Stability has quietly exited the demand landscape—replaced by constant flux, fractured signals, and decisions that can no longer wait for certainty. In this environment, demand planning is being redefined in real time. No longer confined to forecasts and monthly cycles, it is emerging as a high-stakes, enterprise-wide discipline that directly shapes growth, agility, and competitive advantage. This cover story brings together incisive perspectives from industry leaders who are navigating this shift firsthand.
Through their insights, we examine how organizations are rebuilding planning frameworks for continuous uncertainty, embedding AI-driven sensing to respond at speed, and breaking down structural silos that limit true Integrated Business Planning. The discussion extends to sustainability, ecosystem-driven visibility, and the expanding strategic role of planners. As the horizon stretches toward autonomous, intelligence-led supply chains, the question is no longer how to predict demand—but how to continuously align decisions with it.
For decades, demand planning rested on a comfortable assumption: the future could be predicted by extending the past. That assumption no longer holds. Volatility today is not an exception—it is the operating environment. Geopolitical shocks, fractured trade routes, shifting consumer behavior, and shrinking product lifecycles have turned demand into a moving target. Signals are faster, noisier, and often contradictory. In response, demand planning is being forced out of its traditional confines—evolving from a forecasting function into a decision engine that directly shapes growth, agility, and resilience. And yet, a paradox persists.
Even as organizations accelerate investments in advanced planning systems and AI, outcomes remain uneven. As highlighted by Boston Consulting Group, while a vast majority of companies have deployed digital planning tools, only a select few are translating these into measurable gains in accuracy, responsiveness, or service levels. Technology, it turns out, is not the constraint—execution is. The shift underway is more fundamental than a tech upgrade. It is a transition from technology-led planning to decision-led planning.
Leading organizations are no longer focused on refining forecasts alone. Instead, they are redesigning how decisions are made—how quickly signals are interpreted, how trade-offs are evaluated, and how cross-functional alignment is achieved in real time. Without this shift, even the most advanced algorithms risk reinforcing fragmented processes and legacy inefficiencies.
What differentiates leaders is their ability to embed planning into the core of enterprise decision-making. This means integrating data, processes, governance, and talent into a unified operating model—one that is responsive, collaborative, and anchored in clear decision rights. It also means redefining the role of the planner. As AI takes over routine forecasting, planners are stepping into more strategic roles—managing exceptions, orchestrating trade-offs, and aligning stakeholders across sales, marketing, operations, and finance. The planner is no longer a forecaster, but a connector of insight and action.
The implication is unmistakable: demand planning is no longer a supply chain activity—it is an enterprise capability. As markets continue to shift, competitive advantage will not come from better predictions alone, but from better, faster decisions. The organizations that succeed will be those that build adaptive, decision-centric planning ecosystems—where technology amplifies human judgment, rather than attempting to replace it.
What does this transformation look like in practice? The following Q&A brings together perspectives from industry leaders, unpacking how demand planning is being rebuilt for a world defined by continuous uncertainty.
With demand volatility becoming a constant rather than an exception, how are organizations fundamentally redesigning their demand planning frameworks to operate in a state of continuous uncertainty?
Amrit Bajpai, Supply Chain Planning Leader – Global Supply Chain, Schneider Electric: Demand volatility is no longer an occasional disruption—it has become a defining characteristic of today’s operating environment. Structural shifts such as rapid electrification, expansion of digital infrastructure, evolving energy systems, and geopolitical realignments are reshaping demand patterns across industries. As a result, traditional planning approaches built around fixed forecasting cycles are increasingly inadequate. Organizations are therefore moving toward more adaptive and responsive planning frameworks. At Schneider Electric, demand planning increasingly integrates multiple signals—from market trends and customer demand patterns to supply constraints and supplier readiness—into a unified planning view. This allows the organization to evaluate demand and supply dynamics in parallel rather than sequentially.
Advanced digital planning platforms play an important role in enabling this shift. They allow teams to simulate multiple demand–supply scenarios and assess the implications for production, inventory, and distribution decisions. The objective is not simply to improve forecast accuracy but to ensure that the supply chain can consistently deliver on customer commitments while maintaining resilience and efficient working capital management.
Rayapati Srinath Reddy
Rayapati Srinath Reddy, Head – Supply Chain Planning, The HEINEKEN Company: In the alcobev industry, demand has never been perfectly predictable — we deal with seasonality, regional festivities, changing consumer preferences, and regulatory shifts all at once. But what has changed fundamentally in the last few years is the frequency and overlap of disruptions. It is no longer about planning for one shock at a time; it is about building a planning muscle that expects turbulence as the baseline.
We have moved away from rigid annual demand plans anchored to a single consensus number. Instead, our frameworks now operate on rolling horizons with scenario-based overlays. At any point, we are working with a base plan, but alongside it, we carry two or three plausible scenarios — each one stress-tested against variables like raw material availability, excise policy changes, route-to-market disruptions, or sudden shifts in channel mix.
