Artificial Intelligence Archives

How Artificial Intelligence Benefits Logistics & Warehouse Management

Artificial Intelligence | By Kait Peterson • 06/20/2024

As customer expectations rise and order volumes surge, logistics managers face immense pressure to streamline operations and maintain a competitive edge. How can logistics managers handle these challenges? The key to navigating this complexity and achieving operational excellence lies in adopting artificial intelligence (AI). In my conversations with customers and the marketplace, AI is coming up a lot as a solution, but it’s a topic that comes with confusion and sometimes uncertainty.

To alleviate the confusion, you need to know the difference between AI and generative AI. AI in the warehouse doesn’t mean using ChatGPT in your warehouse. You could use ChatGPT, of course, but that’s generative AI. Instead, AI in the warehouse involves robots, who are intelligent agents powered by AI, software, and platforms that will help logistics managers make real-time data-driven business decisions for their operations every day.

As far as uncertainty, that’s where conversations, education, and knowledge about AI comes into play. Here’s how logistics managers are thinking of implementing AI from my conversations with customers and from our own internal talks at Locus.

Dynamic Adaptability: The New Standard for Warehouse Operations

Modern warehouses operate in a state of constant flux with inventory levels fluctuating dramatically, influenced by seasonal peaks, promotions, and sudden market shifts. Traditional logistics and warehouse management systems, with their rigid structures, struggle to keep pace with these rapid changes. AI will introduce a new level of adaptability, enabling real-time decision-making and operational adjustments. By analyzing current data, AI-driven systems will anticipate changes in order volumes and optimize workflows on the fly, ensuring that warehouses remain efficient and responsive.

Enhancing Efficiency with AI-Powered Robotics

The chaotic environment of a logistics center, with human workers, forklifts, and goods in constant motion, requires precision and agility. AI-powered robots, equipped with advanced sensors and machine learning algorithms, seamlessly avoid obstacles and collisions, and adapt to the dynamic conditions of the warehouse floor. This not only enhances operational efficiency but also ensures a safer working environment for human workers.

Prioritizing Safety with Intelligent Navigation

Safety is a paramount concern in any logistics center. With numerous moving parts and potential hazards, ensuring a safe workspace is a significant challenge. AI enhances safety through intelligent navigation and real-time decision-making. AI-powered robots calculate the safest and most efficient paths, avoid obstacles, and execute complex maneuvers such as multi-robot passing in tight spaces. This level of precision reduces the risk of accidents and creates a safer environment for both robots and human workers alike.

Proactive Response to Market Demands

The unpredictability of consumer demand, exacerbated by events like flash sales and viral promotions from social media trends, can lead to sudden spikes in order volumes. Traditional systems often struggle to cope with these surges, leading to inefficiencies and delays. AI excels in predictive analytics and will be able to use historical data to forecast demand trends. This proactive approach will allow logistics centers to scale operations effectively, reallocating resources and optimizing picking routes to handle increased throughput. By anticipating demand surges, logistics managers can ensure that their operations run smoothly, even during peak times.

Mastering SKU Complexity with Artificial Intelligence

Modern warehouses manage a diverse range of products, from small electronics to large industrial equipment. Handling this variety, with numerous SKUs, is a complex task for human workers. AI systems, however, thrive on complexity. They will help to optimize storage layouts, streamline picking routes, and adapt to inventory changes in real-time to ensure that every product is stored, retrieved, and shipped with maximum efficiency. By mastering SKU complexity, AI will enable warehouses to operate at peak performance, regardless of the diversity of their inventory.

The Strategic Advantage of AI in Logistics

For logistics managers, the integration of AI into warehouse operations is not just about enhancing efficiency; it’s about gaining a strategic advantage. AI will provide a level of insight and control that is simply unattainable with traditional systems. By leveraging AI, logistics managers will be able to make informed decisions, optimize resource allocation, and improve overall operational performance. This strategic use of AI can lead to significant cost savings, increased productivity, and a stronger competitive position in the market.

