Goal: Deliver the right products to the right retail outlets at the right cost and the right time.

Result: This company can now quickly respond to a fast-changing market of $2.6 billion, 1/3rd of their total global sales.

The International company grew quickly to become a direct selling giant in China. During their growth phase, they were heavily focused on analytics, incorporating it into their growth strategy. Despite their use of analytics, they lacked a forward view of their supply chain and struggled to improve the accuracy of their inventory and replenishment plans at brick and mortar retail outlets. Logistics planners arranged vehicles, destinations, and quantity of goods and warehouse space based on experience. This resulted in errors and increased logistics costs.

The Chinese division of a multi-national company known for direct sales improved replenishment time 20% and increased customer satisfaction to 97% with SAS DDPO.

With technology built on the SAS DDPO Inventory Optimization Workbench, the company developed an advanced inventory optimization system.

The SAS inventory optimization system (IOS) estimates and models optimum inventory levels based on service levels, delivery times, and costs. With SAS, the company improved predicted demand, transport, inventory replenishment, and key performance indicator alerts.

To more accurately predict demand, the IOS applies SAS time series forecasting to data from tens of millions of orders from the last 3 years. Based on this historical sales data, the system analyzes, models, executes and adjusts predictions for products and regions at different levels, and forecasts demand by specific product and outlet.

Using the optimized model, and predicted customer demand, the system calculates the replenishment frequency of different regions. To get the product where it needs to be on time, the system determines the optimal transportation schedule for the following week, based on transport data related to vehicle type, capacity and costs. This data is displayed directly in the system where tasks and manpower can be assigned according to the schedule, and the planner can make adjustments according to the actual situation.

In the calculations, the IOS fully considers the company’s multilevel replenishment network of factories, logistics centers, outside warehouses and stores, predicted demand, product variation, lead time, packing specs, inventory costs, transport costs, transport frequency, customer service level, inventory strategy, and minimum orders. This data set creates a more effective replenishment policy for reorder, order-up-to, and required quantity levels that can pinpoint each product in each warehouse and store, and adjust dynamically to market changes.

The IOS also provides KPI reports and emergency and alert functions necessary for daily inventory management, including inventory level analysis, procurement proposal analysis, inventory alarms, analysis for items temporarily out of stock, transport no-load rate, and supervision and control over product sales.

    When the SAS DDPO solution was implemented, the company had high praise:

    “With SAS predictive analysis and inventory optimization, we can keep inventory at the right level at the right time. When business or customer demands change, we can quickly adjust via the flexible inventory optimization system. With the IOS, we cannot only reduce logistics costs, but also enhance customer satisfaction and improve our competitive edge.”

    When the company implemented the IOS, the improved accuracy in demand prediction made it possible to plan replenishments, efficiently allocate vehicles, reduce empty loads, reduce the unnecessary transfer of goods among different warehouses and stores, optimize and save warehouse and storage space, prepare logistics plans, and reduce costs.

    The IOS has also provided a unified standard for stock replenishment. This has helped them improve service, reduce errors, and reduce costs. Any errors can now easily be tracked within the system for fast resolution.

    Operational efficiency has improved because data has improved. Stock replenishment time has been reduced by 20%, the inventory of main products has been reduced by 10%, and the quantity and times of goods out of stock have decreased.

    With SAS DDPO, the company has:

    • increased customer satisfaction to 97%
    • cut stock levels
    • maintain an optimum inventory
    • minimize delayed deliveries
    • reduced average total costs, including order costs and inventory costs.

    SAS Demand Planning and Optimization is now SAS Intelligent Planning Suite.


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