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Fulfillment Capacity Planning for an Omnichannel Retailer

A major luxury brand retailer implemented a data-driven capacity planning process to enhance service levels for online consumer orders by refining fulfillment capacity planning while maintaining cost targets.


Background: Originally established as a luxury department store chain, the retailer has expanded its operations to include off-price stores and an online channel, serving consumers across the USA. The retailer oversees three large fulfillment centers and a specialized small fulfillment center dedicated to a large metropolitan area, serving its diverse lines of business. Along with offering customers a comprehensive omnichannel experience through their stores and website, the retailer operates its fulfillment centers to cater to both online and store channels. While adopting an omnichannel approach was the right strategy, they faced challenges meeting service levels and cost targets as the online channel expanded, leading to a reduction in service expectations during peak demand seasons. They recognized that without an improved fulfillment capacity planning process supported by appropriate technology, meeting their targets would remain a challenge. They risked falling behind competitors despite investing in increased fulfillment capacity.

Challenges: Starting a fulfillment planning process posed significant challenges:

  1. Data Availability: Initial planning faced obstacles due to inadequate data availability in databases. Collaborating across the supply chain, fulfillment centers, and finance, our team aggregated crucial data in spreadsheets. Recognizing the value, we advocated for improved data recording practices, working closely with the data engineering team to implement these changes.

  2. Data Granularity: Despite data-gathering efforts, certain information lacked necessary granularity for accurate planning. For instance, worker productivity rates lacked considerations for variables like product type and worker type.


The Solution: Using network optimization and fulfillment capacity planning modules, created a solution that formed the cornerstone of the retailer’s monthly supply planning process and leadership meetings. This solution facilitated informed decisions for supply chain leadership, fostering consensus among various stakeholders across departments and business lines. Leveraging decision intelligence tools, the solution provided:

  1. Enhanced Service Level: Through optimization of planning levers, service levels were predicted and improved at a granular level. Granularity was defined by date, operation (demand fulfillment, returns, etc.), business line (full price, off-price), channel (store, online), fulfillment center, and customer type (cardholders, premium, etc.).

  2. Optimized Inventory Allocation: Efficient inventory allocation to fulfillment centers by considering shipment costs and fulfillment center capacities.

  3. Granular Workforce Productivity Estimations: Better worker productivity rate estimates factoring in fulfillment center, process, product type, and worker type.

  4. Optimized Workforce Allocation: Optimal allocation of fulfillment center workforce by process, shift, and worker type

  5. Hiring Plan: Addressing temporary and part-time worker hiring and training needs.

  6. Adaptive Analysis: Ability to quickly update the analysis as financial forecasts change.

  7. What-if Scenarios: Capabilities to answer questions such as –

    • Should they reallocate incoming inventory to mitigate storage issues?

    • Is there a need for machine capacity expansion, and to what extent?

    • What would be the impact on the service level if they were to add a single unit order packaging automation line?

    • During peak demand seasons, should the fulfillment center expand its current shifts or add an additional shift?

    • What would be the impact on service level and shipment costs if more online orders are allocated to stores?

    • What service level can the retailer commit to by channel and business line during peak demand weeks?

In addition to its ability to promptly analyze and address the aforementioned questions, this solution established standardized processes, and visual reporting, and offered a comprehensive workforce allocation plan to fulfillment centers, enabling proactive workforce management. Benefits Achieved: The implemented solution yielded remarkable improvements across various aspects:

  1. Service Level Improvement: Unlike past years' Cyber Week (Black Friday to Cyber Monday) when the promised delivery window was extended (sometimes from 6 to 12 days for online orders), better planning drastically reduced the need for such adjustments.

  2. Storage Issues Mitigation: Reduced pre-peak season inventory overflow through better allocation strategies.

  3. Improved Workforce Utilization: Optimized hiring and allocation, improved resource utilization and productivity.

Conclusion: This data-driven solution revolutionized the retailer’s monthly fulfillment capacity and workforce planning, fostering standardization and collaboration among stakeholders. Scalable to accommodate network growth, this approach ensures ongoing optimization and adaptability, positioning the retailer at the forefront of omnichannel retail excellence.

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