A nation divided, a political party at war with itself, bitter rivals seeking nomination, an assassination attempt. If this seems like a transcript of the evening news, representing some of the most challenging times in US history, think again. This was the setting for Abraham Lincoln’s first inaugural speech in 1861. The president’s call to “the better angels of our nature” is an enduring line, both because of the beauty of the words and the meaning behind them. Their meaning illustrates key things about our humanity and reminds us not to surrender our strengths in the age of AI.

To get the most out of AI, we have to know what it is good at and how to use it. It’s a wonder of our modern age, and every day there’s a dazzling new accomplishment in the news. Yet its dazzle can distract, blinding us to our own strengths, leading us to surrender too quickly our own formidable powers. Knowing what AI can do masterfully and where it fails miserably is key to setting expectations and maximizing its potential.

The frontier of AI strengths

Recognizing this challenge, last fall leading academics from Harvard Business School, Wharton, MIT and the Boston Consulting Group (BCG) published an illuminating paper, which they artfully named “Navigating the Jagged Technological Frontier.” To determine the frontier of capability, they tested when to use AI and when to rely on our humans by designing an experiment using BCG consultants. The entire paper is worth reading, but here are three key takeaways.

The first is that when the BCG consultants used AI for knowledge-intensive tasks for which technology was expected to excel, labeled “inside the frontier”, it dazzled as anticipated: they got 12% more done, 25% faster, at 40% higher quality. Masterful! The second is that it performed miserably compared to humans at tasks outside the frontier, leading to a 19% performance drop, when many of the consultants blindly trusted AI answers over their own. And third, the frontier is not clearly defined. We must navigate when to use AI carefully.

Pattern-finding machines

AI’s strengths are savant-like, deep but not always broad. A reductive explanation of AI is that it is a pattern-finding machine. Feed it enough data and it learns patterns in that data that enable it to predict future behavior. If a forecast merely projects forward sales history, AI can predict demand even more precisely by layering additional “signals,” such as the impact an upcoming concert is likely to have on burger sales for a restaurant. AI can analyze so much data so quickly that it can recognize patterns beyond the scope of our cognitive capacity, like part combinations that are regularly late from suppliers.

That same pattern-finding machine is at play with large language models, which I call probabilistic sentence completion machines. As much as you may think it understands what you ask, the words it generates are the result of predicting the next most likely word, based on learned patterns. Generative AI responds with such finesse and aplomb that it may seem to have a personality, but underneath the hood mathematical models are generating its response.

Meaning-making machines

AI is a master at patterns but doesn’t understand meaning, whereas we humans are meaning-making machines. Viktor Frankl, the famed Austrian Holocaust survivor who wrote Man’s Search for Meaning, founded a whole school of psychotherapy that posited that this drive is our central motivational force. Meaning is behind the three C’s I argue that AI lacks: context, collaboration, and conscience. AI cannot derive meaning from context, collaborate to build relationships to get things done, or make an appeal to our conscience, as Lincoln’s speech did.

AI is a transformative, disruptive force, in part because our meaning-making mentality feels uneasy with AI’s prowess. I facilitated a series of conversations with executives this past week for a forum on supply chain, and I resonated with one CPG leader, who said her aim is to simultaneously build excitement for AI but also to manage it. According to Zero100, mentions of AI on earnings calls have increased 120% over the last two years, indicating that excitement is high among boards and leaders.

Yet a recent YouGov survey on AI reveals the top feeling most cited by the American public is “cautious” (54%), with “excited” only ranking 6^th (19%). This view is echoed in Canada and the UK, where consumers have a generally negative view on AI, in contrast to the positive view of most other countries surveyed by the XM Institute.  The biggest concern consumers cited in the XM Institute survey was a “lack of a human being to connect to.” Even in a mundane retail interaction we seek connection.

Three C’s: context, collaboration, and conscience

Part of what builds connection is understanding context, which AI cannot do. AI can automate generation of emails and calendar scheduling, but what it cannot understand is the nuance. Anyone who has ever worked with a stellar executive assistant appreciates their ability to know meetings can be delayed or cancelled and which cannot, judgement calls based on understanding meaning, emotion, and even culture. AI can analyze data but not the meaning behind it, which is why the domain expertise of demand planner who understands her company’s data and business is invaluable to applying AI to forecasting.