Practical Steps for Future Implementations of AI in Warehousing

For logistics managers looking to integrate AI into their facilities, a strategic and phased approach is essential. Here are some practical steps to consider when the technology is ready:

Assess Your Current Operations: Begin with a thorough evaluation of your current warehouse operations. Identify areas where AI can have the most significant impact, such as inventory management, picking and packing, or safety protocols.
Set Clear Objectives: Define what you aim to achieve with AI implementation. Whether it’s improving efficiency, reducing operational costs, or enhancing safety, having clear objectives will guide your future AI strategy.
Choose the Right Technology: Research and select AI solutions that align with your specific needs. Look for systems that will offer scalability and integration capabilities with your existing warehouse management systems. Also look for solutions that are proven in the marketplace to have driven real results.
Invest in Training: Ensure that your staff is well-trained to work alongside AI systems. This includes understanding how to operate AI-powered equipment like robots, and how to use AI-driven data insights for decision-making.
Pilot Programs: Start with a pilot program to test the AI technology in a controlled environment. This will allow you to assess its impact, make necessary adjustments, and build confidence before full-scale implementation.
Monitor and Optimize: Continuously monitor the performance of AI systems and collect data on their impact. Use this data to optimize operations further and address any issues that arise.

Why AI for Logistics Management

The benefits of AI in logistics centers are clear, but adopting this technology requires a strategic approach. Logistics managers must be willing to invest in AI-powered systems and integrate them into their existing operations. This involves training staff, upgrading infrastructure, and continually assessing and optimizing AI-driven processes. While the transition may involve challenges, the long-term benefits of enhanced efficiency, safety, and adaptability make it a worthwhile investment.

As the logistics industry evolves, AI is set to play a pivotal role in shaping its future. From improving operational efficiency and safety to managing SKU complexity and responding to market demands, AI offers unparalleled advantages. Logistics managers who embrace AI will not only enhance their current operations but also position their organizations for future success.

By following a strategic approach and leveraging the future capabilities of AI, logistics managers can transform their warehouses into agile, intelligent, and highly productive environments.

Kait Peterson is an experienced leader in supply chain marketing currently serving as the Vice President of Global Product Marketing at Locus Robotics, where she shapes the global product marketing strategies, leads market intelligence, and drives go-to-market initiatives.

Kait comes from over a decade of experience in supply chain technology including leadership roles in marketing and M&A integration with companies such as Korber Supply Chain, Blume Global, Kaleris, and Navis. A passionate advocate for diversity, equity, and inclusion, Kait holds an International MBA from the University of Denver.

With her many contributions to the industry, Kait has recently won the SDCE Pro to Know award. Beyond work, Kait enjoys reading, outdoor activities, global travel, and has a keen interest in Stoic philosophy and ancient history.

Generative AI: Cheat Code to a More Competitive Supply Chain Business?

Artificial Intelligence | By Pedro Quiterio • 06/15/2023

You’ve probably already encountered it in a retail setting. You call a support center and a chatbot answers the call, understanding your natural language and providing information on shipment status. This is called ChatGPT, a natural language processing (NLP) tool that runs on artificial intelligence (AI) to conduct human-like discussions in order to assist you with various requests.

We’re already seeing it in diverse applications, such as writing emails or essays. Even writing code. You ask the bot questions, and give it commands, using nothing more than your voice, and the bot understands enough to provide a remarkably accurate response.

Make no mistake. This technology is coming to a supply chain near you, and the sooner you understand it and get comfortable with it, the better positioned you will be when hits the industry in full force.

Generative AI Explained

The technical term for it is “generative AI.” Simply put, it is AI that “generates” output to fit a specific task at hand. Using computer algorithms, the AI program searches various connected inputs from all available sources to generate human-looking content in the form of text, images, audio, and other synthetic (created) data that is of value to the user.

In many ways, it is the next logical progression of predictive analytics and machine learning combined.

Where predictive analytics uses algorithms to look at established patterns in various contexts to suggest what might happen next, machine learning watches for feedback to improve its performance over time, refining itself for a more appropriate response.

