Will Supply Chain be Source of Competitive Differentiation?
A recent analysis from Kearney, The top five supply chain bets for 2026, concluded that as customers punish inconsistency faster than ever, companies that can deliver reliability will expand market share.
They offer this analysis:
This forces a shift from one supply chain to a portfolio of capabilities designed around distinct value propositions including speed, reliability, customization, cost-to-serve, and compliance. Where commercial commitments are made in isolation from operations, the consequences surface later through margin erosion, excess inventory, and lost customers.
Supply chain becomes the operating core of the customer promise, and leadership must be explicit about where it will overperform and equally clear about where performance ambition can be more modest by design.
Leading organizations are becoming more deliberate about how they serve each channel, market, and customer, including the trade-offs required and their operational implications. Align those choices with differentiated supply chain capabilities for each segment and translate them into targets for the core KPIs (service, cost, cash, risk). Finally, leverage the integrated planning and execution process to deliver consistently against those objectives.
Another area of concern is AI as it moves along the continuum from experimentation to earnings impact.
They offer the following analysis:
In 2026, many pilots will fail to progress beyond experimentation. The root causes are predictable: unclear value cases, poor data quality, fragmented technology stacks, and pilots that were never designed to scale.
AI in supply chain needs to be treated as an industrial capability, with clear ownership, governance, monitoring, and integration into day-to-day processes. Organizations that remain in experimentation are accumulating prototypes and skepticism, while those that focus are translating AI into measurable improvements in cost, cash, service, and risk.
Leading organizations are managing AI use cases as a portfolio, with explicit scale and stop gates. A small number of use cases that materially affect service, cost, cash, or risk are being industrialized, while others are time-boxed with clear exit criteria. Investment is concentrating on priorities with the highest enterprise impact, including decision speed, resilience, and sharpening competitive supply chain advantage.