A leading AI researcher and computer science professor recently lamented to me that people need to turn away from their screens and talk to each other more, because that’s where innovation happens, in conversations that spark collaboration and ideas. Google made millions from the pattern-finding of its page-rank algorithm, which returns the most relevant web pages in a search, in part based on those most viewed. But novel solutions emerge from collaboration along the paths less traveled. Generative AI is the direct result of the pioneering academic research of AI godfathers Geoff Hinton, Yoshua Bengio, and Yann LeCun, who persisted for years in their very unpopular belief that neural networks held promise. Their breakthroughs eventually proved they were right and launched an AI revolution from their very human collaboration.

Expanding supply chain’s efficient frontier

Supply chains are challenged to improve customer experience, manage cost, and optimize inventory. The promise of AI is expanding the efficient frontier of achieving these goals, to accelerate the pace and quality of decisions, as another supply chain leader described in the forum I attended this week. Crunching 35 billion sales records to forecast 25 items or optimizing a supply plan with 25 million variables and 10 million constraints are feats AI makes possible today, in minutes. This remarkable achievement is clearly outside the human frontier and until recently crashed even the fastest computers. Investing in these kinds of AI capabilities is already reaping rewards for the leaders who do so.

Humanity in the age of AI

But as we invest in AI we must also invest in our people. AI is a tool in human hands. At the beginning of any symphony performance the orchestra tunes their instruments. The most masterful violinist is only one sound on her own. Even a perfect demand forecast is only an input into a decision, which itself must be connected to the entire supply chain to make an impact. Optimizing one link in the chain doesn’t optimize the entire chain, no more than tuning one violin tunes the entire orchestra. Expanding that efficient frontier is about each orchestra member mastering her own skills but also the entire orchestra playing in harmony. The leader makes that happen, as Benjamin Zander, conductor of the Boston Philharmonic said in a TED talk, “The conductor of an orchestra doesn’t make a sound. He depends, for his power, on his ability to make other people powerful.”

Lincoln understood the need to make people powerful. On the brink of the Civil War, the nation was even more divided then than it is today, so Lincoln appointed a key rival to his cabinet, William Henry Seward, and asked for his help with the speech, which he felt was his best last-ditch effort to avoid war. Seward encouraged the president to call for unity and gave him the foundation for many of the final lines, including the angelic reference. But it was Lincoln who refined Seward’s suggestion and gifted us with that most-famous line invoking the “better angels of our nature.”

The next inaugural address by a US president will undoubtedly be written with the help of generative AI, unlike Lincoln’s famous speech. Yet mellifluous words alone are not enough. The context, collaboration and conscience behind that famous line is even more remarkable and more human. Navigating the jagged technological frontier means leveraging AI when and where we can but not surrendering the power of our humanity. Orchestrating the combination is what is needed to achieve the best harmony.

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.

AI shows extreme promise for software and product development, manufacturing operations, and engineering and design. Industrial AI, a subset of the broader field of artificial intelligence (AI), refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, such as sensors, machinery, industrial engineers, and frontline workers.

Finding the right AI based solutions for the specific and often challenging requirements of manufacturing and industrial applications can be a challenge. AI isn’t a product that you buy, but instead exists within myriad applications, development frameworks, and solutions all designed to accomplish specific functions. Leading industrial organizations align AI initiatives with broader business objectives, ensuring maximum value from technology investments. ARC analysts recently discussed the far-ranging impact of industrial AI with Larsen & Toubro Technology Services (LTTS), who has turned their expertise in software and engineering into a comprehensive suite of AI-based offerings specific to a range of use cases and applications.

Evolving Industrial AI’s Role in Digital Transformation

Industrial AI, a subset of the broader field of artificial intelligence (AI), refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, such as sensors, machinery, industrial engineers, and frontline workers.

Focusing AI on Business Results

In industrial organizations, achieving desired business outcomes requires a comprehensive approach that encompasses the essential triad of people, processes, and technology. This framework is not just a buzz phrase, but a proven strategic blueprint that guides organizations toward sustainable success. The convergence of AI with other technologies, such as Internet of Things (IoT) and edge computing, opens new possibilities for distributed and embedded AI systems that can operate at the edge of the network, closer to where data is generated.

