/ Case Studies / AI Demand Planning Reduces Inventory Carrying Costs, Improves Inventory Turns, and Credentials Supply Chain Case Studies AI Demand Planning Reduces Inventory Carrying Costs, Improves Inventory Turns, and Credentials Supply ChainAI Supply Chain OptimizationThe furniture retailer was struggling with excess carrying costs and missed revenue due to inaccurate forecasts and poor supply chain visibility. To address these challenges, we developed an AI-driven demand planning system that accurately predicts demand for thousands of products over multiple weeks. This automated system has improved forecasting accuracy by 15%, resulting in annual savings of $16 million across 120 stores. By implementing this innovative solution, the supply chain team has regained trust within the business leading to improved strategic decision-making and enhanced operational efficiency.Critical IssueThe mid-sized furniture retailer faced significant challenges, including $34 million in excess carrying costs and missed revenue due to stockouts. The supply chain analysts’ inconsistent and inaccurate forecasts, coupled with poor visibility and governance, led to bad predictions of consumer demand, resulting in erratic inventory levels and unaccounted over/under-buy costs. The perpetuation of these patterns of mistakes undermined business operations, eroded trust in the supply chain, and hindered the responsible forecasting process.Customer ProfileMid-sized furniture retailer120 stores in 16 statesKey Problems$34M in excess carrying costsMissed revenue from stockoutsour solutionWe developed a demand planning system that accurately predicts 10-to-26-week demand for 10,000 SKU/DCs, addressing long-standing business issues such as intermittent, erratic, lumpy, and smooth demand patterns. This fully automated system is currently running in production and includes a user-friendly dashboard for monitoring accuracy, as well as intuitive explanations for the forecasts. The supply chain team was trained on how to effectively use the system, and it was implemented in just 10 weeks by a team of one. The technology stack used for this system includes Azure, Python, Azure Data Factory, ADLS, AutoML, and Databricks.The resultsImproved forecastsOur system has successfully saved the company$16 million annually across 120 stores by being15% more accurate than human forecasts, leading to improved estimates of consumer demand. This significant cost savings not only enhances financial performance but also accelerates the effectiveness of new supply chain analysts in the industry, allowing them to be proficient in their roles more quickly.additional benefitsOur solution is facilitating the accumulation ofinstitutional knowledge by enabling the learning,encoding, and sharing of the drivers of the businessprocesses. As a result, the Supply Chain team isrebuilding trust with the business, leveraging thisknowledge to improve strategic decision-makingand enhance overall operational efficiency.Explore Our AI SolutionsInterested in exploring how our AI solutions are creating net-new revenue streams?Contact UsConcurrency Center of ExcellenceLearn more about Concurrency Centers of Excellence onlineLearn More
