Case Study:
Supply Chain Management

Objectives

Alpha Matica was engaged to articulate an innovative AI roadmap for a manufacturer and distributor across its UK supply chain.

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The objective was to outline a pragmatic plan for implementing AI and GenAI technologies toward efficiency, customer satisfaction, resilience, and innovation while ensuring alignment with the organisation's broader strategic goals and values.

 Ways of Operating​

Alpha Matica partnered with the senior leadership team across critical departments and respective stakeholders from Operations, Tech/IT, Product, Data, and Strategy. The engagement followed  four steps: 

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1. Set Objective: We established clear and measurable objectives for the AI supply chain aligned with the business strategy.

2. Assess Current State: We evaluated the existing supply chain processes, technologies, and capabilities. We identified pain points and areas for improvement where AI solutions could significantly impact the business.

3. Identify Use Cases: We identified specific AI use cases or applications to address the identified challenges and support the objectives using our systematic AI use cases template and set of supply chain archetypes. 

4. Prioritise Use Cases: We prioritised the previously identified use cases based on their potential business impact, feasibility, and alignment with strategic objectives. When prioritising use cases, we consider factors such as ROI, complexity, data availability, team readiness and implementation timeline.

 Outcome and Benefit​​

• Alpha Matica delivered a pragmatic AI plan of action that reflects the board's objective and the reality of the organisation's data, skills, and technological landscape.

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• The plan aimed to decrease costs between 5% and 15%, depending on the functions and current maturity levels.

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• The plan aimed to boost revenues between 3% and 10%, depending on the functions and current maturity levels.

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• Alpha Matica prioritised developing AI and GenAI projects based on their respective feasibility and return on investment. A right balance between archetype types of projects (e.g., procurement versus demand planning versus stock inventory) and technologies (e.g., Traditional AI versus Generative AI) was proposed.