{"id":61866,"date":"2026-03-13T08:00:54","date_gmt":"2026-03-13T12:00:54","guid":{"rendered":"https:\/\/clarkstonconsulting.com\/?p=61866"},"modified":"2026-03-16T16:24:06","modified_gmt":"2026-03-16T20:24:06","slug":"2026-supply-chain-trends","status":"publish","type":"post","link":"https:\/\/clarkstonconsulting.com\/insights\/2026-supply-chain-trends\/","title":{"rendered":"2026 Supply Chain Trends"},"content":{"rendered":"

\"2026<\/a><\/strong><\/h2>\n

Download the full 2026 Supply Chain Trends Report here<\/span><\/a>. <\/strong><\/h2>\n

This free trends report outlines industry perspectives and expert advice from our team of supply chain consultants. You can view an excerpt of the report below, and if you\u2019d like to discuss any of the trends or other challenges in the supply chain space, connect with our team today<\/a>.<\/p>\n

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Key Supply Chain Trends<\/strong><\/h2>\n

In 2026, supply chain transformation will accelerate through rising AI adoption and deeper integrations across core technologies. <\/span>Organizations will experiment with and\u00a0<\/span>hone in on<\/span>\u00a0specific AI uses cases within supply chain planning and<\/span>\u00a0<\/span>execution to drive cost savings and efficiency within the supply chain. Ongoing\u00a0<\/span>supply chain uncertainty<\/span>\u00a0and increased cybersecurity risk will continue to challenge organizations<\/span>,\u00a0<\/span>highlighting the importance of\u00a0<\/span>assessing supply chain network and\u00a0<\/span>optimizing supply chains to be agile and adaptable, while emphasizing the importance of risk management within daily operations.<\/span><\/span>\u00a0<\/span><\/p>\n

Clarkston’s supply chain<\/strong><\/a> consultants have highlighted the top trends that businesses should consider and keep top-of-mind throughout the year:<\/p>\n

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  1. Expansion of AI use cases within supply chain<\/li>\n
  2. Managing supply chain uncertainty with network design & optimization<\/li>\n
  3. Leveraging integrated technology to drive supply chain outcomes<\/li>\n<\/ol>\n
    Trend 1:<\/span><\/span><\/strong><\/h5>\n
    Expansion of AI use cases within supply chain\u00a0<\/span><\/span><\/strong><\/h6>\n

    In 2026, we\u00a0anticipate\u00a0the focus of AI\u00a0utilization\u00a0to rapidly expand beyond\u00a0planning to\u00a0include greater emphasis\u00a0on execution.\u00a0As\u00a0agentic\u00a0AI\u00a0matures, organizations\u00a0are starting to venture into\u00a0autonomous supply chain execution, often\u00a0with\u00a0<\/span>minimal or no human intervention<\/span><\/a>\u00a0required.<\/span>\u00a0<\/span><\/p>\n

    Within the planning function\u00a0today,\u00a0AI is primarily leveraged\u00a0to\u00a0<\/span>enhance decision-making<\/span><\/a>:\u00a0improving the quality of forecasts,\u00a0identifying\u00a0trends, and recommending actions or exceptions for teams to evaluate.\u00a0Going forward, we\u00a0can expect to see organizations experiment with\u00a0agentic\u00a0AI\u00a0to\u00a0directly make business decisions\u00a0and act\u00a0vs. recommending actions for teams to implement.\u00a0Rather than relying on\u00a0planners to interpret insights, revise orders, and develop production schedules,\u00a0agentic\u00a0AI\u00a0can\u00a0assess all available information,\u00a0determine\u00a0the best course of action, and autonomously carry out these tasks.\u00a0However, achieving\u00a0this vision requires organizations to\u00a0shift their approach across several key areas.<\/span>\u00a0<\/span><\/p>\n

    Data quality and analytics maturity play a foundational role in\u00a0adopting\u00a0an AI-driven approach. Organizations with\u00a0high levels\u00a0of maturity in this area can implement faster and\u00a0extend AI across\u00a0a wider\u00a0set of use\u00a0cases, but that\u00a0doesn\u2019t\u00a0mean\u00a0others\u00a0can\u2019t\u00a0start realizing the value\u00a0of\u00a0<\/span>AI within strategic areas<\/span><\/a>\u00a0and\u00a0focused use cases. Investing in data engineering, data governance, and advanced analytics to ensure foundational data is consistent and reliable drives the potential for\u00a0<\/span>unlocking further AI capabilities<\/span><\/a>.\u00a0As part of that foundation, organizations\u00a0also\u00a0need to evaluate how they\u2019re factoring in\u00a0today\u2019s\u00a0rapidly changing environment and\u00a0the relevance of historical data\u00a0given the constant change\u00a0in recent years\u00a0(<\/span>e.g. COVID\u00a0impacts, tariffs, purchasing pattern\u00a0changes<\/span><\/i>)\u00a0as advanced models and data tools are heavily reliant upon the accuracy of this data.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

    Governance frameworks and guardrails are\u00a0equally\u00a0critical to\u00a0setting agentic AI up for success and\u00a0aligning the\u00a0utilization\u00a0approach across the organization.\u00a0Clear boundaries\u00a0need to be\u00a0defined\u00a0for\u00a0when agentic AI can act independently, when decisions need to be escalated, and where human approvals\u00a0remain\u00a0essential.\u00a0Doing so helps\u00a0ensure\u00a0autonomous execution\u00a0stays\u00a0aligned to business strategy, customer commitments, and the\u00a0organization\u2019s\u00a0overall risk appetite.<\/span>\u00a0<\/span><\/p>\n

    Cross-functional alignment and business process\u00a0redesign\u00a0are\u00a0also\u00a0needed to support an AI-driven approach. Enabling\u00a0agentic\u00a0AI across business functions (<\/span>e.g.\u00a0planning, sourcing, production,\u00a0logistics, and customer operations<\/span><\/i>) requires new ways of working,\u00a0along with mechanisms to integrate\u00a0decisions across various\u00a0functions\u00a0so\u00a0organizations can\u00a0optimize solutions and create\u00a0a more\u00a0adaptive, resilient supply chain.<\/span>\u00a0<\/span><\/p>\n

    Finally,\u00a0successful adoption\u00a0depends on closing the gap between\u00a0<\/span>AI literacy and\u00a0business expertise<\/span><\/a>,\u00a0supported by\u00a0key layers of talented individuals\u00a0with\u00a0skillsets\u00a0that span\u00a0both areas.\u00a0In\u00a0parallel,\u00a0performance measurement\u00a0should evolve\u00a0to evaluate the effectiveness of AI-driven models.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Organizations\u00a0should\u00a0monitor\u00a0the learning rate at which models incorporate new data, adjust to changing conditions, and refine\u00a0the logic\u00a0used\u00a0to make decisions.\u00a0They should also assess\u00a0the time taken to detect issues, which helps clarify how\u00a0quickly the system can\u00a0identify\u00a0anomalies or supply chain disruptions, enable quicker intervention, and reduce downstream\u00a0impacts.\u00a0Advanced planning tools\u00a0are incorporating AI capabilities to enhance planning\u00a0efficiency and drive\u00a0the organization to focus on the true exceptions that need to be managed.\u00a0<\/span>\u00a0<\/span><\/p>\n

    Continue reading by downloading the full report below.<\/strong><\/em><\/span><\/p>\n

    Download the Full 2026 Supply Chain Trends Report Here<\/a> <\/span><\/p>\n

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