Industrial leaders are identifying areas where AI can make a significant impact, such as generative design of sustainable products, production processes, and services, predictive maintenance, supply chain optimization, and quality control.

Use Cases for Industrial AI

Industrial AI solutions and applications are popping up everywhere, but their ability to address specific problems for industrial end users can sometimes be elusive. Many different classes of competitors are entering the industrial AI market, from the large automation suppliers to the hyperscalers to smaller niche suppliers.

The purpose of Industrial AI is multifold. It aims to enhance operational efficiency by automating repetitive tasks, improve accuracy by reducing human error, and enable real-time decision making based on data-driven insights. From generative design of products and production processes to intelligent production operations maintenance and quality control, to energy and supply chain optimization, efficient sales and enhanced customer service, Industrial AI finds its applications across a wide spectrum of industrial operations.

Industrial AI offers several benefits. It can significantly reduce operational costs by optimizing resource usage and improving process efficiency. By enabling predictive maintenance, it can minimize downtime and extend the lifespan of machinery. With its real-time decision making capabilities, it allows for rapid response to changes in market demand or operational conditions, addressing organizational skills gaps, and enhancing agility and competitiveness.

In the realm of product and process design, AI-powered software enables more accurate and efficient design methodologies. Advanced AI algorithms can analyze vast amounts of data to predict optimal design parameters, reducing trial-and-error and speeding up time-to-market. Additionally, AI can assist in simulating and testing product designs under a variety of conditions, ensuring robustness and reliability.

Production operations and maintenance have been significantly enhanced by AI. Predictive maintenance, powered by Machine Learning algorithms, can anticipate equipment failures before they occur, reducing downtime and maintenance costs. AI can also optimize factory operations by streamlining workflows, improving resource allocation, and enhancing quality control.

The logistics of supply chain management are becoming increasingly complex in our globalized economy. AI comes into play here by adding predictive analytics for demand forecasting and inventory management to real-time visibility. It can also optimize routing and scheduling for transportation, leading to reduced costs and improved customer service.

The LTTS Approach to Industrial AI

As a large engineering service provider with considerable strength in software development, LTTS is uniquely positioned to provide value-added solutions to the world of industrial AI. LTTS’ knowledge of specific processes, plants, and facilities can add significant value to solutions. LTTS views AI as one set of enabling technologies, along with IIoT, digital twins, simulation, and other technologies, which can provide value both in the engineering realm, the supply chain, and the domain of manufacturing.

A Business-Driven Approach to Industrial AI

LTTS aligns its service offerings with the business needs of its customers, rather than focusing on technology alone. By understanding the pain points and cost drivers of different industries, LTTS can provide tailored AI solutions that address specific problems and deliver desired outcomes. LTTS has demonstrated the value of AI in improving operational efficiency, reducing costs, enhancing quality, and increasing customer satisfaction.

Industrial AI as an Application Layer

LTTS leverages its expertise in engineering and software development to create AI-powered applications that turn data into knowledge. By applying machine learning, deep learning, neural networks, and other AI techniques, LTTS can build intelligent systems that can analyze data, generate insights, and automate tasks.

Industrial AI as a Partner Ecosystem

LTTS collaborates with technology providers such as Qualcomm, Nvidia, Google, AWS, and Intel to deliver innovative and customized AI solutions. LTTS also works with customers to co-create and co-innovate AI applications that suit their specific needs and goals.

Augmenting Worker Capabilities with Industrial AI

LTTS uses AI to enhance the skills and productivity of the workforce, rather than replacing them. By providing AI tools and training to the frontline workers, LTTS can empower them to make better decisions, improve their efficiency, and reduce human errors. LTTS has plans for training 2,000 Engineers in Gen-AI using technology from Nvidia.

AiKno Metadata Extraction

One common factor amongst all industrial companies is the massive amount of paperwork & manually entered data from the past. The LTTS Cognitive Meta Data Extraction Module automates the tedious task of digitalizing physical data, thereby improving efficiency by 92-95 percent and reducing the time by 85 percent.

What makes the solution unique is its ability to extract metadata from complex engineering documents such as 2D drawings, legacy documents, and scanned images. The OCR is trained in engineering symbols such as GD&T symbols which are heavily used in the industry. The continuous self-learning system can do auto corrections and drive semantics-based rules on human feedback without any need for re-engineering. As a case in point, AiKno helped optimize the PO/RO processing for a transportation major. The manual digitalization of 1,000 documents would take the team 233 hours. With AiKno, this time was reduced by 60-80 percent to just 42 hours.

