AI in Demand Planning — Are You Utilizing Your Full Potential?
In today’s fast-paced world of modern business, supply chains are the backbone of seamless operations. The ability to efficiently manage the flow of goods and services from manufacturing to the end consumer is paramount.
In recent years, artificial intelligence (AI) has emerged as a transformative force, revolutionizing the way supply chains operate. While the most prevalent use case is the application of AI and machine learning (ML) models to boost forecast accuracy in demand forecasting, as the use of AI and ML becomes more prevalent, use cases continue to emerge revealing significant untapped potential.
At Blue Yonder we have seen firsthand how using AI and ML can increase supply chain resilience, boost planner productivity, and bring agility to critical decision making. In this blog, we delve into the myriad ways AI is reshaping the future of supply chains.
Respond faster to disruption and build supply chain resilience with intelligent scenario planning
According to a recent Gartner survey, 68% of supply chain executives feel they’re constantly responding to high-impact disruptions while 67% said they don’t even have time to recover before the next one hits. Scenario planning is a critical tool for understanding the impact of disruptions and driving planning resolution, however, current tools and technology are reliant on planner intuition and manual intervention to create and evaluate multiple scenarios. Not only is manual scenario planning a tedious and time-consuming job, but it also often results in suboptimal decisions because too many granular scenarios or too few wide scenarios were created — missing critical levers and decision points.
Cognitive Demand Planning enabled by AI-powered insights allows planners to map out various levers in a scenario, set boundary values, the fire-and-forget. The advanced algorithms autonomously reduce the problem scope to a logical set of scenarios that are realistic and most applicable. Embedded predictive AI evaluates this feasible set of scenarios and recommends the top scenarios that optimize pre-set objectives. This AI/ML-powered scenario planning reduces the average time taken from hours (or days!) to minutes and allows planners to focus on actual strategic decision-making and actions rather than just collating data.
Make more informed decisions that better reflect your business realities by applying influencing factors
Applying influencing factors in demand planning is essential for businesses to make informed decisions, optimize operations, satisfy customers, and maintain a competitive edge in the market. By understanding the complex interplay of various factors, businesses can navigate the uncertainties of the market with confidence and agility. Factors like weather, new product introduction, holidays, changes in customer preferences, holidays, and cultural events can all have a significant impact on demand. For example, Beyonce’s Renaissance World Tour is projected to contribute about $.4.5 billion to the American economy and in one specific instance increased the demand of silver clothing and accessories by 25% . The ability to identify the right influencing factors, understand their effect on forecast and model their impact is paramount to ensuring forecast accuracy.
However, identifying the right influencing factors in demand planning is easier said than done. It generally requires hundreds of hours of work from data scientists, industry experts and product specialists — and still there’s always a chance that critical factors are overlooked. And as access to more and more data becomes readily available, the challenge of poring over these vast datasets becomes an even greater challenge. Deep meta learning — a cutting-edge innovation in the field of AI — powers a data-driven and algorithmic approach to the identification of influencing factors. With deep meta learning, ML models autonomously and continuously learn to select and configure the best combination of data. Not only does deep meta learning remove any guess work or human bias from the identification process, it also enables a faster reconfiguration of influencing factors when market and business realities demand it – allowing you to stay better aligned with the customer preferences.
With deep meta learning, demand planners will finally be empowered to capture the complete value of unlimited data and unlock the speed of integrated ML.
Unlock productivity and uplevel team performance with generative AI
There is a lot of excitement around the use and impact of generative AI – across a plethora of disciplines. In a recent article, Gurdip Singh, Chief Product Officer at Blue Yonder outlines the most impactful use cases of generative AI in supply chains.
Most notably, embedding generative AI with the natural language capabilities of large language models (LLMs) into demand planning solutions can dramatically improve planner productivity through faster access to data-driven insights, assisted decision-making, and process automation. When integrated directly into the user experience, planners can easily ask clarifying questions, request data, and visualize influencing factors and performance of past decisions, all of which helps to improve the quality of decision-making.
In addition, generative AI models that are trained on enterprise standard operating procedures, business processes, workflows, and software documentation can respond to planner queries with contextualized and relevant answers. Compare this with the current scenario where planners need to dig through multiple text-based resources to find answers to basic queries. And as we look to the next generation of demand planners, a generative AI-based training program can significantly reduce the time and effort required to cross-train planners and train new planners, getting new hires ramped up faster.
Capture your competitive advantage by embracing the full potential of AI
The integration of AI into supply chains is not just a technological advancement; it’s a fundamental shift in how businesses operate. Using advanced models to improve forecast accuracy is just the starting point. By harnessing the power of AI, companies can create agile, responsive, and sustainable supply chains that can more readily meet the demands of the modern market. Embracing these technologies is no longer an option but a necessity for businesses aspiring to thrive in the ever-evolving landscape of global commerce.
As we move forward, the synergy between human intelligence and AI will continue to redefine the very essence of supply chain management, propelling us into a future where efficiency knows no bounds.