Generative AI, or Gen AI, has emerged as a groundbreaking force within the supply chain landscape, captivating industry experts and stakeholders alike. With over a decade of experience in the AI space, Replan has witnessed numerous technologies rise and fall. Yet, Generative AI is one that truly captured our attention.

While there’s plenty of hype surrounding AI in the press, the question remains: will it deliver tangible benefits to supply chain teams in the near term?

A recent McKinsey survey found that about one-third of companies are now using Gen AI across at least one business function but use seems to be limited to individuals using it personally, rather than in a systematic manner. Even where it is systematic, manufacturing functions have the lowest adoption.

KPMG underscores the key areas identified by executives for leveraging Gen AI in operations, asserting that 77% of executives agree it is the most impactful emerging technology for augmenting productivity and fostering collaboration. Furthermore, more than half prioritize the following areas for adoption:

  • Customer service operations (78%)
  • Operations process optimization (66%)
  • Production planning and scheduling (47%)
  • Quality control and inspection (44%)
  • Inventory management (43%)

The gap between the current nonexistent adoption in manufacturing and execs saying they are prioritising planning scheduling tells you that despite the hype, it is very difficult to implement GenAI in technical areas.

Watch out to see which companies make the breakthrough in combining structured algorithmic approaches with GenAI delivering a better user experience to drive wider adoption.

Read KPMG’s report here.

While Gartner’s hype cycle suggests potential impacts in the distant future, we firmly believe that Generative AI will revolutionize supply chain companies as soon as 2024. Read more here.

Its transformative capabilities are poised to reshape every facet of supply chain management, ushering in a new era of efficiency, innovation, and sustainability.

 

Where we predict Gen AI will impact supply chains

  • Demand Forecasting: Gen AI leverages advanced algorithms to analyze sales data, market trends, and relevant variables, generating real-time demand models. Subsequently, it optimizes inventory levels, production schedules, and distribution plans to efficiently fulfill customer demands.
  • Production Planning: By considering customer requirements, production capacities, resource availability, and order priorities, Gen AI facilitates seamless production planning and scheduling. It orchestrates production sequences, allocates resources judiciously to minimize bottlenecks, and enhances overall production efficiency.
  • Risk Management: Gen AI employs sophisticated analytics to assess historical data, market conditions, weather patterns, and geopolitical events, identifying potential supply chain risks. It generates comprehensive risk assessments, conducts scenario simulations, and devises proactive mitigation strategies, enabling organizations to navigate uncertainties effectively.
  • Scenario planning: Gen AI utilizes advanced analytics to analyze historical and real-time data, market dynamics, and external factors, facilitating comprehensive scenario planning. By conducting scenario simulations and considering various parameters such as demand fluctuations and supply chain disruptions, Gen AI helps organizations anticipate potential outcomes and devise proactive strategies. This enables supply chain teams to make informed decisions, optimize resource allocation, and enhance operational resilience in the face of uncertainties, ultimately driving strategic success.

As the industry embraces the transformative potential of Gen AI, supply chain companies stand poised to unlock unprecedented levels of operational optimization and resilience. The future of supply chain management is indeed here, driven by the revolutionary capabilities of Gen AI.

For more insights and updates and your weekly dose of Supply Chain Planning news,  follow Replan on LinkedIn.

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