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Order right
Reduction in Out-of-Stock Instances
Savings on Inventory Costs
Reduction in Inventory Investments
The client relied on manual interventions to perform demand and replenishment planning. With changing times especially post-2019, the client felt that their current system was inadequate to respond to shifts in demand patterns and supply chain challenges. The client faced a huge dip in replenishment accuracy and stock issues were rampant across multiple product locations, among other challenges, such as:
The client was looking for a comprehensive, intuitive, scalable, and robust AI/ML-based solution to generate precise order plans, and avoid stockouts and overstocks while optimizing inventory and improving shelf availability in an accurate and timely manner.
Algonomy’s Order Right perfectly met the client’s need for an ultra-granular, robust, and adaptive replenishment ordering system.
Order Right utilizes a suite of custom machine learning algorithms that adjust to demand and supply chain dynamics at a hyperlocal level, accounting for both increases and shifts in demand.
Its robust framework swiftly addresses retail data challenges such as sparse data, outliers, and noise, allowing your teams to focus on business without worrying about data interventions.
Algonomy’s Order Right helped the client to automate and optimize replenishment schedules for 200+ categories across all store locations in the region via a single platform. Here are the key highlights of the solution:
With Order Right, the client transitioned from sales heuristics-based demand forecasting to ML-based multi-variate demand forecasting at a highly detailed level. The models were trained using various factors such as product hierarchy, holidays, events, promotions, discounts, and demand deviations. Order Right automatically selects the optimal model for each product-location combination based on best-fit criteria, significantly improving the demand forecast accuracy for 90% of SKUs.
The previous replenishment framework depended on manual interventions to account for promotional effects like cannibalization. With Order Right, the client shifted to automated adjustments of replenishment levels, both up and down, between products, taking into account promotions and availability.
Before Order Right, the client depended on suppliers to deliver according to agreed-upon contracts, and any deviations from the SLAs caused stockouts and supply chain disruptions. With Order Right, the client transitioned to dynamic modeling of key factors like lead time, MOQ, minimum size pack, safety stock, display minimums, and pending orders to optimize order plans.
The previous approach of using multiple sheets and systems for order management at different supply chain nodes was ineffective and cumbersome. With Order Right, the client moved to centralized multi-echelon inventory management across stores, warehouses, and stocking points.
Order Right’s robust demand forecasting framework helped demand planners circumvent data challenges such as sparse data, noisy data, outliers, and new product introductions effortlessly with custom retail-tuned algorithms. This significantly reduced the efforts required by the team to get quality data.
Algonomy’s Order Right helped the client transform from static and inefficient to ultra-granular and intelligent replenishment. As a result, the client witnessed a host of benefits including:
Out-of-Stock Instances
Inventory Costs
Savings on Inventory Investments
Annual Reduction in Loss of Sales