The real redesign is not just in tools or models — it is in mindset. Planning teams are being trained to think probabilistically rather than deterministically. We are embedding range-based forecasting and flexible response triggers into our processes so that when a deviation occurs, the organisation does not scramble — it pivots. The goal is not to predict the future perfectly but to be prepared for several versions of it.
Arpita Srivastava
Arpita Srivastava, Supply Chain Planning & Logistics Leader, Schreiber Foods: Such a relevant question… totally agree that demand volatility is not something that happens once a blue moon. We are observing same almost every day in different forms. And the way to manage same is that organizations are redesigning demand to be more dynamic, responsive and iterative process rather than forecast-driven with once a month. The objective is to be able to sense and react to changing demand as quickly as possible rather than having a lower Mean Absolute Percentage Error (MAPE). Organizations are also strengthening cross-functional collaboration through Integrated Business Planning (IBP) where sales, marketing, supply chain, and finance review demand signals together and adjust plans quickly.
Advanced analytics and demand segmentation helps act in a way market demands. Instead of one model fits all, SKUs are classified based on demand patterns—stable, seasonal, or highly volatile—and planned differently.
For example, in an FMCG, demand for products can spike suddenly due to weather, promotions, or regional events. Instead of relying only on historical forecasts, organizations now combine real-time sales data, distributor inventory visibility, and weekly demand reviews. When a sudden demand surge occurs at any node, the team can quickly reallocate production, rework on dispatch priorities, and redistribute inventory across distribution hubs.
Neha Sorathia
Neha Sorathia, Sr. Principal Accenture – Strategy & Consulting, Accenture India: Volatility and disruption are now intrinsic to the planning landscape. Traditional planning models built around periodic forecasts and relatively stable demand patterns were designed for a very different operating context. Leading organizations are therefore redesigning demand planning around adaptability rather than point accuracy. This includes moving away from static monthly cycles toward more continuous sensing, frequent replanning, and faster decision loops. Scenario-based planning is becoming central to this shift. Instead of locking into one number, organizations are building dynamic frameworks that allow them to sense changes early and respond in a structured manner. Ultimately, the future demand planning framework is less about predicting the demand perfectly and more about building systems that can respond intelligently when reality diverges from the plan.
Sanjay Desai
Sanjay Desai, Independent Board Advisor / Mentor: Since the disruption caused by COVID, well-run organizations have realized that demand can no longer be expected to settle into predictable patterns. Instead, the priority today is agility — the ability to respond quickly to evolving customer needs. Rather than relying on a single monthly forecast, companies are increasingly updating demand plans more frequently, often continuously. Demand planning is also being repositioned as part of the broader commercial function, where the baseline forecast should originate. Organizations are therefore moving away from traditional weekly or monthly forecast cycles toward dynamic planning environments where forecasts are adjusted in real time, depending on the product category and market signals. The focus is gradually shifting from measuring periodic forecast accuracy to managing demand variability with agility and responsiveness.
How is demand planning evolving within your organization—from a forecasting function to a strategic capability that directly influences revenue growth, pricing agility, and market responsiveness?
Amrit Bajpai: Within Schneider Electric demand planning is closely integrated into our Integrated Business Planning (IBP) process. Demand signals feed directly into cross-functional discussions around capacity investments, supply prioritization, component allocation, and inventory positioning across global networks. This integration enables planning to influence key business decisions, supporting growth, faster responsiveness to market opportunities, and stronger service reliability for customers.
Rayapati Srinath Reddy: Historically, demand planning in alcobev sat squarely within the supply chain function — it was about generating a volume forecast, passing it downstream, and hoping production and logistics could deliver. That model is no longer sufficient, and frankly, it was never truly strategic. Within our organization, demand planning is now deeply connected to commercial strategy. When we launch a new variant, enter a new state, or adjust pricing in response to competitive action, the demand plan is not an afterthought — it is part of the decision-making table from the beginning. We actively model how promotional intensity, pricing elasticity, and distribution expansion will shape volume, and we use those insights to guide investment decisions.
This shift has turned demand planning into a revenue enabler. We can answer questions like: "If we increase our on-premise presence in a particular city by 20%, what should we expect in incremental volume — and do we have the supply architecture to support it?" That is a fundamentally different conversation from "What did we sell last quarter, and what should we expect next quarter?" The planning function now influences where we grow, how aggressively we invest, and how quickly we respond to market signals — not just how much we produce.
