Modernizing Build-to-Order Operations for a Leading Outdoor Furniture Manufacturer

The company's proprietary operational system began as a manufacturing execution system designed to optimize production scheduling and maximize output. As the business grew, it became the process and workflow backbone for everything from scheduling to shipping, and the original architecture could no longer keep pace with peak-season demand.

• Roughly one-third of annual revenue ships around a single holiday weekend, but demand arrived four weeks earlier than forecast and production could not keep up
• Scheduling logic relied on a 3,000-line hard-coded function developed over 15+ years, too risky to modify and resistant to internal rewrite efforts
• Manual, spreadsheet-based sequencing and cross-plant workload balancing consumed about 150 labor hours a day
• Ship dates for roughly 40% of the business were estimated rather than calculated from real capacity
• Production cell efficiency declined from about 85% to 78% as product complexity grew
• Operational and financial data were spread across multiple cloud and on-premises environments, limiting confidence in board and financial reporting
• Paper-based warehouse picking and peak-season congestion reduced productivity at one campus from 60 to 42 boxes per person per day

Rather than commit to a multi-year replacement program, the manufacturer adopted a phased modernization that augments its core system. Each new capability runs alongside the existing system of record, so floor operations change only after new logic is proven.

• Unified Data Foundation: An embedded data and AI team consolidated operational, commerce, and financial data from systems including Acumatica, Shopify, and Salsify into Databricks on AWS
• Tiered Executive Reporting: Reporting layers span board-level financials, operational drivers, such as margin, and profitability by channel and customer segment
• Intelligent Scheduling Engine: A modular, rules-based engine is replacing the legacy scheduling logic, producing a full schedule about every eight hours and adapting to same-day changes in seconds, with planners comparing scenarios and approving the final plan
• Directed Warehouse Execution: A logistics engine is introducing capacity-aware logistics zones, directed put-away from the end of the production line, load planning by virtual truck, and digital picking in place of paper tickets
• Event-Driven Integration: Cloud-native services, APIs, and event-based messaging keep the new engines synchronized with the existing system of record
• Side-by-Side Architecture: New engines are validated in shadow mode against live operations and roll out one facility at a time

With the data foundation in place and the scheduling and warehouse engines progressing through build and validation, the modernized operating environment is creating the foundation for the company to continue scaling their highly differentiated made-to-order model. As modernization progresses, it will support greater intelligence and efficiency of operations across the organization.

• Greater Peak-Season Capacity: A capacity-aware scheduling engine is designed to plan production against real labor, machine, and cell constraints, helping the company fulfil more peak-season demand from its existing plants without equivalent increases in manual planning effort
• Growth Within the Existing Footprint: Capacity-aware logistics zones and directed put-away help the company make better use of existing warehouse space as order volume grows, supporting its made-to-order model through the busiest weeks of the year
• Increased Operational Efficiency: Configurable scheduling rules and system-directed warehouse execution are designed to reduce spreadsheet-based sequencing and paper-based picking, allowing teams to manage greater volume more efficiently
• Reliable Customer Commitments: Calculated, capacity-aware ship dates are designed to replace estimates, helping the company keep its delivery promises when demand peaks
• Connected Decision-Making: A unified data foundation provides greater visibility across the business, from board-level financials and margin drivers to profitability by channel and customer segment
• Financial Impact: The data foundation underpins a data and AI program the client credits with $16M in EBITDA impact