Using AI to Improve Agility in Supply Chain

A leading American multinational shipping and supply chain management company employs an AI-driven GPS tool to devise the most effective routes for its fleet. This tool gathers customer, driver, and vehicle data, leveraging algorithms to generate optimal routes. By avoiding backtracking and navigating around traffic congestion, the tool assists drivers in punctually and efficiently completing their deliveries. 

Moreover, these routes can be dynamically adjusted based on real-time road conditions and other relevant factors. The optimization of delivery routes profoundly impacts various aspects of the company’s operations, ranging from time and cost savings to the reduction of emissions and vehicle wear and tear. By using the tool, the company anticipates a reduction of 100 million delivery miles, translating into substantial savings.

Artificial Intelligence is all-pervasive these days, yet its most profound influence might be seen within the supply chain. From predictive order anticipation to delivery management, AI can significantly enhance efficiency across all supply chain segments. It helps in building an intelligent supply chain network.

Establishing a Smart Supply Chain Infrastructure

Enterprises capable of centralizing data within their supply chain and implementing AI comprehensively can establish a cohesive and genuinely intelligent supply chain infrastructure.

Such an intelligent network offers the following benefits:

  • Enhanced Visibility: It enables businesses to promptly detect late-breaking disruptions in the supply chain or fluctuations in demand, furnishing crucial insights for near-real-time issue resolution.
  • Improved Agility: Companies can swiftly meet unique customer demands with precision and scalability by fostering agility. This leads to heightened product availability, improved service levels, reduced instances of lost sales and inventory management costs, and enhanced efficiency in production and fulfilment processes.
  • Enhanced Resilience: The intelligent network bolsters resilience, maintaining vital metrics such as On Time and In Full-service standards. Concurrently, it aids in diminishing the carbon footprint and mitigating overall sustainability risks for companies.

However, improving logistics and supply chain management using AI requires a phased approach:

  • Incorporate AI-driven data quality procedures to enhance the value of current Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) infrastructure and applications.
  • Employ AI for predictive and prescriptive analytics to mitigate operational risks from shortages and route planning.
  • Create AI-integrated dashboards to facilitate real-time visibility sharing among organizational stakeholders and trading partners.
Integrate diverse data sources, historical and real-time, to power an AI-driven, adaptive supply chain.

It’s fair to note that certain roadblocks in scaling AI in supply chains exist.

Challenges in Scaling AI in Supply Chains

Despite acknowledging the potential and value of AI, companies are likely to encounter persistent difficulties in expanding their investments more broadly. Why so? Here are some common obstacles to implementing AI in supply chains:

Functional Silos: Organizations harbour data in isolated compartments across the enterprise. Fragmented and disjointed data impede the application of intelligence, constraining insight generation and value creation.

Data Strategy and Quality: Companies grapple with the adequacy of their data collection. They either lack the appropriate data or possess data of insufficient quality to achieve desired outcomes.

Ownership: Determining the driving force behind the broader AI rollout and leading the initiative poses challenges for many companies. Uncertainty regarding leadership and advocacy for AI initiatives can easily hinder progress.

Prioritization of Use Cases: While AI holds vast potential and can be applied across numerous facets of business, aligning AI strategy with business strategy and prioritizing use cases for maximum value delivery proves challenging.

Identifying Suitable Solutions: The abundance of vendors, technologies, and solutions promising similar outcomes complicates the decision-making process for companies, making it arduous to discern the most suitable option.

Lack of Qualified Talent: Scaling AI necessitates highly technical talent, such as data scientists. However, technical expertise must be complemented by a deep understanding of business operations and strategy. Bridging the gap between these two domains and implementing a robust talent development strategy is imperative for AI to yield a significant impact.

Conclusion

The ongoing turbulence impacting supply chains is anticipated to persist. This is a timely opportunity for companies to revamp for enhanced agility. The utilization of AI holds tangible value, offering the potential to convert supply chain fragility into a competitive edge. Leveraging eSoftLabs’ expertise in AI-powered supply chain management services can empower your business to optimize processes effectively, unlocking newfound competitive advantages.

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