Varun Kakkar, Senior General Manager – Supply Chain at Birla Opus, Grasim Industries: Demand planning is no longer a back-end forecasting function—it has evolved into a strategic lever that directly influences business performance. Today, it plays a central role in CXO-level discussions, shaping decisions around revenue growth, pricing strategies, and market expansion. Within the organization, the demand planning function works closely with sales and marketing teams, contributing actively to market planning, seasonal strategies, and promotional effectiveness. This integration allows demand planners to not only anticipate demand but also actively shape it—through better alignment of supply with market opportunities, improved product mix decisions, and sharper pricing interventions. As a result, demand planning is increasingly being recognized as a value creator, driving both topline growth and margin enhancement while enabling faster and more informed responses to market dynamics.
Sanjay Desai: In many organizations, demand planning has moved out of the operational shadows and into the center of strategic business discussions. It is no longer just an input for supply planning; it now plays an active role in shaping revenue targets, promotional strategies, and pricing decisions. Demand planners today provide critical insights into product mix dynamics, shifts in channel performance, and the impact of promotional peaks. These insights help leadership teams determine which customers, products, or markets should be prioritized — and where resources should be scaled back. As a result, the role has evolved significantly. What was once primarily about processing historical data has transformed into a capability that supports commercial decision-making through advanced analytics, scenario planning, and forward-looking market insights.
AI-driven demand sensing is rapidly advancing. How close are enterprises to building planning environments that can automatically detect shifts in demand patterns and recalibrate decisions in near real time?
Amrit Bajpai: Many organizations are already taking meaningful steps toward real-time demand sensing by leveraging advanced analytics and artificial intelligence. These technologies are capable of identifying demand anomalies, analysing order patterns, and generating predictive insights from large and diverse data sets. However, most companies still operate within a human-supported planning model. While digital tools provide powerful insights, planners continue to play a critical role in interpreting the broader business context. Factors such as macroeconomic shifts, large infrastructure project cycles, regional market dynamics, and customer investment patterns often require human judgment to translate data signals into actionable decisions.
Over time, planning environments are expected to become increasingly closed-loop and digitally enabled. In such systems, technology will continuously sense changes in demand, assess supply constraints, and recommend adjustments across sourcing, production, and distribution networks. Human planners will increasingly focus on evaluating scenarios, managing exceptions, and collaborating across the ecosystem to ensure supply chains remain resilient and responsive.
Rayapati Srinath Reddy: The honest answer is that most organisations — including many in our industry — are still on a journey with AI-driven demand sensing. We are past the experimentation phase and into practical application, but we are not yet at the stage where decisions are being fully recalibrated autonomously in real time.
What we have been able to do meaningfully is layer machine learning models on top of our traditional statistical forecasts. These models ingest a wider set of signals — weather patterns, local event calendars, retail off-take trends, and even social media sentiment around certain categories — to sharpen short-term forecasts. In alcobev, where a single festival weekend or a state-level policy change can move volumes dramatically, this kind of signal detection has proven genuinely valuable.
The gap, however, is in the "last mile" of decision-making. Sensing a demand shift is one thing; automatically triggering a supply response — adjusting production schedules, redirecting inventory, modifying distribution plans — requires a level of system integration and organisational trust in algorithms that most companies are still building. I would say we are perhaps 60 to 70% of the way there on the sensing side, but only about 30 to 40% on the autonomous response side. The next few years will be about closing that gap — and it will require as much change management as it does technology investment.
Varun Kakkar: AI-driven demand sensing capabilities have made significant progress in recent years. Modern tools are now capable of identifying subtle shifts in sales momentum, detecting emerging patterns, and generating actionable insights with far greater speed and accuracy than traditional systems. These capabilities enable supply chain teams to respond more proactively—optimizing inventory, improving service levels, and reducing stock imbalances. However, while the technology has matured considerably in terms of sensing and analytics, fully autonomous decision-making is still evolving. At present, AI serves as a powerful decision-support engine rather than a complete decision-maker. It augments human judgment, allowing planners to make faster, more informed decisions. Over time, as confidence in models and data integrity improves, we can expect a gradual transition toward more automated, self-correcting planning systems.
Neha Sorathia: AI models today can detect subtle shifts in demand patterns much earlier than traditional statistical methods, especially when they incorporate diverse signals such as point-of-sale data, promotions, macroeconomic indicators, and digital behavior. That said, the limiting factor is rarely the model itself. The real challenge is organizational readiness to act on those insights. Many companies now have advanced sensing capabilities, particularly in more mature industries. Translating those signals into coordinated decisions across supply, manufacturing, and distribution still requires strong governance, clear decision thresholds, and tight integration across planning systems. Until those foundations are in place, fully autonomous adjustments remain aspirational rather than practical.
Sanjay Desai: Most organizations are currently somewhere in the middle of this journey. Modern planning systems can already analyse large datasets — including POS data, customer orders, and e-commerce signals — identifying patterns far more quickly than traditional approaches. Leading organizations with mature supply chain capabilities are increasingly integrating data science and advanced analytics into their planning processes to enhance efficiency and improve decision quality. In the near future, we may even see the traditional title of “demand planner” evolve into something closer to “demand data scientist.” Near real-time updates will become the norm once organizations establish clear governance rules that define when systems can act autonomously and when human intervention is required.
