The term “control tower” has been grossly overused in the domain of supply chain management over the years and is used today to describe practically anything from basic visibility to network-wide fully autonomous solutions. This makes it nearly impossible for anyone looking for a capable solution to effectively compare options side by side.
Digital transformation doesn't start with software. It doesn't start with AI. It starts with understanding your processes, your data, and your business as it exists today. That is where meaningful transformation begins.
Stage presentation from the 2026 Generis American Manufacturing Summit, where Ashok Erramilli and Peter Nilsson discuss 5 myths about planning and execution. Modern APS systems use advanced optimization algorithms to create efficient plans and production schedules. Changes in supply chain conditions, demand, and manufacturing constraints often occur between planning and execution. As a result, optimized plans can quickly lose relevance or become difficult to execute on the shop floor. Aligning optimized planning with real-world execution has long been a key goal of S&OP and S&OE initiatives. Many organizations struggle to achieve this alignment due to fragmented data, limited execution visibility, and static planning assumptions. A unified, high-resolution data foundation can help reconcile planning decisions with execution realities. Advanced optimization techniques can support continuous adjustment of plans and schedules as conditions change
It is not a secret that Artificial Intelligence (AI) is now starting to transform industries globally, and manufacturing is no exception. Mainstream everyday adoption, however, will most probably come from Agentic AI, more commonly referred to as AI Assistants or Agents. But before we go into how AI is transforming the business world, let’s keep in mind that the technology will likely not solve all manufacturing challenges and that not all AI Agents are created equal. There are mainly two kinds of AI Agents. One is based on the AI we all started talking about a few years ago, which is more of Machine Learning (ML) and is based on the statistical relationships between numbers. The AI that is more popular these days is Generative AI, or just GenAI for short, which is the statistical relationships between words. In manufacturing, Machine Learning is being used heavily to identify patterns and optimize processes from numerical data, such as production rates, defect rates, and performance data. This kind of AI plays a crucial role in refining the level of operational effectiveness, pinpointing the underlying causes of production problems, and enhancing overall product quality. GenAI is the opposite, however, and is all about interpreting and generating human language so that machines can comprehend questions, provide insight, and interact in a more intuitive, human-friendly manner. In manufacturing, the ability can be harnessed to simplify the reporting of data, aid operators in instructions, or even handle customer service queries more conversational in nature. Empowering the Workforce This is where the Augmented Connected Worker (ACW) concept comes in, a digitally empowered front-line worker using AI-powered tools to enhance decision-making, reduce errors, and enhance productivity. With the provision of real-time information, voice-enabled instructions, and perfect communication, ACW retains human workers at the center but with tremendous digital support. Take the semiconductor industry as an example, its production process involves hundreds of complex steps, and one small deviation can cause a drastic decline in product quality. Machine Learning has been a game-changer in this respect as it has enabled manufacturers to detect infinitesimal changes in the process and make real-time corrections to prevent defects and yield loss. GenAI, in turn, helps to reduce communication efficiency by enabling engineers and operators to ask systems for quick information or detailed reports, which is time-efficient and reduces errors. Another example of the ACW concept can be seen in the use of GenAI in production planning systems to narrow the gap between novice and expert users. Manufacturing planning and scheduling problems are complex, and sophisticated optimization methods are needed to solve them. While modern user-friendly interfaces hide much of the complexity, these systems can appear as “black boxes” to novice users, who may not understand the planning choices made. GenAI can provide natural language explanations to the decisions made by such systems, bridging the gap between mathematical sophistication and human understanding, and empowering novice users to work as effectively as experts. By enabling more intuitive interactions and demystifying system outputs, GenAI tools further strengthen the role of the Augmented Connected Worker, enhancing not only operational execution but also user confidence and autonomy on the shop floor. With both of these types of AI increasingly being employed in everyday manufacturing activities, they will complement each other to improve productivity, rationalize processes, and provide more efficient decision-making. The future of manufacturing lies in an environment where AI does not just support employees but positively assists them in maximizing both operational effectiveness and communication in general. Unlocking AI’s Full Potential Through Digitization However, to truly take full advantage of AI, businesses must first digitize their operations. AI is enabled where data is interrelated and connected, with businesses needing to go beyond isolated systems and processes. Real AI strength is derived from optimizing end-to-end processes, unifying planning, scheduling, and execution as a smooth digital ecosystem. With the establishment of these ‘improvement strings’ throughout the business, AI can provide more profound insights, not just by optimizing separate silos, but by enhancing the whole process flow. This approach unleashes better and more powerful decision-making, giving businesses an edge in a data-driven world. AI is only as powerful as the systems it connects to. Don’t let siloed data hold your business back. Start connecting your planning, scheduling
