SmartAB? Wisdom #54: AI In Supply Chain Management (SCM) – Part 4: It All Boils Down To A Single Word…

As a follow-up to my previous posts on the above, I would like to make the following observation: today, the most common references to using AI in Supply Chain Management (SCM) are all about the so-called Generative AI (GenAI).

Undoubtedly, Generative AI is helpful in some areas of SCM innovations, but as a 30-year AI veteran, I see patterns others don’t. To me, it’s just a small subset of the real advantages AI brings to the SCM table. Better yet, I can describe the most profound impact of AI on SCM’s evolution with just a single word: OPTIMIZATION…

Surprised? Well, try and substitute all the headlines on the above with their modified versions incorporating the word OPTIMIZATION – and you will see a perfect fit. Example? A simple Copilot query on how AI can optimize SCM brings the following response:

“AI can significantly optimize supply chains by enhancing various aspects of their operations. Here are some key ways AI can be beneficial:

Optimized Demand Forecasting: AI can analyze historical data and current market trends to predict future demand more accurately. This helps in better inventory management and reduces the risk of overstocking or stockouts.

Optimized Inventory Management: AI systems can track inventory levels in real-time, automate reordering processes, and optimize stock levels across multiple locations. This ensures that the right amount of stock is available where and when it's needed.

Optimized Routing: AI can optimize delivery routes by analyzing traffic patterns, weather conditions, and other variables. This reduces delivery times and fuel consumption, leading to cost savings and improved customer satisfaction.

Optimal Supply Chain Visibility: AI enhances visibility across the supply chain by integrating data from various sources. This allows for better tracking of goods, improved transparency, and quicker response to disruptions.

Optimized Predictive Maintenance: AI can predict when equipment is likely to fail and schedule maintenance before a breakdown occurs. This minimizes downtime and keeps the supply chain running smoothly.

Optimized Quality Control: AI-powered systems can detect defects and inconsistencies in products during the manufacturing process, ensuring higher quality and reducing waste.

Optimized Sustainability: AI can help companies optimize their supply chains to be more environmentally friendly by reducing waste, improving energy efficiency, and optimizing resource use.

Optimized Cost Reduction: By identifying inefficiencies and automating repetitive tasks, AI can significantly reduce operational costs.”

Implementing AI in supply chains can be complex and requires careful planning, but the potential benefits in terms of OPTIMIZING efficiency, cost savings, and improved customer satisfaction are substantial.”

Optimizing Transportation Logistics

Take routing optimization as an example. Truth be told, I spoke about innovative AI optimizing and solving Travelling Salesman Problem (TSP) solutions in the past. Solving it by calculating every permutation using a brute force approach doesn’t work.

Simply put, as the number of destinations increases, the corresponding number of roundtrips grows exponentially, soon surpassing the capabilities of even the fastest computers.

? With 10 destinations, there can be more than 300,000 roundtrip permutations.

? With 15 destinations, the number of possible routes could exceed… 87 billion.

For all the NP-complete problems that are based on factorial calculations, including the traveling salesman - methods such as Google Maps Route Planner or Excel Route Planner no longer suffice. Instead, Optimized Routing AI offers approximate solutions that are sufficiently accurate and very fast.

“At sea, the world’s largest shipping company, Maersk, leverages AI to optimize its shipping routes and fuel consumption. With an AI-powered routing system that factors in weather patterns, ocean currents, and other influencing factors, the company determines the most optimized route for each of its shipments.

And similarly, AI shows up in nearly all of Amazon’s processes, from months before delivery begins right up to when a driver is assigned to the delivery of a package to a customer’s doorstep. The company famously deployed AI-powered robots in its warehouses to maximize efficiency and cut back labor costs. The robots move packages around the warehouse, pick and pack orders, and transport packages to the shipping area.”