Despite widespread adoption of Integrated Business Planning (IBP), many organizations still struggle with fragmented decision-making. What leadership or structural shifts are required to make truly synchronized, enterprise-wide planning a reality?
Amrit Bajpai: Achieving synchronized planning requires more than the adoption of advanced technology platforms. While digital tools provide the necessary visibility and analytics, the real transformation lies in organizational alignment and decision ownership. One of the most important structural shifts is the move toward end-to-end accountability for planning decisions. Rather than operating in functional silos, demand, supply, procurement, and logistics teams must work within a unified framework where decisions are made with a holistic understanding of the entire value chain. This requires breaking down traditional barriers between commercial and supply chain teams.
Equally important is the alignment of performance metrics. When sales, operations, and supply chain teams are evaluated against shared business outcomes—such as service levels, inventory health, and responsiveness to market demand—it naturally drives collaboration and better decision-making.
Governance also plays a critical role. Integrated Business Planning (IBP) processes provide the structured forum where cross-functional leaders can review demand signals, evaluate supply capabilities, and make coordinated decisions. When these structural elements come together—clear ownership, shared metrics, and disciplined governance—planning becomes deeply embedded in how the business operates rather than functioning as a standalone forecasting activity.
Rayapati Srinath Reddy: This is a question that resonates deeply because I have seen firsthand how IBP can look excellent on paper yet deliver underwhelming results in practice. The process exists, the meetings happen, the dashboards are built — but too often, the decisions made in an IBP forum are revisited or overridden outside of it.
The root cause is rarely the process design. It is almost always a combination of leadership alignment and organisational incentives. When the sales team is measured purely on top-line volume and the supply chain team is measured on cost efficiency, you will inevitably get competing priorities showing up in the same planning room. No amount of process sophistication can fix misaligned incentives.
The structural shift required is threefold. First, IBP needs genuine executive sponsorship — not just attendance, but ownership. The senior-most commercial and operations leaders need to treat it as the primary decision-making forum, not a reporting ritual. Second, organisations need to align KPIs across functions so that trade-offs are made transparently and collectively. In alcobev, for instance, the decision to prioritise a high-margin SKU over a high-volume one has implications for production, warehousing, distribution, and sales — and everyone needs to own that trade-off together. Third, and perhaps most importantly, companies need to invest in planning talent that can speak the language of both finance and operations — people who can translate a demand signal into a P&L impact and communicate it across functions.
Varun Kakkar: Achieving the full potential of Integrated Business Planning (IBP) requires more than just process implementation—it demands a fundamental organizational shift. The most critical enabler is strong top-down alignment, with leadership actively championing IBP as a core business discipline rather than a supply chain initiative. Equally important is the integration of finance into the planning process, ensuring that all decisions are aligned with financial goals and business priorities. Demand planning must act as the central anchor of the monthly planning cycle, connecting cross-functional inputs from sales, marketing, operations, and finance into a unified plan. Organizations that successfully embed IBP into their operating rhythm benefit from greater transparency, faster decision-making, and improved alignment across functions. Ultimately, synchronized planning is as much about governance and culture as it is about tools and processes.
Arpita Srivastava: Well, one thing I always say is that any technology alone cannot fix fragmented decision-making; leadership alignment does. Many IBP implementation/processes fail because functions still optimize their own targets—sales pushes volume, supply focuses on efficiency, finance prioritizes cost; each working in their own silo. True synchronization requires shared KPIs and executive ownership of the end-to-end plan. Leading organizations run a single monthly executive IBP meeting where commercial, supply chain, and finance align on one consensus plan. This alignment is critical when balancing production capacity between low margin but mandated products versus high-margin products. Structurally, organizations are moving toward cross-functional planning teams and central demand control towers that integrate data, decisions, and accountability across the enterprise.
Neha Sorathia: Integrated Business Planning is fundamentally an organizational transformation. While platforms can connect data, they cannot automatically align incentives or resolve cross-functional trade-offs. Many IBP initiatives struggle because commercial, operational, and financial teams continue to operate with different metrics, assumptions, and success criteria. For IBP to truly deliver value, three structural shifts are essential:
First, leadership alignment around enterprise-level outcomes rather than functional optimization, supported by the right KPIs.
Second, planning forums must evolve from review meetings into decision-making forums where trade-offs are explicitly resolved.
Third, companies must establish a single version of the truth, ensuring that all functions operate from the same data, assumptions, and scenarios.
When these elements come together, IBP becomes far more than a planning cadence; it becomes a mechanism for enterprise synchronization, enabling agility and organizational growth.