Predictive Analytics

The LTTS AiKno predictive analysis framework offers real-time insights into equipment health and identifies anomalies or failures well before they actually occur. Using built-in AI/ML models, service requests are automatically initiated, or machines run self-diagnosis programs to resolve issues. The LTTS AI framework can automatically preprocess and run different ML algorithms and evaluate the models on the basis of key metrics. It can automatically choose the best possible model and lower the manual effort of creating the models.

Using AI to Create an Asset Health Framework for a Global Food and Beverage Company

LTTS is actively implementing AI-based projects today with measurable results. The company recently implemented a predictive asset health framework for a major global beverage company to address primary asset performance management related issues such as change management and a lack of consistency in deploying solutions across multiple plants. Unplanned downtime is a significant source of loss for this industry, particularly in areas such as bottling and filling lines.

The end user wanted to transition from a preventive maintenance philosophy to a predictive one to improve uptime and OEE. The user wanted a solution that could be deployed across multiple plants in the US. LTTS provided an AI-based predictive maintenance solution across 14 critical assets in one plant, in areas such as the blow molder, fillers and cappers, labelers, packers, and ammonia chillers. The solution featured over 200 sensors with 34 edge gateways, providing real-time health monitoring of over 100 asset components. LTTS deployed analytics at the edge, enabling early fault detection combined with reduced latency and data congestion. The edge analytics were deployed using pre-built models along with custom machine learning for greater accuracy. The impact on the business was substantial; availability of the production lines improved significantly, with over 17 hours of unplanned downtime avoided in an 8-month period, with potential savings of around $300,000.

Conclusions

Understanding AI’s potential implications and benefits, and the ability to effectively wield it, is crucial for industrial organizations. Artificial Intelligence can be a daunting task, but with the right strategies and approaches manufacturers can increase their odds of successfully implementing AI into their operations. Software providers have a lot of technical expertise, but in many cases, they focus on technology but have comparatively smaller services and solutions businesses that can both implement and maintain a solution, which is really the key to obtaining business value. Finding solution providers with the right experience implementing these solutions in specific use cases can be a challenge.

LTTS is unique among many of the companies providing AI solutions for industrial applications because of both their depth in engineering expertise and software development. The company’s engineering expertise gives unique capabilities in accessing engineering data, and LTTS has considerable expertise in digitizing data and turning it into useful information that can be ingested by AI models to create an effective solution.

UPS dominated business and trade headlines for a day in late January when the world’s largest package delivery company announced plans to cut 12,000 full- and part-time management jobs as part of a new initiative called “Fit to Serve.”

What stood out even more was the “subhead” of UPS’ announcement. As reported by The Wall Street Journal, “Those jobs weren’t likely to return even when business picks up,” UPS said, adding that the reductions are part of what would be “new ways of working.” (Emphasis mine.)

Meanwhile SAP jumped to an all-time high based on a bullish forecast of double-digit growth in cloud revenue. But the good news came with a price: the company said it will spend roughly $2B restructuring “8,000 jobs to either retrain employees with AI skills or to replace them through voluntary redundancy programs.” Google, Microsoft and others have announced similar moves.

Allow me to translate: “New ways of working” and “retraining” = doing more with less.

In other words, boosting productivity has rapidly emerged as the overarching mandate in business going forward — and AI is widely seen as the rocket fuel for launching productivity into the stratosphere.

Make no mistake: AI is indeed a once-in-a-generation technological paradigm shift and a transformative force for business reinvention. But AI is no panacea. The companies who will achieve the biggest gains in productivity will implement AI through the dual lenses of business process reengineering and change management.

Redesign the process, then use IT

I’ll give you a recent example from my business, which enables real-time supply chain visibility, with AI-powered predictive insights and analytics, for the world’s largest shippers and their partners.

I was speaking with the Chief Supply Chain Officer at one of the world’s largest CPG companies. One minute, he conveyed how productive his workforce is, but a moment later, I was scratching my head when he told me that their biggest issue going forward is “ironing out scheduling issues.”

What?

