ADA Global’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.

ADA Global’s Order Right helped the client to automate and optimize replenishment schedules for ~500 categories across all store locations in 13+ geographies via a single integrated interface. Here are the key highlights of the solution:

Ultra-Granular Multi-Variate ML-Based Forecasting

With Order Right, the client transitioned from manual, Excel-based forecasting reliant on historical sales to multivariate, ML-based forecasting that accounts for channel, category, and store-specific nuances at the hyperlocal level. This new approach incorporates factors such as product lifecycle, promotions, holidays, and events. As a result, demand planners achieved increased forecasting accuracy across product locations, leading to substantial downstream benefits.

Auto-Optimization for Promotional Effects

Previously, the client relied heavily on manual interventions to adjust for demand fluctuations caused by promotions. This often led to sub-optimal outcomes, resulting in excess stock of some products and out-of-stock situations for others within the same category. With Order Right, the client transitioned to automatic adjustments of product orders to counter promotional effects, significantly reducing inventory imbalances.

Modeling Supply Chain Constraints

The client heavily relied on global suppliers for its products, so any disruptions in the supply chain often came as a surprise, causing teams to scramble to manage the situation and devise tactics to protect the business. With Order, the client transitioned from a static, contract-based supply chain approach to dynamic modeling. Now, the client can optimize replenishment plans using self-learning models that account for key constraints such as lead times, pending orders, expiry dates, minimum order quantities, ordering frequency, and minimum display quantities. This ensures that replenishment plans dynamically adjust to supply chain constraints, resulting in greater accuracy and less crisis management.

Effortlessly Leveraging Retail Data

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.