Sanjay Desai: It is useful to remember that Sales & Operations Planning (S&OP) emerged in the late 1980s, while Integrated Business Planning (IBP) began gaining traction in the early 2000s. Yet the fundamental challenge remains unchanged — clarity of decision ownership and the speed of execution. To make enterprise-wide planning truly effective, leadership must strengthen accountability across functions and align teams around shared outcomes. The era of siloed functional KPIs is gradually giving way to unified organizational goals supported by coordinated execution across verticals.
Structurally, this also means simplifying governance. Instead of multiple fragmented meetings, organizations are moving toward integrated decision forums where finance, sales, supply chain, and product teams jointly review trade-offs and align on execution priorities. Without disciplined execution and clear decision rights, even the most sophisticated IBP frameworks risk becoming procedural rather than impactful.
As sustainability becomes a board-level priority, how can demand planning play a measurable role in reducing overproduction, excess inventory, and the overall environmental footprint of supply chains?
Amrit Bajpai: Demand planning has a significant but often underappreciated role in advancing sustainability across supply chains. Improved demand visibility allows organizations to operate with greater precision, reducing inefficiencies that often translate into both financial and environmental costs. When demand signals are clearer and more reliable, companies can minimize overproduction, avoid excess inventory accumulation, and reduce the likelihood of product obsolescence. This also reduces the need for emergency shipments and expedited logistics, which tend to carry a higher carbon footprint.
At Schneider Electric, sustainability is not treated as a separate agenda but is integrated into operational decision-making. Demand planning helps strike the right balance between customer service levels, operational efficiency, and environmental responsibility. By enabling more accurate production and distribution planning, it contributes to reducing waste, lowering logistics emissions, and supporting the company’s broader ambition of building sustainable and lower-carbon supply chains.
Rayapati Srinath Reddy: Sustainability in the alcobev industry is a particularly interesting challenge because we are dealing with agricultural raw materials, glass and packaging waste, water-intensive processes, and complex cold chain requirements. Demand planning sits at a critical intersection here because the most direct lever it controls is the accuracy of what we produce and where we place it. Every unit of overproduction carries an environmental cost — wasted raw materials, unnecessary energy consumption, excess packaging, and in some cases, product that reaches the end of its shelf life and must be written off. Improving forecast accuracy by even a few percentage points translates directly into reduced waste across the value chain. This is still early-stage, but the intent is clear: demand planning should be able to quantify the sustainability trade-off of every significant planning decision, and that visibility will only improve as we embed these metrics into our planning tools and governance processes.
Varun Kakkar: Demand planning is uniquely positioned to drive sustainability outcomes, given its holistic view of business trends, future demand signals, and historical patterns. By improving forecast accuracy and aligning production more closely with actual demand, demand planners can significantly reduce overproduction, excess inventory, and associated waste. Beyond planning accuracy, demand insights can inform broader sustainability strategies—such as optimizing product portfolios, reducing slow-moving inventory, and supporting circular economy initiatives. At the same time, supply chain and manufacturing functions are advancing complementary efforts through green warehousing, sustainable logistics, eco-friendly packaging, and zero-discharge manufacturing facilities. Together, these initiatives position demand planning as a critical enabler of both operational efficiency and environmental responsibility, linking business performance with sustainability goals.
Arpita Srivastava: A very key impact demand planning brings from a sustainability point of view is by improved forecast accuracy and demand consolidation. Now suppose you need 100 units in a month; you can split it as 3-4 units every day to 100 units in one go. This split in line with market demand, production capacity and logistics planning can significantly impact waste we generate and energy we consume. No if this demand goes up or down, this will impact utilization of resources across the chain and hence increasing both losses and energy consumption. For short-shelf life products, overproduction directly leads to spoilage and disposal costs. By using better demand sensing and shorter planning cycles, companies can reduce excess inventory and avoid unnecessary production runs. The sustainability impact is measurable: lower waste, reduced energy use in manufacturing, and fewer emergency shipments. Essentially, every percentage improvement in forecast accuracy translates into less waste and a smaller carbon footprint. Demand planning thus is significant as by improving forecast accuracy and aligning production to real consumption we optimize our environmental footprint.
Neha Sorathia: Many sustainability inefficiencies like excess production, obsolete inventory originates upstream in planning decisions. When demand signals are misinterpreted or planning cycles are slow to react, organizations end up producing more than the market ultimately absorbs. Improving demand planning therefore directly reduces waste, overproduction, and carbon-intensive logistics adjustments. Increasingly, organizations are embedding sustainability metrics into planning decisions, evaluating scenarios not only on cost and service but also on environmental impact. This is particularly relevant in industries like retail, where long planning lead times and high obsolescence amplify sustainability risks.