It took some digging to understand that despite the substantial productivity gains we’ve enabled to-date, further gains would require additional business process reengineering. As it turns out, there are a certain number of “scheduling use cases” that require a warehouse manager to leave our system and work within another vendor’s app. That’s a drag on time and resources. To address these additional use cases, you have to deeply analyze the workflows between business functions and systems, identify the gaps, redesign the process — and then use IT (and AI) to integrate the systems and automate the process.

Opportunities abound — but big gains depend upon sound change management

AI presents nearly limitless opportunities to boost the productivity of supply chain professionals across every industry, every team and every need. Take onboarding and training new employees. With tools like GenAI, new workers can get real-time answers to questions and highly-personalized training. It’s easy to imagine a faster and more efficient path to productive work.

Or consider the potential for radically more user-friendly interfaces to enterprise applications. Even today’s most simplified “single-pane-of-glass” enterprise applications require that workers are well-versed in the system’s features and functions and trained to get the data and insights they are looking for. Tomorrow’s apps promise an interface that’s as clean and simple as a Google search bar. Users will simply pose their questions and the system will do all of the hard work “under the hood” to return workflow-specific insights and recommended actions.

The race is on to harness AI to achieve quantum leaps in productivity — but success also hinges on investing time, energy and resources in change management. Without a robust commitment to managing these profound changes in how people work, technology initiatives will fall flat if not fail entirely.

So what are some of the fundamental characteristics of sound change management?

Executive sponsorship at the C-level is a pre-requisite to any significant change initiative — in part because real change invariably cuts across multiple internal organizations, leaders and teams. And if one team doesn’t cooperate or fully embrace the change, everything breaks down (not unlike supply chains during a disruption).

Similarly, creating a cross-functional governance structure is key, with representatives from every impacted department playing an active role in the rollout of new initiatives.

Metrics are critical as well. But be wary of setting “moonshot goals” that may take years to achieve. Set milestones that can be achieved in two weeks, a month, a quarter — and then go from there.

Lastly, company leaders and managers must have the courage and fortitude to accept short-term disruptions. Often, true transformation might mean that your pace temporarily slows as the organization adjusts and refines to its new methods. But this is a strategic deceleration, one that promises greater speed and accuracy in the long run as you build a more robust, resilient, and productive operation.

Real change requires more than deploying the latest technology, however powerful. It takes an organization-wide commitment, resources and time — with great care taken to redesign the old ways of doing things, and to bring the workforce along for the journey.

Matt Elenjickal is the Founder and Chief Executive Officer of FourKites. He founded FourKites in 2014 after recognizing pain points in the logistics industry and designing elegant and effective systems to address them. Prior to founding FourKites, Matt spent 7 years in the enterprise software space working for market leaders such as Oracle Corp and i2 Technologies/JDA Software Group. Matt has led high-impact teams that implemented logistics strategies and systems at P&G, Nestle, Kraft, Anheuser-Busch Inbev, Tyco, Argos and Nokia across North America, Western Europe and Latin America. Matt is passionate about logistics and supply chain management and has a keen sense for how technology can disrupt traditional silo-based planning and execution. Matt holds a BS in Mechanical Engineering from College of Engineering, Guindy, an MS in Industrial Engineering and Management Science from Northwestern University, and an MBA from Northwestern’s Kellogg School of Management. He lives in Chicago.

As a child, I was mesmerized by beauty pageants, especially Miss World, where women from all around the world strutted in gorgeous fashion. I vividly remember the moments when the top winners declared their lofty goals for humanity: solving world hunger!

Since the emergence of AI as the shiny object of our times, we see similar grandiosity: that AI will solve world hunger. Consequently, everyone asserts they are utilizing AI, and those in the supply chain world are no exception. However, the question remains: how? How will it address “the world hunger problem” in supply chains, particularly in the context of supply chain planning? How can AI contribute to end-to-end decision automation?

Here, let’s explore 6 essential elements of AI-powered automation in supply chain planning & analytics, culminating in a powerful solution.

1) Streamlined Data Flow and Process Automation Is all about AI

At the heart of effective supply chain automation lies the seamless flow of data across various sources and digital platforms, akin to a well-constructed highway for data. This ensures the secure, high-capacity, and bi-directional transfer of essential information such as master data on products, customers, production-distribution infrastructure, transactional data on sales, inventory status and position, transportation execution data, external data e.g. competitor pricing, weather, recommendations, action triggers.