Generative AI combines the two concepts in a conversational context. The machine understands your voice, and with that input, it intelligently scours the universe of data to serve up a refined response in whatever format you need to accomplish the task.

Generative AI in Practice

With little imagination, it is not difficult to see how this technology can be a game-changer for a logistics service provider (LSP).

A transportation manager comes in to start the day. Overnight, the system has already recognized the shipments due out, the carriers in play, available inventory, staff, orders in progress, traffic and weather patterns, etc. The manager asks aloud, “What are our priority shipments for today and do you see any potential issues based on current status?”

In seconds, the bot responds with a full report on screen, showing problem areas flagged, and brief notes highlighting concerns and potential solutions. No pointing, clicking, or typing is involved.

The manager glances at the screen and sees a shipment to Louisville, KY flagged due to a storm delay. He asks the bot, “How long will the storm delay ABC Company’s shipment to Louisville and, can you find a better route?”

The bot answers in a human voice, “The storm will delay the shipment by four hours. Carrier B can reroute the shipment if the load is transferred to them by 2 o’clock, saving one hour in transit. Do you want to execute the transfer?”

The manager hesitates, then asks, “Will the change in carrier add cost to the customer?”

The bot answers, “Yes. A $115 upcharge will apply. Do you approve of the change?”

The manager answers, “No. Contact the customer and ask them if they will approve it.” The bot then pings the customer with the information and a reply comes back within minutes.

The bot responds, “The customer in Louisville does not approve the upcharge. The four-hour delay is acceptable. Do you want to ship it?” The manager confirms the shipment. The order is processed to the customer’s satisfaction.

Coming to a Supply Chain Near You

The scenario described above is not that far off. The technology already exists and, in the opinion of this writer, supply chain companies will begin deploying applications capable of just this kind of interaction by next year, if not sooner.

In short, generative AI represents a new era of staff augmentation. It is not meant to replace warehouse workers. It is meant to supplement decision-making, using AI to quickly survey all available data on a given topic, provide a quick and accurate analysis of the situation, offer up practical recommendations to the current situation, and support human decisions through automation. All in real-time. All the graphic, written, or audible outputs are appropriate to effectively communicate and execute the transaction.

The benefits are tangible. Workers spend less time performing mundane, time-consuming tasks and more time making strategic, value-added decisions. Customers experience better service. Partners work more seamlessly together. And new efficiencies are realized, as technology improves productivity and human and financial performance at once.

Prepare for the Next Competitive Edge

LSPs who begin thinking in terms of AI-enhanced operations will have a leg up on the competition when the phenomenon manifests. The best course of action is to learn more now.

Be aware of the technology and ensure that your existing infrastructure will be able to accommodate the capability when it arrives. Perform a technology health check and assess your best practices. Identify areas where a generative AI pilot project might improve results quickly.

Talk about the topic and start asking questions. Are you on the cloud? Do you have a capable partner? Are your people prepared for it? By cultivating a curiosity for it and laying the foundation for it now, you will begin to position yourself for the next evolution of competitive leverage.

The supply chain is ripe for generative AI. Are you?

Pedro Quiterio, Senior Supply Chain Consultant at Open Sky Group, is a seasoned, supply chain technology and logistics professional with over 15 years of experience in the industry. Pedro has a proven track record of streamlining operations, reducing costs, and improving delivery times through the implementation of innovative technology solutions. Pedro has a deep understanding of logistics systems, transportation operations, and supply chain management processes. With meticulous attention to detail and a systematic approach to problem-solving, Pedro has successfully managed large-scale logistics projects for leading companies in many industries. He is dedicated to delivering high-quality results and continuously seeks new opportunities to enhance and optimize supply chain processes.

Open Sky Group, global specialists in the Blue Yonder supply chain solutions (formerly JDA RedPrairie Software), helps companies like yours implement and upgrade warehouse, labor, and transportation management systems. To learn more, visit Open Sky Group today.