Sanjay Desai: Effective demand planning is one of the most practical ways to reduce waste across the supply chain. When demand signals become more accurate, organizations can significantly reduce emergency production runs, product expiries, and unnecessary transportation. In the future, demand planners — or perhaps demand data scientists — will increasingly evaluate forecast performance not just against accuracy metrics, but also against waste reduction, markdowns, and avoidable CO? emissions. Over time, sustainability metrics will naturally converge with cost and efficiency metrics. When better planning simultaneously improves financial performance and reduces environmental impact, sustainability becomes embedded within everyday operational decisions and aligns seamlessly with broader ESG governance frameworks.
Demand visibility increasingly depends on ecosystem collaboration. How are you integrating distributors, channel partners, and downstream data streams into your planning architecture to create a more accurate and responsive demand picture?
Amrit Bajpai: The traditional model of demand planning—where forecasts were generated largely from internal sales data—is increasingly giving way to a more collaborative ecosystem approach. Today, a significant portion of demand intelligence originates outside the organization. Distributors, channel partners, system integrators, and suppliers all possess valuable signals about market activity. By integrating these inputs into planning systems, organizations can develop a far more accurate view of real demand patterns.
Digital collaboration platforms are playing an important role in enabling this integration. They provide visibility into channel inventory levels, project pipelines, order trends, and supplier commitments. This shared transparency allows companies to detect demand shifts earlier, anticipate potential supply constraints, and respond faster to market changes. As supply chains become more interconnected, the ability to incorporate ecosystem insights into planning processes will become a key differentiator for companies seeking to improve responsiveness and resilience.
Rayapati Srinath Reddy: In alcobev, the route to market is complex. We operate through a layered distribution network that often includes state-level regulations, multiple tiers of distributors, and a mix of on-premise and off-premise channels. Getting a true picture of end-consumer demand from behind this distribution wall has always been one of our biggest planning challenges.
We have made meaningful progress by investing in distributor management systems that capture secondary and, in some markets, tertiary sales data in near real time. This has been a game-changer for understanding what is actually moving off shelves versus what is simply being pushed into the trade pipeline. When we combine this with point-of-sale data from modern trade partners and our own direct-to-consumer channels, we start to see a much richer demand picture.
The harder part is making this data actionable within the planning cycle. Raw data from distributors is often noisy, inconsistent, and arrives at different frequencies. We have invested in data harmonisation layers that clean and standardise these feeds before they enter our planning models. We are also working on collaborative forecasting pilots with key distribution partners — sharing our demand outlook with them and getting their on-the-ground intelligence in return. It is not yet a seamless two-way data flow, but the direction is clear. Ultimately, the organizations that win in demand visibility will be those that treat their ecosystem partners not as data sources to extract from but as planning collaborators to invest in. That requires technology, yes, but also trust, transparency, and shared incentives.
Varun Kakkar: Enhancing demand visibility requires a shift from enterprise-centric planning to ecosystem-driven intelligence. We are actively collaborating with distributors, dealers, contractors, and even end influencers such as painters to capture granular, real-time demand signals. Through the deployment of digital solutions such as track-and-trace systems, barcode scanning, and integrated data platforms, we are building a connected, data-driven planning ecosystem. These technologies enable seamless data flow across stakeholders, improving transparency and enabling more accurate demand forecasting. This integrated approach not only enhances responsiveness but also elevates demand planning into a strategic capability—one that is deeply embedded across the value chain and capable of driving more informed, end-to-end decision-making.
Sanjay Desai: True demand visibility extends well beyond the factory walls and depends heavily on collaboration across the supply chain ecosystem. Many companies are now integrating data from distributors, retailers, and digital marketplaces to create a more comprehensive view of demand. This represents a shift from an “inside-out” approach to an “outside-in” perspective where market signals drive planning decisions. Achieving this requires both technological and cultural change. Technically, organizations need shared data platforms, standardized interfaces, and a unified data architecture. Culturally, success depends on trust, long-term partnerships, and clear data governance frameworks that ensure all stakeholders see tangible value in sharing information.
Over the next three to five years, which investments in technology, talent, and planning governance will most strongly determine whether organizations achieve intelligent, future-ready demand planning maturity?
Amrit Bajpai: The maturity of demand planning capabilities will largely depend on three foundational investments: technology, talent, and governance. Technology forms the backbone of modern planning systems. Advanced planning platforms, digital control towers, and analytics tools provide real-time visibility across the value chain and allow organizations to model complex scenarios. These technologies help planners move from reactive decision-making to proactive supply chain management. However, technology alone is not sufficient. Talent plays an equally critical role. The next generation of planners must combine strong supply chain knowledge with analytical capabilities and business decision-making skills. They must be able to interpret data, evaluate trade-offs, and collaborate effectively across multiple functions.
