Most warehouses have a strategy problem. Too many leaders rush into robotics and high-density storage without defining success, cleaning up processes, or agreeing on the KPIs that will prove value. The result is expensive technology layered onto leaky workflows, weak data foundations and vague business cases. To close the gap between automation spend and measurable ROI, organizations must start with strategic design, scenario-tested business cases and data integrity, then let automation amplify a model that already works. ERP systems can help with this process by linking the information systems together.
Tony Wayda, principal, client advisory and partnerships for JBF Consulting, discussed the challenges companies face and what they’re trying to grapple with ahead of Modex 2026 in Atlanta.
Question: Where do you see the biggest gaps between warehouse automation investments and measurable ROI, and how can leaders better quantify value creation?
Tony Wayda: The biggest gap is strategic alignment. Research shows 92% of supply chain leaders report technology investments haven’t fully delivered expected results, with integration complexity and data issues as the primary culprits. Leaders rush to automate before defining what success looks like operationally and if they have the KPIs defined to measure them. To better quantify value creation, perform a strategic design assessment first—fix process leaks so automation accelerates a high-performing model, not a broken one.
Q: What practical steps can supply chain leaders take to ensure robotics and automation projects are tightly aligned with clear, outcome-based business cases?
TW: Our research shows 38% of business cases are “check the box” exercises lacking rigor, and 49% of organizations start with tools before strategy is set. Leaders should define their target operating model first, stress-test the business case against multiple scenarios, and establish measurable KPIs tied to P&L impact before engaging any vendor. Anchor every automation decision to a specific operational outcome that is measurable and stress tested.
Q: How are changing customer expectations for speed, personalization, and sustainability reshaping long-term supply chain network strategy and facility footprints?
TW: Speed, personalization, and sustainability are forcing organizations to rethink where inventory sits and how it moves. We worked with a company that needed to cut transit times without overbuilding, achieving a 50% reduction in average U.S. transit time and 20% inventory reductions. The lesson: network redesign must balance service-level ambitions with capital discipline. Customer expectations set the target; your network model must deliver it profitably.
Q: What frameworks do you recommend for balancing cost efficiency with service-level commitments when designing or redesigning supply chain networks?
TW: Most supply chain risk is created upstream, long before disruption occurs. Yet only about 25% of logistics leaders revisit strategy annually, leaving decisions anchored to outdated assumptions. Leaders should embed risk scenarios into core strategic planning, not treat resilience as a side initiative. Define ownership across scope, risk, and outcomes so when disruptions arrive, your response is governed by design, not improvised under pressure.
Q: What role should scenario modeling and stress testing play in ongoing supply chain planning, beyond one-time network optimization projects?
TW: Scenario modeling should be embedded in an ongoing planning cadence, not limited to one-time network studies. Slightly more than a quarter of organizations have built future ready capabilities; the rest are reacting. Continuously stress test business case assumptions, capacity, and service commitments to expose vulnerabilities early. This builds agility into the supply chain, enabling faster and more confident decisions as conditions shift and turning planning into dynamic, proactive management.
Q: How can organizations turn fragmented data into actionable visibility that directly improves fulfillment decisions, inventory placement, and order promising?
TW: Poor data quality costs organizations an average of $12.9 million annually, creating a silent tax on every decision. Start with a data integrity audit to establish a trusted foundation. Prioritize integration across TMS, WMS, and order data, where connected environments resolve exceptions up to 40% faster. AI can accelerate this by cleansing, standardizing, and enriching data at scale. True visibility is not a dashboard, it is clean, connected data enabling real-time, decision ready insights.
Q: What metrics best capture the impact of improved data visibility on inventory health, stockouts, and customer satisfaction across channels?
TW: Focus on metrics connecting data quality to operational and financial outcomes: order-to-shipment cycle time, exception resolution speed, inventory turns, stockout frequency, and perfect order rate. At one client, improved visibility drove a 12% year-one freight spend reduction and 70% faster order-to-shipment speed. The best metrics aren’t just operational—they tie directly to your P&L and customer experience commitments.
Q: Where do technology-led initiatives most often fail to gain workforce adoption, and how can leaders better involve frontline teams from the start?
TW: Adoption fails when technology is deployed as a technical go-live rather than a business transformation. Many logistics transformations fail to hit critical metrics—but teams that incorporate employee feedback increase success odds. The failure point is the “Go-Live Gap”: the system is live, but processes, policies, and roles haven’t been redesigned around it . Involve frontline teams in process design, not just training.
Q: What change management practices have you seen most effective in helping employees trust, adopt and continuously improve new supply chain technologies?
TW: The most effective practice is making change tangible and role-specific. Generic training doesn’t build trust—redesigning processes and policies by role does. We see the greatest adoption when organizations define clear accountability, provide measurable enablement tied to specific workflows, and maintain disciplined hypercare governance post-go-live. Research shows 20% of transformation value is lost after implementation. A structured stabilization phase with frontline feedback loops captures and sustains that value.
