrough in supply chain management

Supply chains connect many functions across a company and beyond, which is why optimizing one link doesn’t optimize the entire chain. For example, AI can greatly increase the accuracy of forecasts, but we want more than highly-efficient silos. As a trio of Canadian economists argue, “AI can be used to resolve uncertainty, but unless that can translate into aligned decisions all the way down, the fundamental problem—that demand needs to be aligned with supply—hasn’t really been solved. Like the swing of a bullwhip, your own solution has reverberations down the line.” The power of AI on its own is not enough.

The real breakthrough is not from AI but with concurrency, which integrates AI in the workflow to align decision-making across the supply chain for faster response. We want AI for its ability to predict with greater precision, speed, and elegance, and we need concurrency to connect supply chains for better, faster response, no matter what the conditions are. Because AI embedded in concurrency leverages predictions while absorbing the volatility we cannot predict from the inevitable disruptions our supply chains will always face.

Fourth principle: The power of AI must be democratized

For AI to realize its potential it must be unleashed from the lab of data scientists. We will always need experts to explore new ways to apply AI, but empowering supply chain practitioners to adopt it themselves will extend its reach. For this reason, the best solutions require only an understanding of company data and business, not technical proficiency in AI or data science.

So while Workday’s survey found 72% of leaders feel their organizations lack the skills necessary to full implement AI, applying AI doesn’t have to involve diving into the deep end. If solutions are designed for someone with supply chain context and business knowledge, they can “consume” the results of a model without knowing how to build it. Democratizing AI in this way ensures its use, so choose to work with a provider who allows you to start from where you are and evolve.

Fifth principle: Explainability is essential for AI adoption

One additional trade-off of AI is when the speed, precision, and elegance are delivered in black boxes that even the data scientists who constructed them cannot unpack. Lack of explainability is a barrier to adoption, because if you are personally held responsible for a forecast, it can be hard to trust a “machine.” Researchers have found that humans are more forgiving of what they perceive to be error on the part of fellow humans than they are from machines, a trait that can lead to them to develop “algorithm aversion.”

One approach to overcoming this aversion is state-of-the-art techniques that make black box AI models more understandable. For example, tools like a SHAP diagram (SHapley Additive exPlanations) can be surfaced in demand sensing to help a planner see how adding a signal like weather affects predictions. To increase people’s willingness to adopt the value of AI-driven insights, we need to arm them with the information they need to explain their decisions embedded in solutions they can understand.

The opportunity for AI in supply chains is massive

Supply chains have never needed more help, and AI has never been more ready, so the time is now. But as we ramp up our us of AI we need a human-centered approach that amplifies the power of concurrency to drive the most intelligent supply chains on the planet. When AI is embedded across the end-to-end supply chain, expertly fusing the best techniques available, we can reimagine what is possible in our supply chains.

GXO Direct to Use AI To Drive Labor Efficiencies

Warehouse Management Systems | By Steve Banker • 05/15/2023

Steve Lewis, the president of GXO Direct, the shared services division of GXO Logistics, spoke at Blue Yonder’s ICON user conference. Blue Yonder is one of the world’s largest providers of supply chain software solutions. GXO is the world’s largest contract warehousing provider. Afterwards, Mr. Lewis spoke in more detail to industry analysts. Mr. Lewis announced that Blue Yonder’s warehouse management system (WMS) – with the attached modules for labor management, automation, and order management – would become the preferred solution offered to customers in the Direct division.

GXO Direct operates warehouses across major markets in the U.S. and Canada. The network of warehouses enables one day delivery for brands anywhere nationwide. Since its introduction in 2018, GXO Direct has grown exponentially, adding millions of square feet each year. They offer multiple customers across different verticals access to warehouse space, technology, and value-added services that includes e-commerce shipping and returns. The Direct division serves multiple customers out of the same warehouses. In Southern California, for example, they have a 670,000 square foot warehouse that serves eight different fast-growing ecommerce retailers. By operating hubs comprised of several warehouses located in a campus, GXO is able to increase operational agility, reduce reducing transportation costs, and speed up delivery times.

GXO (NYSE: GXO) is a publicly traded company headquartered in Greenwich, Connecticut with nearly 1,000 warehouses and more than 130,000 employees globally. The company generates $9 billion annually and serves customers across a breadth of industries. In GXO’s annual report it states that roughly 30 percent of its revenue is from automated sites. This is substantially higher than the industry average of 8 percent. This is because the company bet early and big on technology. Their selection of Blue Yonder is another example of this.

