Demand and Supply Planning: From Responsive to Predictive to Demand-Sensitive

Reimagining Demand Planning for the Modern Supply Chain

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Whether you’re a medical technology supplier, a distributor of industrial apparel and accessories, an ecommerce startup or a high fashion retailer, supply chains, building supply responsiveness and network agility is no longer just a strategy but a competitive imperative. However, 40% of companies still rely on spreadsheets for demand planning, underscoring a disconnect between outdated practices and the need for agile, data-driven planning. Leading organizations recognize this gap and are incorporating technologies like robotics and autonomous transportation. On their own each adds massive value, but used by generative AI for tabular data, the time series information generated by these processes can be an exponential value driver for companies focused on customer satisfaction and efficiency.

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The Journey to Demand-Driven, Sustainable, Customer-Centric Supply Chains

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Modern supply chains have shifted towards demand-driven strategies, connected inventory, and integrated transportation and fulfillment. Industry pioneers like Nike, Amazon, and GAP invest heavily in demand sensing, real-time planning, and flexible fulfillment. Nike’s Triple Double initiative initiated nearly six years ago stresses speed, innovation, and customer connections and is still delivery results. Amazon Prime's value prop centers on same or 2-day delivery. GAP leverages cloud-based design collaboration for agile internal and external planning.

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The pandemic accelerated this shift away from cost-centricity, and as we are increasingly hearing in customer and conversations, four areas are emerging as target initiatives for moving demand-sensitive supply chains even closer to the order event.

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1. Robotics: AI is ushering in innovative applications ranging from autonomous 3D imaging by drones to warehouse robotics that replace repetitive, manual tasks and deliver exponentially faster pack and ship lead times.
2. Connected products: 79% of supply chain leaders polled by PWC in 2023 pointed to full or partial implementation of IOT in their supply chain operations.  Telemetry, usage and maintenance data seamlessly intersect as both demand sensing inputs and channels to engage customers at the point of use.  
3. Autonomous Transportation: The promise of meeting delivery SLAs is being revolutionized by autonomous vehicles and drones for reliable fulfillment. ArkInvest’s  Big Ideas research report sees autonomous transportation approaching 1-2 trillion annually by 2030. Ranging from fuel savings to providing near round the clock accessibility, autonomous transportation promises to deliver meaningful returns on capital.  
4. On-shoring and Near-shoring closer to demand: A Gartner/Capterra study reported last year that 88% of small to medium-sized enterprises have switched to suppliers closer to home, with 65% of SCM professionals saying economic inflation is a top concern going into 2023 and 43% citing the improvement in inventory closer to both production and customers.

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Generative AI for Time Series Connects the Dots

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With these advancements comes a whole new set of internal and external data with corresponding challenges of formats, aggregations and dimensions that existing forecasting and planning platforms are simply not built for. This is where Generative AI that is purpose built for time series and tabular data really shines. It uncovers connections and insights from historical and ongoing demand and supply chain data, including sources ranging from weather and road conditions to social sentiment, maintenance and replacement history. These data sets need to be integrated with the new time series data resulting from the exciting initiatives discussed above. The resulting compression of long-range planning and near-real time sensing together with sensitivity analysis and planning makes responsive supply chain mechanisms even more demand sensitive.  

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The Changing Role of Demand Planning

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Guided by generative AI, demand forecasting, planning and supply chain execution are transforming from detached, reactive functions into a 4-wheel drive vehicle for supply chains, capable of driving both the whole and making localized adjustments. By ingesting real-time signals beyond past shipments – like weather, retail foot traffic, social media trends – the demand plan adapts swiftly to emerging opportunities and risks. This breaks down the barriers between traditional forecasting and real-time monitoring, simulation, and AI-enabled prescriptive insights. This convergence of demand-sensing and forecasting powers continuous planning.

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Conclusion

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The journey from predictive to demand-sensitive is not just a trend but a necessity. Leading supply chain organizations recognize they must bring demand planning into the modern era, transitioning from intuition and spreadsheets to real-time internal and external data. However, technology alone is insufficient.  It’s essential to recognize that adopting these digital tools requires an organizational shift in mindset and processes. Change management is vital to integrate advancements seamlessly, enabling processes and people to evolve together.  The question is, where does your organization stand on the maturity spectrum? I will discuss that in an upcoming post.

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We invite our readers to share their insights, experiences, and thoughts on their transformation journey. Together, we can shape the future of an AI-enabled supply chain.

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See how Ikigai is helping organizations tackle their supply chain challenges with its innovative AI platform for tabular and time series data.

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