Similar to shifting gears to keep accelerating a car, a digital transformation strategy is necessary to keep up with today’s business environment. Although the theoretical strategies are easy to design, explicit steps are needed to change processes, avoid pitfalls, and ultimately execute an organizational gear shift. Before I go on, let me first remind automatic transmission drivers of what a gear shift entails. You take your foot off the gas, press the clutch, shift gears, and then release the clutch to get back on the gas, all in one synchronized motion. Inevitably, by disengaging the engine, a gear shift can momentarily slow you down if not executed well. In any case, like a gear shift, a digital transformation requires a thoughtfully executed orchestration of activities at the right moment in time to deliver the optimal outcome and avoid slowing you down if even temporarily. If your goal is to continue at the same pace, a gear shift might not be needed. However, if your goal is to accelerate, it becomes essential. Here’s my advice for your digital transformation strategy. Assess and Strategize: Assemble a tag team of cross-functional team members who can document current business processes. Despite having a system of record, many companies tolerate or aren’t aware of undocumented, offline processes, especially those that are woven into the fabric of the organization. It’s not uncommon to discover that individuals have created unofficial spreadsheet routines for key steps and manually transfer the output from the spreadsheet to the official system. Avoid this experience and devise a systems readiness review process to detect and document any such behavior. Choose and Integrate Technologies: Once you have defined your digital transformation goals, investigate how other organizations similar to yours have approached their transformation journeys. To get the transformation underway sooner, you can often bypass the more comprehensive RFI/RFP vendor selection process and pick 2 to 3 of the presumed best-fitting providers based on quick research and 3rd party validation. Center your selection process around a few key parameters, such as speed of delivery, industry-specific features offered, ability to integrate with your existing technology investments, alignment to your business needs, and cost. Automate and Standardize: Perform a gap analysis to identify the differences between old and new systems. Decide what you can give up and what you can’t live without if the new platform selected doesn’t exactly match your needs. We all think our business is unique and, therefore, want our digital transformation to fit that distinction rather than have our processes fit the transformation. However, many providers’ standard platforms are well thought out by teams of industry experts who offer deep experience in the sector. The better platforms have feature sets that can be toggled on and off and are, therefore, adaptable, flexible, and agile. Try to avoid customizations, as that may cause unintended version blocking and limit the ability to upgrade to successive releases without further modification. Manage Change and Scale: Train employees, foster a digital-first culture, and ensure scalability for future growth. Make it an organizational imperative for all users to let go of the old and start applying the new, so business processes transform to fit the latest system. Everyone tends to stick with what they know, but change is compulsory during a transformation. In addition to setting a dictum and ensuring it is followed, also make it clear to users that mistakes are expected and accepted as part of the necessary and inevitable learning and adoption process. Monitor, Measure, and Improve: Track KPIs, gather feedback, and continuously refine processes for ongoing optimization. The need to fast-track is often the result of deadline pressures because of competitors, launching big new initiatives, or unavoidable operational faux pas. Nevertheless, the transformative effects of simplification and standardization provide a quick way to get ahead of these challenges and open the aperture of opportunity. If you are not willing to commit to a gear shift, you won’t be able to accelerate your car past a certain point. That said, a gear shift doesn’t have to slow you down, like an automatic transmission; it’s all about how effectively it is executed. Highly skilled drivers will argue that a manual transmission offers more control and performance, allowing you to time shifts perfectly, such as avoiding upshifting before a hill or sharp curve. Success i
In its report Blockchain Beyond the Hype, the World Economic Forum offered some grounding advice: “For any organization, blockchain technology should not be a goal in itself, but a tool deployed to achieve specific purposes.” The overarching purpose of any supply-chain company boils down to delivering the best possible service at the lowest possible cost. Fully realizing that ideal in today’s market requires two resources: multi-enterprise business networks and digitization. What’s fueling blockchain mania isn’t a solid business plan backed by glowing use cases, but a near-mythic notion that the technology will solve all manner of supply chain challenges. Unfortunately, blockchain currently remains at odds with both multi-enterprise business network functionality and the process of digitizing physical goods. Let’s take a look at how.