The Need for Speed

GXO chose Blue Yonder as one of their warehouse software partners for its Direct network because the rapid implementation of a warehouse management system (WMS) is critical in supporting Direct’s fast, flexible model. They recognized that with the right Cloud solution, they could make implementation two times faster than traditional on-premise solutions.

That is quite a feat since GXO’s approach to implementations was already being done in 16 weeks. 16 week implementations were possible because Direct looked at how the last 40 warehouse start-ups had been configured and used that data to create a new template that would allow for fast implementations. This allowed them to know before an implementation even began which configuration switches to turn on. Not so many years ago, it was not unusual to hear of WMS implementations taking up to 9 months. It is breathtaking to think that they plan to take the implementations down to 8 weeks.

Speedy implementations cut the costs of implementations and allow them to onboard new customers more quickly. Additionally, they were looking for labor savings of 5% based on the labor management solution. The savings from labor management will dwarf the savings that stem from speedy implementations.

Additionally, they were looking for a solution so simple that if a change to a process needed to be made, an operations manager, rather than an IT analyst, could make the change. Mr. Campbell visited the customer sites of many of the leading providers of WMS. At one Blue Yonder site he asked a manager, “what if I wanted to receive not by ASNs (advanced ship notices) but by PO (purchase order) with serial capture?” The manager said it was not a problem. In a few minutes, he located the right configuration switch, clicked it, and BOOM, the new process was enabled. This enables changes to be made as needed in real-time.

GXO is known for being customer-centric, providing customized solutions based on the unique needs of its blue-chip customer base. If a potential customer has a preference for a different WMS, and it is one of the 10 WMS solutions that GXO supports, they can implement the preferred solution. However, they plan to lead with Blue Yonder as the solution for GXO Direct. Furthermore, even if another vendor’s WMS is used, GXO Direct believes they can seamlessly layer the Blue Yonder labor management solution over the other vendor’s WMS solution.

A Different Approach to Labor Management

In order to layer in workforce management capabilities, GXO will start by documenting standard operating procedures to optimize operations. But then, instead of using industrial engineers with stop watches or other work measurement techniques to determine what the labor standards should be, machine learning will be used.

Historically, when a labor management system (LMS) was implemented, engineered work-measurement techniques were used to determine how long it took to complete a task to determine where to position employees within the warehouse at a given time and help them operate more efficiently. A traditional LMS considers the distance traveled, whether a pick is in the golden zone or requires stooping or stretching, the equipment used, and many other variables to create labor standards. Because processes change, industrial engineers needed to continue to update the standards.

The trouble is that industrial engineers have become a scarce commodity. This is making it difficult for companies to establish labor standards or even to keep existing standards up to date. Fortunately, machine learning can be used to solve this problem.

With the new Blue Yonder solution, GXO will be able to apply machine learning to the data generated across their network of warehouses to establish standards, have the proper number of workers in the warehouse, and properly position and reposition those workers. With a machine learning approach to driving labor standards, data is acquired over time. This data shows that when Steve moves from location X to location Y it takes on average 29 seconds. When Dave moves from the same origin to the same destination, it takes 36 seconds. On average, across the work force, the average time is 33 seconds. That 33 seconds can be made the standard. This is then done across all tasks in the warehouse. These standards can then be used to forecast how many workers are needed and where in the warehouse they will be needed.

The Blue Yonder WMS product manager I talked to says that the labor standards generated by machine learning will not be the same as those generated by engineered labor standards. They are likely to be lower. That is a good thing. You would not want the new standards to be higher than what would be produced by industrial engineers. If the standards turned out to be higher, you would be pushing your workers too hard. In time, as Blue Yonder acquires data from multiple machine learning LMS implementations and then compares the results to traditional industrial engineering standards, they may be able to shrink the divergence between the standards generated by machine learning and what would be created by industrial engineers.

Will Labor Management Increase Turnover?

Mr. Lewis does not believe that the new labor management solution will increase turnover. Investments in robotics mean that workers are not walking or lifting as much. The optimization associated with cobots shrinks the distances traveled. In goods-to-person (GTP) automation, GPT robots bring the goods to workers at their stations. These investments, Mr. Lewis believes, have increased employee satisfaction, safety and retention.

Further, having the right warehouse leadership in place is key to employee satisfaction and retention. GXO has comprehensive leadership training for shift managers all the way up to the site managers. They have meetings at the beginning of each shift where team members reinforce the importance of safety, give suggestions on how to improve processes and debrief on operations at the site. Employees get to participate in continuous improvement projects and team members’ suggestions and feedback are valued and encouraged.