Finally, governance ensures that these capabilities translate into effective action. Robust integrated planning processes—linking commercial, supply chain, procurement, and finance teams—create the discipline required to make aligned decisions. When these three elements work together, organizations can build planning capabilities that are both intelligent and adaptive.
Rayapati Srinath Reddy: If I had to prioritize the investments that will matter most over the next three to five years, I would group them into three buckets. On the technology front, the biggest unlock will come from investing in connected planning platforms that bring demand, supply, and financial planning into a single integrated environment. Most organisations, including ours, still operate with planning tools that are stitched together through spreadsheets and manual handoffs. Moving to a cloud-native planning ecosystem — with embedded analytics, scenario modelling, and real-time data ingestion — is foundational. Alongside this, selective investment in AI and machine learning for demand sensing and pattern recognition will continue to deliver incremental gains, particularly in short-term forecasting accuracy.
On the talent side, the demand planner of the future looks very different from the demand planner of the past. We need people who are comfortable with data science concepts, who can interpret algorithmic outputs critically, and who can communicate planning insights in business terms. This means investing in upskilling existing teams and recruiting from non-traditional talent pools — data analysts, business school graduates with a quantitative bent, and even people from outside the supply chain function who bring fresh perspectives.
On the governance front, the most underrated investment is in decision rights and planning discipline. Technology and talent mean little if the organisation does not have clear governance around how demand signals translate into supply decisions, who owns the trade-offs, and how performance is measured. Building a robust planning governance framework — with defined escalation paths, clear accountability, and regular performance reviews — is what separates companies that use planning tools from companies that are truly well-planned.
Varun Kakkar: Over the next five years, the organizations that succeed will be those that invest decisively across three critical pillars: technology, talent, and governance. On the technology front, there will be a strong shift toward AI-enabled, digital-first supply chains with end-to-end integration across planning and execution layers. However, technology alone is not sufficient—there is an equally pressing need to build talent that can effectively leverage these advanced tools. This includes upskilling existing teams and attracting new talent with expertise in analytics, AI, and data science. Finally, robust planning governance will be essential to ensure alignment, accountability, and consistency in decision-making. Organizations that successfully integrate these elements will be better positioned to achieve true demand planning maturity and unlock sustained competitive advantage.
Arpita Srivastava: Major areas I see that investments can determine and/or influence any organizations’ existence and growth would be having right technologies at right place and right people to work on those technologies with structured frameworks driving day-to-day operations. On the technology side, companies are investing in AI-driven planning platforms and digital supply chain twins to simulate disruptions. But technology alone won’t bring the change. The biggest shift will be in talent. There will be need for stronger data literacy, the ability to interpret AI-driven insights, and the commercial acumen to challenge sales assumptions. Most of the roles in any organizations are evolving into a data-savvy business strategist who can interpret analytics and influence commercial decisions, someone who can grow at the pace of technology introduction. So this will call for extensive training programs and upgrading the talent pool that any organization has. And at times, job reallocation depending on current skillset will be required to manage. Governance is equally important—it will determine whether insights translate into action. Organizations will need faster IBP cycles, clear decision rights, and shared KPIs across sales, finance, and supply chain.
Neha Sorathia: Future demand planning maturity will depend on investments across three interconnected dimensions.
First, technology platforms that can integrate diverse signals, support scenario simulation, and generate explainable AI-driven insights.
Second, talent transformation, where demand planners evolve into analytical decision partners who combine business understanding with technology fluency.
Third, planning governance, ensuring that organizations can act on insights quickly through clear decision rights and disciplined escalation mechanisms.
Organizations that invest only in technology will see limited impact. The real advantage will come from total enterprise reinvention by aligning technology, talent, and operating model into a cohesive planning ecosystem.
Sanjay Desai: Three areas will clearly differentiate leaders from followers: technology, talent, and governance. Technology investments will increasingly focus on building strong data foundations and platforms capable of integrating statistical models with machine learning capabilities. However, technology alone is not the solution. Talent will play an equally important role. Demand planners must develop stronger analytical capabilities and the confidence to challenge assumptions across sales, finance, and operations using data-driven insights and compelling business narratives. Finally, governance will be critical. Organizations need clearly defined decision-making frameworks, supported by senior leadership involvement when commercial priorities and system recommendations diverge.
Looking toward 2030 and beyond, how do you envision the demand planning function evolving in an era of increasingly autonomous, intelligence-led supply chains, and what structural shifts will define the next frontier of planning excellence?
Amrit Bajpai: By the end of this decade, demand planning is expected to evolve into a far more intelligence-driven capability powered by digital technologies. Advanced analytics, artificial intelligence, and machine learning will enable systems to detect shifts in demand patterns, assess supply constraints, and recommend optimal responses across sourcing, production, and distribution networks. Rather than focusing primarily on generating forecasts, digital platforms will increasingly act as decision-support engines that continuously monitor the supply chain environment and propose actionable scenarios.
