Gartner: How AI is Revoluting Supply Chain Management

The four pillars of AI strategy (Credit: Gartner)
Gartner have looked at how supply chain AI is transforming supply chains and how potential impact it will have on KPI and robotics

AI is rapidly revolutionising supply chain management, promising to enhance strategy and operations significantly.

By 2028, expect two transformative shifts in supply chain operations: 25% of KPI reporting will be powered by generative AI (GenAI) models, and smart robots will outnumber frontline workers in manufacturing, retail, and logistics.

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Define the AI strategy

Come next year, more and more data-driven decisions (95%, according to Gartner) expect to be at least partially automated. However, only 10% of CEOs say their businesses use AI strategically, and even fewer technology leaders (9%) say they have business has a defined and clear AI vision statement.

In a recent Gartner survey, the company found that mature AI companies were more likely to define performance metrics early in the ideation phase of every AI use case.

Creating a comprehensive AI strategy that mitigates risks and uses AI to gain a competitive edge, with that strategy being both defensive and competitive, addresses the four key pillars: vision, value, risks, and adoption.

AI is rapidly revolutionising supply chain management (Credit: Gartner)

AI Vision:
Establish a clear vision for how AI advances supply chain strategy. This clarity fosters organisational fluency and supports funding AI initiatives that promise a high return on investment. Goals include:

  • Reducing supply chain costs
  • Increasing productivity through automation 
  • Improving customer satisfaction
  • Enhancing forecast accuracy with predictive analytics.

AI Value:
Identify barriers that could impede AI success and leverage change management to overcome them. It involves sizing the AI portfolio, piloting projects, and collaborating with IT and analytics leaders to establish accountability.

AI Risks:
Mitigate regulatory, reputational, competency, and technological risks by establishing AI governance, strengthening cybersecurity, and developing data literacy among the workforce.

AI Adoption:
Prioritise AI initiatives based on their value and feasibility. Focus on high-value projects that are practical and likely to succeed, ensuring a balanced approach to AI implementation.

GenAI Pilot Life Cycle (Credit: Gartner)

Align short-term GenAI applications with key supply chain goals

A successful supply chain AI strategy hinges on identifying which processes will benefit most from AI implementation. However, collecting potential use cases for digital transformation can be overwhelming. Evaluating and prioritising these use cases is time-intensive and complex. It's essential to assess the practicability, impact, and cost of each proposal while considering potential risks and benefits.

Chief Supply Chain Officers (CSCOs) increasingly turn to GenAI to enhance their operations. However, the task of identifying, evaluating, and prioritising potential AI use cases can be daunting. Practicability, impact, cost, and potential risks are crucial in decision-making.

Despite these challenges, nearly 70% of business leaders believe the benefits of GenAI adoption outweigh the risks. To harness the power of GenAI, Gartner advises CSCOs to align short-term AI initiatives with four key supply chain objectives:

  1. Defining and Implementing Strategy: Designing and managing the strategy, including its financials and sustainability.
  2. Managing Technology: Overseeing data management, creating a digital roadmap, and leveraging technology investments.
  3. Building an Effective Organisation: Executing a talent strategy and fostering the right organisational culture.
  4. Managing Supply Chain Performance: Defining and managing processes, developing analytics, and governing performance.

Short-term GenAI Use Cases:

Content Creation: Generating written content based on specific requirements.
Information Discovery: Providing answers to prompts.
Summarisation: Condensing conversations, articles, emails, and webpages.
Content Classification: Sorting content by sentiments or topics.
Chatbot Enhancement: Improving conversational abilities.
Software Coding: Generating, translating, explaining, and verifying code.

Beyond understanding the GenAI's benefits and use cases, it's crucial to recognise the risks and barriers to its adoption. AI models depend heavily on the quality of their training data; incomplete or inaccurate data can lead to flawed insights. Additionally, large volumes of data increase the risk of exposing intellectual property and raising the carbon footprint. There's also the risk of losing human expertise; for instance, relying solely on generative AI for supplier risk assessments could cause this skill to diminish among employees over time.

Jeff Block, SVP of Structural Procurement & Growth Programs at Dell Technologies, describes the cost, productivity, and strategic benefits of supply chain automation

Jeff Block, SVP of Structural Procurement & Growth Programs at Dell Technologies

“We are trying to digitise everything we do, building what we would call a ‘frictionless’ supply chain,” he said.
“How do you make simplicity of data from all your supply base, through all your facilities, and thinking in terms of automated forecasting, automated lead time setting, and driving different aspects of what we do, predicting how much inventory we need to hold. By taking all the data inputs in, and taking away the task-driven activities from people so they can focus on the more strategic aspect of procurement or supply chain.
“Managing more with less, and how can digital help you do that? That’s a big important focus for the long term.”

Why, What, and How to Invest in Supply Chain AI

Supply chain technology is seen as a crucial driver of competitive advantage by industry leaders. A recent survey reveals that 27% of supply chain leaders rank "gaining competitive advantage" or "addressing competitive disadvantage" as a top reason for investing in emerging technology, with 9% listing it as their primary goal.

Gartner say only 27% of supply chain leaders seen technology as a critical enabler of competitive advantage

Gartner has identified four major areas of AI technology investment:

Labour:
Addressing labour costs and shortages through automation and enhancing workforce motivation. Gartner predicts that by 2028, 40% of large warehouse operations will deploy employee engagement tools.

Intelligence:
Enhancing decision-making speed and quality through advanced analytics, machine learning, and GenAI. These technologies are vital as supply chains grow more complex and volatile.

Edge:
Combining cloud applications' value with the responsiveness of edge technologies. By 2027, 80% of manufacturing operations management solutions are expected to be cloud-native and edge-driven.

Security:
Tackling cyber risks prevalent in supply chains. By 2026, 15% of supply chain software will utilise software bills of materials (SBOMs) to counter cyberattacks.

AI has the potential to transform job roles, meet evolving customer demands, and boost workplace productivity. However, not all AI use cases offer equal benefits. It's crucial to categorise AI initiatives as follows:

Everyday AI:
Focused on productivity, these solutions improve efficiency but do not provide significant market differentiation.

Boundary-pushing AI:
These initiatives aim for substantial improvements in large-scale operations but are not yet transformative enough to revolutionise supply chain management.

Game-changing AI:
Innovative and creative solutions that can enhance core operations, generate new revenue streams, and make lasting societal or cultural impacts. For successful AI implementation, supply chain leaders should ensure organisational readiness and follow a structured approach.

Gartner recommends a five-step formula:
  • Collect impactful, measurable, and quickly solvable use cases.
  • Assemble the necessary skills and talent.
  • Gather relevant data for the selected use cases.
  • Select AI techniques aligned with the use cases, skills, and data.
  • Conduct proofs of concept to integrate new AI expertise into the organisation.

Beyond understanding the benefits and use cases of GenAI, it's crucial to recognise the risks and barriers to its adoption. AI models depend heavily on the quality of their training data; incomplete or inaccurate data can lead to flawed insights. Additionally, large volumes of data increase the risk of exposing intellectual property and raising the carbon footprint. There's also the risk of losing human expertise; for instance, relying solely on GenAI for supplier risk assessments could cause this skill to diminish among employees over time.

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