The use of robotics reduces training times and helps workers become highly productive much faster. In some cases, new hires working with cobots can reach standard levels of productivity by the end of the first day. It is always the goal of GXO to develop temporary workers into full time employees and give them career paths that can lead to management.

Final Thoughts

Historically, contract logistics companies made minimal investments in technology. Those days, for GXO at least, are gone. In the strategy section of GXO’s annual report, the company claims, “We deliver value to customers in the form of technological innovations, process efficiencies, cost efficiencies and reliable outcomes.” After talking to Mr. Lewis about their investments in technology and training, this did not sound like empty rhetoric.

FedEx Works to Monetize their Big Data

Logistics Service Providers | By Steve Banker • 01/24/2022

FedEx Dataworks CEO — Sriram Krishnasamy, the CEO of FedEx Dataworks

A network effects business model allows a company to gain more value as more companies use its products or services. The value of the offering rapidly increases because each additional user increases the value of the network. Today, we mainly think of digital companies like Google and Twitter in this category. Google and Twitter mainly monetize the data through targeted advertising. The value of the data captured by Google and Twitter has made them the darlings of Wall Street.

But it turns out big logistics firms also generate Big Data, and they are also working to monetize this data. A case in point is FedEx. FedEx Dataworks is an organization within FedEx that uses data science and machine learning to help make shipping more efficient. The richness of FedEx’s data comes from the 17 million packages they ship per day around the globe. Each shipment is scanned many times as it moves from origin to destination.

Sriram Krishnasamy, the CEO of FedEx Dataworks, said that the data is much richer than just scan data. FedEx enriches this data with weather and traffic data. FedEx is doing the obvious thing with that data; they are using predictive analytics to improve the flow of goods through their network and make themselves into a more reliable carrier. “Information is just as important as the package,” Mr. Krishnasamy asserted. “We have always been good at capturing data. Now it is in a big data infrastructure” and they can find connections between data that may look unconnected.

This predictive intelligence is particularly important for high value packages that must be delivered quickly. A case in point is the FedEx Surround solution. FedEx Surround is based on proactive monitoring and intervention controls across a delivery network. FedEx Surround predicts shipment success by combining information about a package with external data such as weather to manage the risks surrounding the shipping process. Shipment data includes scans, shipment route, conveyance and SenseAware ID, a small Bluetooth tracking sensor that can be placed on a package. This allows a white glove service team to monitor the location of a package and take action if it looks like the package will not arrive on time. For example, an intervention might consist of a team scooping up the packages at risk at a sort center and putting them in a van that will leave immediately. But, from origin to destination, FedEx asserts, there are multiple intervention points.

FedEx Surround’s first use was in tracking critical, sensitive shipments of COVID-19 vaccines as they moved from manufacturers and distributors to medical centers across the U.S. In the first year of COVID-19 vaccine distribution, FedEx delivered approximately 300 million doses throughout the U.S. with the help of predictive analytic tools. The vaccine shipments move through the FedEx network with an average transit time of less than 20 hours. The tracking devices, combined with the white globe teams monitoring the shipments, allowed for proactive interventions that led to a 99.91 success rate on getting vaccines delivered on the day of commit.

But FedEx Dataworks is looking to do more than just improve their ability to be a reliable partner. And they are looking to leverage their customers’ data, not just their own. Currently, they have proof of concept project using the data of one of their largest customers. Customer data is being ingested through an API framework and then combined with the FedEx network data.

In one use case, customer data is being used to help predict demand and returns in a joint planning process. The better the forecast around demand and returns, the more the customer stands to save on shipping by injecting the package at the right time and place into the FedEx delivery network. For example, seven-day ground is cheaper than express deliveries. The right injection point is not necessarily the FedEx sort center closest to the customer. Traffic and congestion might suggest injecting packages into a more distant sort center will actually improve the odds of an on-time delivery.

Over time, machine learning will be leveraged so that forecasts will get better. FedEx will also learn from its failures. To the extent that the joint forecast helps inventory to be positioned in the right location and shipped using lower cost services, both FedEx and their customers can benefit. The customer benefits by paying for the service that best meets their needs. FedEx benefits from stickier customer relationships.

There are other opportunities to monetize the data. FedEx ships to 220 nations. Trade documentation is needed for international shipments. Properly classifying goods for duties and insuring goods are not shipped to bad actors (denied party screening) may sound mundane. It is devilishly tricky. Machine learning based on Big Data can help FedEx serve their customers by getting better and better at this.

Another opportunity is fraud prevention. FedEx has validated addresses in their databases. This might create an opportunity to expose fraudulent transactions.