But the transportation logistics problems are no longer confined to just planes, trains, and automobiles. It equally applies to the more recent inventions – such as drone swarms…

Drone swarm missions are hard to manage. Full stop. Optimizing their paths is also a FACTORIAL problem. Without state-of-the-art AI, brute-force drone swarm deployments are ineffective…

According to GAO: “Drone swarm technologies coordinate at least three and up to thousands of drones to perform missions cooperatively with limited need for human attention and control. For example, an aerial drone swarm could potentially assist with controlling multiple wildfires, assessing damages, finding access points, and suppressing the fire by raining firefighting liquids on it—all with minimal human direction.”

And as previously mentioned, perhaps optimizing the flight path of a single drone is not too difficult, but dealing with hundreds of them… is

Optimizing SCM With Drone Swarms

“Drone swarms may be more efficient and robust for certain applications than single drones because swarms can complete a variety of tasks in parallel without human supervision. And they can continue operating if individual drones become inoperable.

Drone swarms can use various methods of command and control, including preprogrammed missions with specific predefined flight paths, centralized control by a ground station or a single control drone, or distributed control, where the drones communicate and collaborate based on shared information.

More advanced methods of control include swarm intelligence, inspired by the collective behaviors of insect colonies and flocks of birds, as well as artificial intelligence techniques to teach drone swarms to respond to new or unexpected situations.”

Drone swarm technologies and algorithms have become more mature in recent years. Advancements in artificial intelligence and machine learning have improved decision-making and obstacle avoidance.

High-speed communications technologies such as 5G and 6G networks have improved real-time data sharing among devices. Other advancements include energy efficient components, such as lighter materials and energy efficient motors, as well as advanced sensing technologies for environment mapping. In addition, there are now high-resolution cameras and infrared sensors for surveillance, reconnaissance, and search and rescue.

Despite these advances, drone swarm use remains limited due to a number of challenges. Most current drone swarm applications are still relatively simple. For example, aerial light displays are conducted with preplanned motions. Tasks such as tracking and determining the positions of multiple drones in uncontrolled environments still pose a significant challenge for drone swarm technologies. Weather conditions in emergency management situations like hurricanes or wildfires could exacerbate these challenges.”

“In recent years, drone technology, initially developed for military purposes, has significantly expanded to various industrial sectors. In agriculture, drones equipped with advanced imaging sensors and AI algorithms monitor crop health with precision, detecting early signs of nutrient deficiencies or water stress to optimize yields.

By integrating various sensors and working alongside other rescue equipment, drones enhance the effectiveness of disaster response efforts. Drones play a crucial role in search and rescue missions with their real-time mapping capabilities, quickly surveying large areas and creating topographic maps for efficient rescue operations.

Surveillance drones provide commanders with real-time information and situational awareness, which is essential for strategic decision-making. In military contexts, drones are indispensable for surveillance, reconnaissance, and combat. Armed drones execute precision strikes with minimal collateral damage, enhancing operational efficiency and reducing personnel risk.

Additionally, drones equipped with advanced sensors can detect and track hostile forces, enhancing military preparedness and response capabilities. However, as drone operations continue to expand in scale, traditional control methods utilizing Ground Control Stations (GCSs) and remote control reveal significant limitations, particularly in scenarios involving large swarms of drones.

These conventional methods are increasingly inadequate in terms of response time and operational efficiency. The reliance on direct communication between an ever-growing number of drones amplifies the risks associated with delays, packet loss, and mission disruptions.

Furthermore, the task of programming multiple drones individually proves to be not only highly time-consuming but also inefficient. This inefficiency is starkly illustrated by incidents at drone shows, where numerous drones have collided due to errors in direct programming.

Such events highlight the critical need for more advanced and reliable methods of managing and controlling large-scale drone operations to mitigate these risks and enhance overall performance.

Optimizing Warehouse Management

According to Gartner: “Large-scale warehouse automation is a complex process in which many factors and variables must be properly accommodated to get it right. Warehouse automation has become an essential aspect of modern logistics operations.

The growth in automation options and their positive impact on labor, productivity, throughput, and capacity is expanding the global warehouse automation market. This market is expected to reach $71 billion by 2032, at a CAGR of 15.91% during the forecast period 2023 to 2032, with the Asia/Pacific region expected to grow fastest.

