Early enterprise order management system adopters have begun to develop their allocation strategies and define brand-appropriate business rules in the pursuit of seamless inventory and store fulfillment. But enterprise order management’s long-term promise extends far beyond the ship-from-store capabilities now enabled nationwide. Those who can optimize and continually refine their order management systems will be able to successfully execute broader omnichannel strategies to improve the customer experience and drive bottom-line financial results.

The challenge now comes in leveraging diverse data sources within omnichannel order management to work through the complications raised by these new capabilities. Challenges that have surfaced along the way include store staff becoming overwhelmed by online order fulfillment, increased split shipments or costly shipping upgrades, poor in-store inventory management, and misaligned governance and confusion over order management responsibilities and logic.

Retailers who can quickly synthesize operational, financial and consumer insights to optimize their omnichannel order management decision logic—and then systematize continual refinement—will be able to improve service levels and, ultimately, omnichannel profitability.


Optimized omnichannel fulfillment can minimize costs and maximize responsiveness to consumer demand. A correctly configured enterprise order management (EOM) system can enable retailers to leverage store inventory when a distribution center is out of stock, determine the maximum distance from which a multi-item order can be fulfilled before shipping costs and delivery times become inefficient, and provide accurate information to customers to manage delivery expectations and maintain or improve customer satisfaction.

Omnichannel order management settings also require continual evolution—an ongoing strategy that needs to be adjusted, monitored and revised over time. Ultimately, the goal is to identify and balance business rules and logic governing where orders are filled and which inventory is used to achieve maximum results— and then do it over and over again as seasonal priorities shift and business conditions change. At the heart is identifying and prioritizing the various levers at play and aligning those priorities with the retailer’s operating model.

Kurt Salmon, alongside Manhattan Associates and Analytics Operations Engineering, has developed a simulation methodology to model the impact that different system configurations and decision logic will have on key performance indicators.

Order management optimization involves:

  • Cross-functional workshops to strategize allocation, store fulfillment, store capacities and employee roles
  • Data analysis to capture performance factors and variables
  • Examination and fine-tuning of the retailer’s order management business rules
  • Identification and prioritization of the levers and variables that inform the simulations, e.g., inventory levels and locations, minimum store SKU assortment requirements, store staffing levels, transportation costs, etc.
  • Simulation of order demand characteristics and omnichannel fulfillment
  • Analysis of simulation results and reconfiguration of business rules to drive improved results
  • Future-state process maps for various functional groups

Once a retailer’s unique business rules have been input into the simulation engine, the retailer can test scenarios and replicate tests under different models or capability enhancements as often as variables shift. Evaluating the operational and financial impact of business rule changes before deployment means retailers can take informed action without putting customer service or financial results at risk.

Retailers also don’t have to wait for actual business results to assess the impacts on key outputs and metrics. They can simultaneously analyze multiple variables and business conditions, e.g., peak vs. non-peak demand, and thus understand the impact of changes on multiple weeks of simulated data within hours.


A $4.1 billion department store needed to transform its ship-from-store package into a robust omnichannel solution focused on sales, profit and the customer experience. Kurt Salmon designed and optimized an omnichannel fulfillment strategy that led to a 6% reduction in split packages, a 5% reduction in average distance to customer for fulfillment and 40¢ saved per package. Eleven percent of store orders moved from FedEx Zone 3, 4 or 5 to a closer zone.


Kurt Salmon’s alliance with Analytics Operations Engineering unites unsurpassed expertise in all the drivers of a successful retail value chain with the advanced, cross- enterprise, quantitative methods of a rigorous engineering discipline. Analytics’ 20 years of experience combine with Kurt Salmon’s 80 years of experience across the supply chain to produce exceptional financial results.

Kurt Salmon has completed a significant percentage of Manhattan Associates’ enterprise order management (EOM) projects, and Manhattan has named the firm a top implementation partner for four years running.