In conclusion, network effect business advantages are not something that accrue only to large digital companies. Big carriers have opportunities not just to improve their service, but to monetize this data in a variety of ways.

Editor’s Choice: Machine Learning Will Make Warehouse Managers More Productive

Warehouse Automation | By Chris Cunnane • 06/15/2021

Note: Today’s post is part of our “Editor’s Choice” series where we highlight recent posts published by our sponsors that provide supply chain insights and advice. Today’s article is from Lucas Systems and highlights the benefits of machine learning in the warehouse.

Real-world uses of AI in business have exploded in the past decade, but few of those applications are focused on warehousing and distribution. That is changing as companies like Lucas introduce machine learning tools to improve planning and decision-making in the DC. These new tools will free time for managers and engineers, making them more productive and their DCs more efficient and effective. This article provides an introduction to machine learning for warehouse managers.

New AI/machine learning applications will provide DC managers and industrial engineers with insights to:

Dramatically improve workforce planning and management
Proactively re-slot products to improve efficiency
Predict and eliminate stock outs and other exceptions
Optimize automation/robotics alongside human workers
Rapidly identify and implement other process improvements

Engineered Standards vs. Machine Learning

As a DC planning tool, machine learning represents an alternative to traditional engineering and process modeling.

For example, the traditional approach to workforce planning is to use an engineered labor standards system. ELS-based systems are programmed to calculate expected work completion times for a given task or group of tasks. They use pre-defined models of the process, a limited number of variables, and pre-determined or recorded average time values. ELS usually requires a significant upfront investment of time and money in engineering and measurement (and maintenance

In contrast, a machine learning solution uses algorithms to analyze warehouse data and develop a predictive model for workforce planning. The data can come from a number of sources, including warehouse optimization systems, mobile devices, and automation systems (or WCS).

One drawback of an engineered approach is that the formulas and measures need to be updated when the operating environment changes. Likewise, the models don’t account for all of the indirect variables that may affect results – for example, how congestion due to volume increases may impact efficiency. Finally, the more complex the engineered model, the longer it takes to process the data and provide an output. Machine learning addresses each of these issues.

To read the full article, click HERE.

Mars, AB InBev, and the Art of Supply Chain Sustainability

Sustainability | By Chris Cunnane • 05/05/2021

Supply chain sustainability and the concept of “going green” continues to drive a lot of operational initiatives and innovations as companies and countries look at their overall carbon footprint and look for ways to be more environmentally friendly. There is of course a system of checks and balances for global corporations when looking at supply chain sustainability, as even though they may have the best intentions, profitability is still the main business driver. Over the last few years, supply chain sustainability has become increasingly important, as manufacturers and retailers have taken the lead on green initiatives across the full spectrum of the supply chain, including in the warehouse, store, and through transportation.

At last month’s Blue Yonder Icon virtual summit, Lisa Manley, Vice President of Sustainability at Mars, and Ezgi Barcenas, Global Head of Sustainability for Anheuser-Busch InBev, detailed how their respective companies are focusing their efforts and strategies to support supply chain sustainability. Sustainability initiatives cannot exist in a vacuum. Both speakers touched on how the Sustainable Development Goals (SDGs) established by the United Nations are a driving force behind individual initiatives.

UN Sustainable Development Goals

The SDGs developed by the UN are a collection of 17 interlinked global goals designed to be a “blueprint to achieve a better and more sustainable future for all”. The SDGs were set up in 2015 by the United Nations General Assembly and are intended to be achieved by the year 2030 and are included in a UN Resolution called the 2030 Agenda.

The 17 SDGs are: (1) No Poverty, (2) Zero Hunger, (3) Good Health and Well-being, (4) Quality Education, (5) Gender Equality, (6) Clean Water and Sanitation, (7) Affordable and Clean Energy, (8) Decent Work and Economic Growth, (9) Industry, Innovation and Infrastructure, (10) Reducing Inequality, (11) Sustainable Cities and Communities, (12) Responsible Consumption and Production, (13) Climate Action, (14) Life Below Water, (15) Life On Land, (16) Peace, Justice, and Strong Institutions, (17) Partnerships for the Goals.

Mars and Supply Chain Sustainability

Ms. Manley Vice President of Sustainability at Mars, indicated that global businesses play a large role in helping to advance the SDGs. While there are 17 dedicated SDGs, Mars has been purposeful on where to focus their efforts. To that end, the company is prioritizing specific SDGs. First is SDG 1: no poverty. According to Ms. Manley, poverty plays a huge role in agricultural supply chains. The UN highlights the five “outcome targets” for SDG 1: eradication of extreme poverty; reduction of all poverty by half; implementation of social protection systems; ensuring equal rights to ownership, basic services, technology and economic resources; and the building of resilience to environmental, economic and social disasters.