As the warehouse automation market expands with alternative solutions, emerging vendors, and increased options, more businesses are exploring their choices. Many organizations, however, don’t have the knowledge or expertise required to evaluate their decisions and successfully adopt automation.

The road is fraught with potential pitfalls that can hinder success and prevent companies from achieving their goals. Some of the common mistakes companies make when adopting warehouse automation solutions are misaligned expectations and goals, choosing the wrong solution and not considering potential post deployment challenges ahead of time.

The role of AI in the transportation and logistics industry is varied. It has led to significant advancements in several operations, the most talked about of which has to date been warehouse automation.

Robots have helped efficiently sort, pick, and pack inventory, considerably speeding up order fulfillment. However, while this has dramatically improved efficiency in warehouse processes, it is only the tip of the iceberg when it comes to the potential applications of AI.”

“AI makes it possible to deploy smart warehouse systems that can rapidly adapt and respond to new scenarios and optimize operations across the entire logistics network. As a result of streamlined, optimized warehouse operations, overall productivity goes up substantially.

Logistics operations are key to supply chain operations, right from procurement to production and distribution. Optimizing logistics is thus critical to successful demand fulfillment. AI enables businesses to optimize the movement of material to ensure order fulfillment at the least possible cost – leading to enhanced customer satisfaction and a much-needed competitive edge in the market.

In 2021, it cost more than $20,000 to ship a regular 40-foot container from China to the US east coast – compared to less than $3,000 only two years ago. In the face of such disruptions, it is essential for businesses to maximize the outcomes of their logistics processes and enhance the value derived from all of their logistics assets.

AI helps this by offering fully transparent visibility into fleet performance, allowing logistics executives to strategically utilize their assets as well as safeguard them against unanticipated risk.

AI also enables businesses to match capacity to demand – thus decreasing shipment of empty containers, and reducing the number of vehicles on the go. These vehicles can then be irected to locations where there is demand (or is predicted to go up), ensuring efficient asset utilization while significantly reducing operational costs. In summary, AI in transportation and logistics holds the potential to accelerate efficiency, cost, and sustainability across the entire logistics network.

Generative AI In Procurement

As McKinsey is quick to point out: “GenAI is an AI model that generates content in response to a prompt. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. Much is still unknown about gen AI’s potential, but there are some questions we can answer—like how gen AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of AI and machine learning.”

“In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements.

While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts.

A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.

Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion to the global economy—annually.”

And GEP adds: “Given their complexity and interconnectedness, supply chains are a perfect setting for applying generative AI technologies that help create synthetic data and models and facilitate simulations, scenario planning and risk analysis, thereby improving supply chain resilience and adaptability. Enterprises can apply generative AI tools for several purposes, including:

? Knowledge capture and information retrieval: Generative AI, like ChatGPT, starts from a broad knowledge base and learns from interactions, absorbing and expanding insights to refine responses for accuracy and relevance.

? Contextual understanding and supply chain cross-domain knowledge: ChatGPT comprehends and retains context, referencing previous messages to enable coherent interactions. This extends to supply chain cross-domain knowledge, enabling it to gather insights into logistics, sourcing, procurement and more, which it can then leverage to provide relevant responses for effective communication and decision-making in complex supply chains.

? Creativity and language generation: ChatGPT generates creative and contextually appropriate responses, going beyond facts. It engages in imaginative discussions, fostering innovative thinking and creative problem-solving.

To stay competitive, organizations need to prioritize evaluating and implementing AI solutions quickly — as AI is here to stay, watching on the sidelines is no longer an option, and any delay presents a competitive disadvantage. Organizations must also think strategically, beyond just bolt-on plug-ins and applications or force-fitting solutions without determining how they meet requirements.

By taking an AI-first approach and considering where and how they can apply AI’s potential to bring real value, organizations can implement intelligent-by-design solutions that are scalable, flexible and future-proof.

Generative AI is poised to become a creative collaborator — working alongside people to amplify their skills — transitioning past automation and analysis to generating solutions, anticipating disruptions and challenges and inventing entirely new pathways for procurement and supply chain leaders.

