Relanto Director of Data and AI. Leads organizational transformation using data, technology and artificial intelligence.
In today’s rapidly changing business landscape, staying ahead of the curve requires a sophisticated approach to demand planning. Traditional methods often fail to adapt to the complexities of modern markets, forcing businesses to constantly operate in firefighting mode while being blinded to market changes. However, the integration of generative AI and large language models (LLM) presents an unprecedented opportunity to transform demand planning processes.
Prioritization of commercial drivers
Prioritizing commercial drivers is at the heart of effective demand planning. Generative AI excels at analyzing deep historical data to discern patterns and correlations that were previously elusive. By identifying key factors such as seasonal trends and emerging market developments, businesses can anticipate fluctuations in demand with greater accuracy, minimizing the risk of inventory imbalances. The use of these factors can differ across functions, channels and markets, providing a personalized approach to demand planning and allowing businesses to shape their future instead of managing the past.
Business planning collaboration
Collaboration is essential for successful demand planning. Generative AI facilitates cross-functional teamwork by providing insights and predictive alerts through cognitive chat agents. This ensures alignment across departments, from logistics to sales and marketing, resulting in more accurate forecasting and improved collaboration. For example, it can integrate input from logistics and supply chain teams and align them with sales and marketing planning cycles.
Trade inputs and factors
Generative AI can effectively integrate various business inputs into the planning process, including sales data, market trends, and consumer behavior insights. By identifying the most influential factors, organizations can tailor their strategies to specific functions, channels and markets, enabling proactive decision-making. Generative AI can unlock market and business wisdom within unstructured data and establish its relationships with measured data to provide a more holistic measure of planning outcomes.
Demand Planning Design Principles
AI optimization enhances demand planning design principles, providing flexibility in terms of cadence, time horizon, channels, units and financial factors. AI can tailor the planning process to a company’s specific needs, whether it’s orders, shipments, dollars or units and whether financial factors are integrated bottom-up or top-down down. This adaptability allows organizations to fine-tune their strategies for maximum effectiveness, particularly during key moments of strategic planning.
Metrics to measure performance
Accurately measuring performance is essential for continuous improvement. Common metrics include mean absolute percentage error (MAPE), weighted forecast accuracy, and forecast bias. AI can help organizations establish benchmark ranges for these metrics, distinguishing between average and best-in-class performance. Generative AI enables real-time calculations and granular insights, enabling teams to make data-driven decisions and establish reference ranges for key metrics like forecast accuracy and bias.
Steps in the Demand Planning Process
Integrated Business Planning (IBP), Sales and Operations Planning (S&OP), and Sales, Inventory, and Operations Planning (S&IOP) are the key processes integrated with demand planning. AI streamlines these integrated processes by automating data analysis and scenario planning. By optimizing stakeholder engagement and reducing bottlenecks, organizations can enable seamless execution at every stage of the planning process. Synchronizing lead times across different functions helps ensure everyone is working with the same data and assumptions, improving the accuracy of demand forecasts while promoting collaboration across business functions.
Interconnectivity with other planning functions
Demand planning is closely linked to other functions such as sales, marketing and finance. Generative AI can serve as the connective tissue between these functions, helping to ensure that data and information are shared transparently and that known and potentially unknown business drivers are discovered and understood. For example, it can align the sales and commercial plan with the demand plan, financial plan, marketing plan and launch plans, enabling a holistic approach to business planning. Additionally, it can identify potential risks and opportunities in the plan and provide concrete recommendations.
The ability to leverage generative AI and LLMs to optimize the demand planning process is a game-changer for businesses looking to thrive in today’s dynamic markets. By prioritizing business drivers, improving collaboration, integrating diverse inputs, following flexible design principles, measuring performance accurately, streamlining process steps, and fostering interconnectivity with other functions planning, companies can transform their demand planning strategies. Adopting AI-driven demand planning can lead to more accurate forecasting and help provide a competitive advantage to meet evolving customer demands, shape the market and achieve sustainable growth.
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