The second SDG to highlight is SDG 17: partnerships for the goals. Mars has a deep belief that the only way they are going to make a meaningful difference from a sustainability perspective is by working collaboratively with others.

The third SDG that Ms. Manley highlighted, and went deeper into is SDG 13: climate action. Climate action is incredibly important for Mars and its supply chain partners. To that end, Mars started with a commitment to 100 percent renewable energy a number of years ago and is making steady progress. The company has established long-term power purchase agreements at the country level. As a result, Mars has been able to procure renewable electricity to cover the entire business in those specific countries. The next part is to drive progress toward a science-based target that goes across value chain. Mars is working with top suppliers to take action similar to what they have done with renewable energy and launched a “Pledge for the Planet” a few years ago, asking suppliers to join them in a pledge for sustainability.

Mars is also focused on advocacy. It is one of 300 companies that came together in a request to the Biden administration to dig deep in its ambitions for climate action. It is asking for country level goals to reduce emissions by 50 percent of more by the end of 2030.

Ms. Manley highlighted another area of sustainability that is part of its strategy: eliminating single use plastics. For Mars, becoming more sustainable with its packaging is a huge priority. The company looks at the initiative in four ways:

Identify the packaging they simply don’t need and stop using it. The company looks at different models of packaging use, and how it can be re-used or recycled. Ms. Manley gave the example of re-usable containers for pet food, which can be refilled at the store.
Once you know what you do need, redesign the packaging to make it fit into a circular model.
Namely, what is on the horizon for packaging technology? Mars has a dedicated team exploring the future of biodegradable papers and bio-based plastics.
Mars can make all the changes it wants with packaging designs, but if the systems for collecting and recycling used packaging aren’t established around the world, the program cannot scale.

AB InBev and Supply Chain Sustainability

In an audience poll during the session, next generation technology like artificial intelligence (AI) and machine learning (ML) for forecasting demand and pricing was driving the most interest in sustainability. According to Ms. Barcenas, Global Head of Sustainability for Anheuser-Busch InBev, these findings are consistent with what influences sustainability in most supply chains. AB InBev definitely sees an opportunity to use more data to understand the impact of climate change on the value chain. In agricultural supply chains, there is a disproportionate level of impact in vulnerable communities, so the more they can bring in modeling and predictive analytics, the stronger sustainability initiatives can be.

Ms. Barcenas highlighted two distinct areas that AB InBev is looking at for supply chain sustainability. The first is the company’s approach to water management. There is a saying in the brewing industry: “no water, no beer.” But Ms. Barcenas pointed out that water is a critical resource beyond just being a main ingredient in beer. As the world’s leading brewer, AB InBev is committed to being part of the solution in areas where they are seeing growing water stress. The company’s focus on water management goes across 3 approaches:

AB InBev is internally looking at water efficiency. The company is the most water efficient brewer in the world. Over the last decade, it has reduced its water usage over 40 percent. The company is working to continue that trend as part of its 2025 sustainability goals.
Promoting a reduction in water usage or improving the health of a water shed is great. However, companies need to have a measurable improvement piece for the results to stand up. To that end, AB InBev has a rigorous reporting process on how these improvements are being measured, and partner with the Nature Conservancy and World Wildlife Fund (WWF) in its initiatives.
AB InBev is working with farmers to be more climate resilient. This means ensuring farmers have the best practices in place, helping to provide the right crop management protocols. AB InBev uses AI and ML to help build a climate model and see how barley sourcing impacts the climate.

Ms. Barcenas also highlighted some of AB InBev’s procurement strategies for a sustainable supply chain. AB InBev has embedded sustainability in its procurement functions. The company has identified that over 80 percent of carbon emissions come from the value chain. The biggest opportunities are to enhance collaboration and innovation with their suppliers to address shared sustainability challenges. Ab InBev has over $30 billion in supply chain spend and tens of thousands of partners upstream. It needs to bring scale to new levels to innovate with them.

Part of this innovation was the launching of its Eclipse Initiative. The initiative was launched in 2019 and is a global sustainability platform. As of now, there are 50 suppliers that are part of the collaboration with a shared vision. The initiative looks to bring the suppliers together to co-innovate and co-design packaging and raw materials sourcing in the future. The end goal is improved supply chain sustainability.

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