What is AI-managed task execution and optimization, and does it work?

Artificial Intelligence | By Bengt Tuner • 10/13/2022

Many companies use the buzzword Artificial Intelligence (AI) in their sales and web pages. Extreme benefits are promised with minimal effort. Install an app on your mobile device, access a software platform, heaven opens up, all the benefits are showering down on you, and everyone walks around with a big smile. Does this sound familiar to you?

The actual situation is different. Companies struggle to get competitive value using AI, and there are several reasons for this, varying from overpromised sales messages down to how companies apply AI.

Applying AI for cross-functional task execution and optimization that includes all functional areas requires a holistic approach using internal and external data. A cherry-picking approach focusing on selected functional areas of the supply chain will not bring any miracles for task execution and optimization. Then you are stuck in zoning, dedicated spaces for resources and silos of optimization.

Another mistake many companies make is installing AI to provide data for manual task execution and optimization decisions. This is, unfortunately, another showstopper. Because humans can’t recognize patterns supplied by internal and external data, this is too complex.

What is the right approach to maximal output when using AI for task execution and optimization?

Miracles can happen if you use AI for decisions across functional areas; this will help you to change from a manual-run operation to a highly efficient and system-run environment without manual decisions.

Bengt Tuner is Group CEO of Locanis. Bengt has extensive experience as Entrepreneur and expert in Supply Chain Management and Supply Chain Execution. With over fifteen years experience as Senior Manager and CEO for small, medium and large transport & logistics companies and more than ten years experience as CEO for IT companies building front-end Supply Chain Execution Software.

Emerging Tech in Your Fulfillment Network: Stepping Beyond Digital Transformation

Business Intelligence | By Diego Pantoja-Navajas • 06/10/2021

As an entrepreneur I’ve been reflecting on this a lot: The current milestone in logistics and fulfillment is using emerging technologies to capture and leverage exponentially growing data sets in warehouses and throughout the entire fulfillment network. Data sets have grown quickly in the cloud paradigm – and they exploded in 2020. Perhaps nobody realized how quickly or how extensive that growth would be, but all of that data has value. How are we as supply chain leaders and businesses tapping into that data and using it to make our supply chains more efficient?

This intelligence wasn’t broadly available to us ten years ago. Cloud technology uncovered it, Covid-19 accelerated it, and today, right now, businesses need to tap it to become competitive. The challenge is that these data sets are growing too quickly, and businesses are challenged with the task of capturing, managing, understanding, and finally acting on it without having an assist – and that’s where emerging technology comes in. Automation and autonomous technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) can help supply chains harness the abundance of data to gain predictive insights into their business to increase their value and efficiencies. Leveraging different models of thinking, representing key metrics using data aggregation, exposing real anomalies, and recommending a course of action based on operating models will make businesses smarter.

The rise of autonomous technologies

There has never been a doubt in my mind that automation and autonomous technologies were the next evolution of our industry after cloud technology. Now that the world is finally understanding how fragile supply chains and businesses are in a demand driven model, businesses and consumers want supply chain leaders to leverage these advanced technologies as soon as possible. Business leaders have learned how expensive it is to manage this data manually, and they are aware their businesses cannot scale by simply adding more resources at the pace supply chains are moving. They are aware of the untapped competitive value and competitive advantage of managing data automatically. However, they are frustrated that their organizations are not quickly pivoting to adopt these technologies to extract and leverage this data, get faster insights requiring less resources, and reduce overall IT costs.

As the years pass, many supply chain vendors have decided to add intermediate layers to their enterprise resource planning (ERP) systems or warehouse management systems (WMS) as a strategy for adding automation and low-level emerging technologies. For example, Warehouse Execution Systems have been labeled as having the ability to provide unprecedented, automated business intelligence, but remember, this is a whole layer of functionality that sits on top of a WMS or ERP. A whole layer of technology had to be invented to solve this problem, and forward-thinking companies – not having the solution already available in their WMS platforms — are investing in middleware applications in order to automate and ultimately tap that reservoir of information. In my head there is no need for another layer of integration. The solution should be as simple as having one true SaaS Cloud WMS, and bring that automation and autonomous technology layer into the WMS itself. What I wanted to solve was bringing an automation layer in without creating a layer of complexity – everything managed within one application – without worrying about integrations and third-party, bolt-on, software.