In this future model, the role of human planners will also evolve. Instead of spending large amounts of time on manual data consolidation and forecast adjustments, planners will focus on evaluating scenarios, collaborating with ecosystem partners, and managing strategic exceptions. Their role will be to apply judgment, contextual understanding, and cross-functional coordination to ensure that supply chains remain resilient and responsive. Ultimately, the evolution of demand planning will reflect a broader shift in supply chain management—from static forecasting processes to dynamic, sensing networks capable of responding to market changes in near real time.
Rayapati Srinath Reddy: By 2030, I believe the demand planning function will look fundamentally different from what most of us recognise today. The core shift will be from planning as a periodic, human-driven process to planning as a continuous, intelligence-augmented capability that operates in the background of every business decision. In practical terms, this means demand plans will not be "created" in the traditional sense — they will be continuously generated, updated, and refined by intelligent systems that monitor an ever-expanding set of signals. The planner's role will shift from building forecasts to governing and curating the intelligence that produces them, making judgment calls on exceptions, and ensuring that the algorithms are aligned with business strategy.
In the alcobev industry specifically, I expect we will see much tighter integration between demand planning and consumer insights. As direct-to-consumer channels grow and data from digital commerce, loyalty programmes, and social listening becomes richer, the demand plan will increasingly reflect not just "how much" but "who, where, when, and why." This level of granularity will enable hyper-local planning — tailoring assortments, pack sizes, and promotional strategies at a city or even neighbourhood level. The biggest leadership challenge in all of this will be letting go of the illusion of control. The best demand planning functions of 2030 will not be the ones that produce the most accurate single-point forecasts. They will be the ones that are most adaptive, most resilient, and most comfortable operating in a world where certainty is the exception, not the rule.
Varun Kakkar: By 2030, demand planning is expected to transition into a largely autonomous, intelligence-driven function. Advances in AI and data analytics will enable zero-touch planning for a significant portion of standard product categories, where systems can independently sense demand, generate forecasts, and trigger execution decisions. Beyond automation, the role of demand planning will expand significantly in strategic importance. It will evolve into a key driver of revenue and profit maximization, offering actionable insights not just within the organization but also to customers, dealers, and channel partners.
By enabling better inventory optimization, reducing dead stock, and improving sell-through rates, demand planning will become a critical enabler of value creation across the ecosystem. The next frontier of planning excellence will be defined by this shift—from reactive forecasting to proactive, intelligence-led orchestration of demand and growth.
Arpita Srivastava: With the pace of technological advancements and GPTs being available, by 2030, demand planning will look very different from the spreadsheet-heavy process many organizations still run today. Most of historical data analysis and forecast generation itself will become automated. Real time visibility across the chain will be enhanced. AI systems will continuously read signals like retailer POS data, distributor inventory, weather trends, and even social media cues to update demand in near real time. At the ground level, this means planners won’t spend most of their time debating numbers. Instead, they’ll focus on exceptions and decisions. Say, if sales of a drink suddenly spike in a region due to weather change, the system may automatically recommend increasing production, reallocating stock from nearby hubs, or shifting promotions to bridge supply-demand gap. The planner’s role will be to validate these decisions and align them with commercial priorities.
Structurally, companies will move toward centralized planning control towers that bring together demand, supply, and financial planning in one place. Functional boundaries might cease to exist. I also foresee major changes in the talent pool having more GenZ professionals taking up mid-to senior roles. This will for sure impact and challenge the current ways of working as they are naturally data-driven and comfortable working with AI tools. They will expect real-time dashboards, automated insights, and faster decision cycles rather than static monthly reports. This generational shift will accelerate the adoption of more agile, tech-enabled planning cultures.
Neha Sorathia: Routine forecasting tasks will become increasingly automated as AI systems continuously monitor signals and adjust baseline projections. We will also see deeper convergence between demand, supply, and financial planning, supported by Agentic AI Agents that allow organizations to maximize for specific outcomes within a designed decision framework. In this future environment, the defining capability will not be the ability to predict demand perfectly. It will be the ability to sense change early, evaluate implications quickly, and orchestrate coordinated enterprise responses with agility. Demand planning will be one of the first functions to transition toward autonomous planning with humans in the lead for exception scenarios.
Sanjay Desai: By 2030, planning ecosystems will become more collaborative, integrating suppliers, distributors, and partners into shared decision frameworks. Ultimately, planning excellence will not be defined by the extent of automation alone, but by the clarity of human roles and an organization’s ability to sustain transformation through capability building and cultural change. Because, in the end, true transformation is not driven by technology — it is driven by people and the mindsets they bring to change.
(Disclaimer: The views and opinions expressed in this article are solely experts’ own and do not represent the official policy, position, or views of their employers or any organization with which they are affiliated. The content is based on their independent analysis, experience and expertise. )
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