The technology simplifies supply chain management, mitigates supply chain disruption, evaluates and selects suppliers, decreases supply and supplier risk, drives sustainability and ESG (Environmental, Social and Governance) initiatives and furthers ethical and sustainable sourcing.

But beyond that, it will simulate disruptive scenarios and develop action plans, create contracts with legal nuance and specific requirements, optimize inventory levels based on data, trends, and forecasts, and reshape supply chains for constant optimization — all quickly and seamlessly.

By embracing experimentation, organizations can explore the application of generative AI in these areas. They must equip themselves with the knowledge and skills to navigate the landscape, engage with AI effectively and make informed decisions. By doing so, companies can avoid potential risks and leverage the power of generative AI to enhance human creativity and optimize production while maintaining human control and oversight.”

Hence, “GenAI empowers procurement and sourcing with intelligent automation, streamlining supplier management, purchasing, and risk mitigation. Unlike conventional procurement systems that rely on structured data and fixed rules, GenAI leverages vast amounts of structured and unstructured data to offer real-time insights and actionable outputs, transforming key areas of procurement and sourcing.

One of the most significant benefits of generative AI is automating manual, repetitive tasks. Procurement processes like drafting contracts, purchase orders, and RFQs can be automated with GenAI, which analyzes historical data to generate accurate and consistent documents. This not only saves time but also minimizes human error, ensuring legal and compliance standards are met seamlessly. By automating these tasks, procurement teams can focus on more strategic areas, boosting overall efficiency.

The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy.

In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

Developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task.

If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

In terms of supplier sourcing and risk management, GenAI can synthesize both internal procurement data and external market insights to offer smart, data-driven insights. Procurement teams can leverage these insights to develop optimized negotiation strategies and proactively manage supplier risks. GenAI’s ability to analyze supplier performance and identify potential risks, such as market fluctuations, helps organizations make more informed and resilient sourcing decisions.

Predictive insights and scenario analysis further enhance decision-making in procurement. By analyzing historical trends, GenAI can provide valuable insights on pricing, demand, and supplier performance, which can be further enhanced when combined with real-time data analysis systems. This helps procurement teams mitigate potential disruptions in the supply chain before they occur, enabling proactive risk management.

Generative AI also brings custom recommendations to procurement operations. By integrating AI-powered recommendation engines, procurement platforms can suggest optimal sourcing strategies or suppliers tailored to real-time market conditions and company needs. These recommendations, based on both internal spending data and external factors like logistics and pricing trends, allow organizations to act swiftly and avoid costly delays or disruptions.

Lastly, GenAI drives improvements in advanced spend analytics, where it enhances the ability to categorize spending, uncover cost-saving opportunities, and highlight strategic areas for procurement improvement. GenAI ensures organizations maintain strong, favorable agreements with their suppliers by analyzing contracts and identifying opportunities for renegotiation or highlighting inconsistencies.

In essence, generative AI offers a fundamental transformation in how procurement and sourcing functions are managed, enabling organizations to make smarter, data-driven decisions. By providing insights from historical data, automating routine processes, and enhancing traditional predictive analytics, GenAI empowers procurement teams to proactively manage risks, optimize costs, and drive overall efficiency.”

As recently stated by Purolator: “In the business world, there are already practical applications for the role of AI in the supply chain that logistics leaders are capitalizing on, whether it’s using ChatGPT to ask questions regarding demand forecasting or prompting bots to produce risk assessments. According to a study by McKinsey, early adopters of AI have had impressive results, improving costs by 15%, inventory levels by 35% and service levels by 65% compared to slower-moving competitors.

Planning for the future role of AI in the supply chain is crucial for staying ahead in today’s dynamic business landscape. As technology continues to evolve and transform industries, integrating AI into the supply chain can unlock new levels of efficiency, productivity, and competitiveness. The best way to embrace the potential that accompanies AI is for companies to start by assessing their current processes and identifying impact areas”…

For More Information

Please see my other posts on Linkedin, Twitter, Substack, and CGE’s website.

AI Boogeyman

You can also find additional info in my hardcover and paperback books published on Amazon: “AI Boogeyman – Dispelling Fake News About Job Losses” and on our YouTube Studio channel…

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