高責任物流の現場は、誠実に管理されています。温度、湿度、振動、位置情報、作業記録、品質基準は、各社ごとに残されています。問題は、現場の努力が足りないことではありません。

 

それでも、荷主は「責任を証明できない」。

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医薬品、半導体、精密機器、医療機器、重要部材、研究用資材、保守部品などの物流では、貨物は製造元、委託先、倉庫、輸送会社、3PL、卸、製造ライン、販売拠点、保守拠点、回収・廃棄拠点など、複数の会社と拠点をまたいで動きます。

そのたびに、ログ形式、時刻基準、判断基準、品質基準が分かれます。結果として、「誰が・いつ・どの条件で・何を根拠に判断したか」を、荷主が一つの証拠連鎖として示せません。

この構造問題は、医薬品物流だけの問題ではありません。医薬品ではGDPガイドラインが流通過程の品質保証を求めています。一方で、半導体・精密機器・医療機器・重要部材でも、温湿度、振動、衝撃、納期遅延、工程停止、品質劣化など、説明責任の重い判断が増えています。

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さらに改正物流効率化法により、一定規模以上の荷主・物流事業者には、中長期計画、定期報告、CLO選任などが求められます。つまり、物流は「運ぶ」だけでなく、「判断と責任を説明する」段階に入っています。

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ここに物流AIが入ると、問題は解消されません。むしろ加速します。

いまの物流AIは、配車ルート、在庫配置、需要予測、温度異常、作業手順を「提案・通知」する助言役です。しかし次の段階では、AIがルートを組み替え、在庫配置を変え、発注数量を決め、温度・振動・遅延リスクを検知して、出荷・搬送・保管を止める実行主体になります。

そのとき、「AIがそう判断したから」では説明になりません。必要なのは、AIが関与した判断について、「誰が・どの条件で・何を根拠に通したか」を後から第三者が検証できる仕組みです。

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物流のAIガバナンスとは、AIが賢いかどうかではありません。問題発生時に、判断の条件・根拠・責任の連鎖を第三者が検証できる形で残すことです。

 

EU AI Act、ISO/IEC 42001、NIST AI RMFが求めているのも、精度そのものではなく、この責任構造です。

この設計の数理基盤がADICであり、現場実装のパートナーがオンザリンクス社です。

High-accountability logistics is already managed with sincerity. Temperature, humidity, vibration, location data, work records, and quality standards are recorded by each company. The problem is not a lack of effort on the ground. Even so, the shipper cannot prove responsibility. In logistics for pharmaceuticals, semiconductors, precision equipment, medical devices, critical components, research materials, and maintenance parts, goods move across multiple companies and sites: manufacturers, contractors, warehouses, carriers, 3PL providers, wholesalers, production lines, sales locations, maintenance sites, and recovery or disposal points. At each handoff, log formats, time standards, decision criteria, and quality standards differ. As a result, the shipper cannot present a single evidence chain showing who made which decision, when, under what conditions, and on what basis. This structural issue is not limited to pharmaceutical logistics. In pharmaceuticals, GDP guidelines require quality assurance across the distribution process. In semiconductors, precision equipment, medical devices, and critical components, decisions involving temperature, humidity, vibration, shock, delivery delays, production-line stoppages, and quality degradation are also becoming increasingly accountability-heavy. In addition, Japan’s amended Logistics Efficiency Act requires certain large shippers and logistics operators to prepare medium- to long-term plans, submit periodic reports, and appoint a Chief Logistics Officer. Logistics is no longer only about moving goods. It is entering a phase where decisions and responsibility must be explainable.
When logistics AI enters this structure, the problem is not solved. It accelerates.
Today, logistics AI mainly acts as an advisor. It proposes delivery routes, inventory placement, demand forecasts, temperature alerts, and work procedures. In the next phase, however, AI will become an actor that reorganizes routes, changes inventory placement, determines order quantities, detects temperature, vibration, and delay risks, and stops shipment, transport, or storage operations. At that point, “the AI made that decision” is not an explanation. What is needed is a mechanism that allows third parties to verify, after the fact, who approved what, under which conditions, and based on which evidence when AI was involved in the decision.
This is what AI governance in logistics actually means.
AI governance in logistics is not about whether AI is intelligent. It is about leaving the conditions, evidence, and chain of responsibility for each decision in a form that can be verified by third parties when a problem occurs.
The EU AI Act, ISO/IEC 42001, and the NIST AI RMF are not asking only for accuracy. They are asking for this responsibility structure.
ADIC provides the mathematical foundation for this design, and ONZA LINX is the real-world implementation partner.