Process automation is pivotal in providing end-to-end visibility across the supply chain. It gives planners and managers a holistic workflow view, offering insights within their operational domain and broader supply chain processes. This comprehensive view enables more informed decision-making and enhances the ability to adapt to changing conditions.

In a world characterized by dynamic supply chains, plan automation means dynamic planning and re-planning of executable steps based on real-time data and alterations in upstream and downstream processes. For instance, adjustments in order volumes trigger immediate updates to demand, inventory, supply, production, and transportation plans.

2) AI-Infused Data Quality Assurance

Ok, we built the proverbial highway. Now, we must ensure cars continuously run on clean and safe roads. In tech speak, this means effective AI-driven decision-making based on high-quality and internally consistent data. Consequently, AI-based data diagnostics capabilities have been developed to maintain data quality continuously. How?

AI validates Master Data and maintains internal consistency with transactions such as:

• Checking and identifying unreasonable records in Master Data (e.g., Incremental Order Quantity > Minimum Order Quantity, delivery cycle > shelf life and transactions (e.g., outliers, product with active sales but no forecast, sales in an inactive product or customer).
• Cross-checking consistency between Master Data and transactional data
• Replacing missing or unreasonable records

AI techniques, particularly Machine Learning (ML), are pivotal in enhancing decision-making at every stage of the supply chain. This results in more accurate recommendations and efficient process automation.

These advancements are reflected in impressive User Acceptance Rates (UARs), leading to planner efficiencies that are significantly higher than industry norms.

3) AI in Forecasting

One of the critical areas where AI shines is forecasting. AI techniques, particularly ML, enable organizations to:

• Identify demand drivers in historical data
• Quantify the impact of demand drivers such as promotions, stockouts, competitor pricing, weather, or special events, improving demand forecasting accuracy
• Identify specific patterns in historical demand data and match them with the best forecasting algorithms
• Compete with other common statistical forecasting techniques, enhancing the precision of future predictions.

4) AI in Inventory Management

AI also plays a crucial role in optimizing inventory management by:

• Automatically segmenting products into groups based on sales volume, unit price, total profit, and COV (Coefficient of Variation) to establish service level and investment targets.
• Tracking the sales behavior of each product throughout its life cycle and adjusting group membership based on changes in demand patterns.
• Supporting optimal strategies for inventory investment and allocation to support your sustainability goals.

5) AI in Fulfillment and Transportation/ Network Design

Effective fulfillment and transportation planning, and Network Design are vital for any supply chain operation. AI techniques help by:

• Efficiently matching available inventory to open orders, considering dynamic rules and priorities, whether based on channels or customer preferences.
• Optimizing transportation routes and modes based on real-time data and factors such as cost, capacity, and delivery times.
• Supporting what-if scenarios by estimating transportation rates on lanes (i.e., origin location, transportation mode, required service, destination location) for which you do not have commercial rate data.
• Evaluating multiple strategy scenarios in multiple dimensions to determine whether the differences among them are significant (i.e., the recommended strategy is within the error bounds of the inputs)

6) AI supports organizational learning by creating a corporate memory within an organization:

AI tracks every input, output, and change in data at a high level of fidelity. For instance, you can see the full history of imports, exports, edits at the userID level, stage in the workflow, time of transaction, and reason codes for changes.  AI algorithms process this vast amount of data for root-cause analyses and alerts, for example, lower UARs than expected, high forecast bias, and low forecast value-add.

These insights result in input data corrections, algorithm enhancements, process improvements, and KPI target changes. AI effectively facilitates continuous improvement in supply chains!

Ah, if I ever make it to a beauty pageant, that will be my declaration.

Nilufer Durak is the Chief Operating Officer, Head of Customer Success at Solvoyo. Nil is a highly motivated technology executive, passionate about implementing Solvoyo’s bold autonomous supply chain vision with clients. With over two decades of experience in Corporate America, Nil has developed a deep understanding of customer success and operational excellence. She is best known for her boundless energy and ability to get things done. Currently, she is COO and Head of Customer Success at Solvoyo, a leading supply chain planning and analytics SaaS company based in Boston. Nil has also been an active member of women’s professional networking groups, advocating for women’s leadership roles in the technology and entrepreneurship fields.