Harnessing the power of managing data within a single database

One year ago, we moved in that direction by migrating our cloud native, mission-critical SaaS application to Oracle Cloud Infrastructure (OCI – Gen2) and moved all our Rackspace and AWS PostgreSQL databases to Oracle Autonomous Transaction Processing (which also runs on OCI-Gen2) for better performance, higher elasticity, improved security and reduced TCO. To fully understand our strategy, it is important to understand the differentiation between autonomous and automated. For example, a warehouse process that can be accomplished automatically is still not autonomous if a warehouse user needs to respond to an alert, make decisions, or synchronize different systems to initiate the automated activity. In contrast, an autonomous system combines the power of managing data within one single database in the cloud while also applying advanced algorithms to process KPIs, detect anomalies, predict future scenarios, offer prescriptive courses of action, and provide forecasts based on multi-dimensional trends.

Oracle Warehouse Management does this! We are able to differentiate and provide unique value to our customers by leveraging the in-database machine learning algorithms available within Autonomous Transaction Processing. We deployed more than 40 different AI/ML algorithms. This approach has further helped our customers by improving their logistics network capabilities and efficiencies not just by generating automated tasks but by helping them predict future target values, calculating expected profit from each order fulfilled, or rerouting orders within the warehouse to improve overall warehouse flow to optimize space and make better of use of robotics and other material handling components. Business executives want to go to Mars, but a good CIO knows that first she/he has to get to the launching pad. Operations teams want practical applications for their emerging technology plays — practical use cases for automation and autonomous technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT).

Next generation cloud platform

As a next generation cloud platform and leveraging our modern cloud native REST services-based architecture we rely on API calls to connect to any material handling equipment (MHE), robots, or smart machines in real-time. We orchestrate all these different systems, robots, or smart machines by constantly making API calls. We offer our customers limitless performance by instantly and transparently scaling up as demand increases making it easy to accommodate peak processing workloads in zero downtime. The elastic, independent, and autonomous scaling of our computing enables our true cloud capabilities.

As a platform that was built in the cloud, for the cloud, our integration capabilities are seamless. That ease of connectivity means we are naturally positioned to meet that next milestone of using emerging technologies to capture and leverage data sets throughout the entire fulfillment network. We are fully capable of helping the enterprise level business speed up their innovation and maybe even get ahead of their industry. Furthermore, we can do it without adding complexity to the system and without having to worry about integrating bolt-on technology.

The wave is here, and a race is on to see who can catch it first. It’s too much intelligence to let go to waste, and this intelligence was always the ultimate promise of cloud technology and we are ready today to help you realize it.

Diego Pantoja-Navajas is vice president of WMS development at Oracle and former founder and CEO of LogFire. He is an industry thought leader in SCM who enjoys exploring the impact of advanced and emerging tech on supply chains.

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.

Driving Sustainability in the Age of Supply Chain Disruption

Sustainability | By Jon Chorley • 04/22/2021

The global pandemic has created a watershed moment for global supply chains. As we adjust and respond to this “new normal”, one important area is the effect of these shifts on the environment. From that perspective some changes have been clearly positive (e.g. fewer commuting miles driven), some largely neutral (e.g. alternate sourcing strategies), while others are potentially negative (e.g. the daily delivery of packages to our homes). Alongside all of this is the growth of automation and related technologies which also have environmental considerations. Combined with the heightened concerns regarding climate change and associated sustainability impacts, all businesses should consider and assess these changes using the lens of environmental sustainability.

Managing Disruption and Change with Emerging Technologies

Emerging technologies play a role in managing disruption and are driving change in the transportation and logistics industries. We are now seeing an increase in the use of automation, not just within a TMS or WMS, but the digital coordination across all phases of logistics, from shipper to broker to carrier to warehouse. AI and machine learning provides more accurate information during the transportation planning process; using IoT to monitor shipments, cargo conditions, and yard management have allowed companies to make quick adjustments when disruptions occur; and mobile digital assistants provide feedback and instant visibility into shipments and inventory.

Business logistics costs as a percentage of GDP is currently 7-8% in United States. In developing countries it can be more than double that. Economic development is both measured by and based on logistics. As supply chains continue to evolve to drive more efficiency, we will see changes in three main areas:

Physical infrastructure – Electrification and low emissions transportation and IOT supported by high volume ubiquitous connectivity via 5G
Systems infrastructure – Cloud and improved information sharing through social and business networks, often enabled by blockchain
Applications techniques – Using AI/ML to drive real-time insights and decision making – plus improved optimization, resilience and responsiveness

All of these come together in some way with autonomous transportation options. These will most likely show up first in contained environments such as yard management – possibly augmented by some human oversight. Volvo’s POD in Sweden is a great example. The knowledge gained in these settings can be used for subsequent expansion to other, less structured, environments.

So when we talk a lot about Industry 4.0 or the 4th industrial revolution – the combination of smart technology, machine to machine communications and IOT – it’s good to remember that the logistics space is right in the middle of that, both in a business and consumer context.

The net result of all of these developments is to drive increased efficiency, both in terms of activity within a given logistics “leg”, but perhaps more importantly, between those legs. Combined this with a shift to clean energy to power those activities, and you get a very significant environmental benefit.

Information Driving the Transformation of Value Chains

Being able to collect, analyze and decide using that information, and to be able to do all this in real-time, will give companies competitive advantage. FedEx founder and chairman Fred Smith, famously said “Information about the package is as important as the package itself.”

The keys to success are security, opt-in, and transparency. There is value to sharing data, like receiving suggestions for a better route, more accurate ETAs, and complementary product choices. Your movement as determined from your smart phone is what helps generates those redlines on your car’s map. When value accrues to both the people who supply that data and the people who receive and use it – and everyone is aware of the exchange – then it’s a fair and even playing field. The choice of whether to participate needs to be up to the individual, and the penalties of breaking that trust need to be equally clear.

What applies to personal information, also applies to information sharing across business value chains. If there are greater mutual benefits to sharing data than to holding it private, then more data will be shared and this will tend to yield efficiency benefits and in turn, to environmental ones.

“Speed is the New Cheap”

Transparency and choice apply to both businesses and consumers. Knowing the impact – to people, to profits, and to the planet – of your decisions. I often say “speed in the new cheap” but there is a price to pay for that speed, and that price is often paid by the environment without us being aware. Residential deliveries that made up 25% of US parcel volume in 2000, grew to over 50% pre-pandemic, and has grown more since. That’s not just limited to the US. China increased its lead in global parcel volume in 2019, shipping more than 63 billion parcels last year, representing 26% YoY growth and making up substantially more than half of global parcel volume. Informing the consumer of the impact of their choices and then providing options to combine shipments or delay till a weekend delivery – each of these tagged by environmental implications (e.g. a “green leaf”) – can help drive choices that improves consolidation and reduces environmental impacts. As can smarter consolidation and routing algorithms at the shipper, as well as the shift towards electrification in local delivery fleets.

And in general, we must continue developing those cost-effective electric transportation options, whether it is transportation on-demand or public transportation and adopt these at scale. These will have a big impact on sustainability.

Jon Chorley is chief sustainability officer for Oracle, where he drives and coordinates all initiatives, both internally and externally, related to environmental sustainability. This responsibility covers all areas, from IT infrastructure and business operation to corporate reporting and risk management. Jon is also group vice president of product strategy for Oracle’s supply chain management (SCM) applications and leads the team responsible for driving the business requirements and product roadmaps for these applications. Chorley has more than 25 years of experience in the software industry in a broad set of roles including sales, implementation, and development.

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