The Future is Here: AI and Predictive Analytics in Procurement 4.0

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Procurement's Transformation Journey

Procurement, often considered the backbone of organisations, has undergone several evolutionary stages. From ancient Romans efficiently managing war supplies to modern-day enterprises optimising costs, the journey has been remarkable. Now, with the advent of AI, we're entering Procurement 4.0; a phase where data-driven decision-making and predictive analytics take centre stage.

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Procurement is on the cusp of exponential transformation

Before 2020, AI faced limitations that hindered widespread adoption. However, organizations have now crossed the chasm, embracing AI for procurement functions. Innovators and early adopters are reaping benefits like higher savings, improved supplier relations, and increased revenues. But what exactly is Procurement 4.0, and why is AI crucial?

Hold on, why are we talking about AI and what is 4.0?

Procurement 1.0: Time Efficiency: In the early days, procurement focused on streamlining logistics. Efficiently managing the flow of goods ensured timely delivery and operational effectiveness. Think of it as the "logistics sprint" phase.

Procurement 2.0: Cost Optimisation: As mass production became prevalent, organisations sought ways to optimise costs. Standardisation, economies of scale, and strategic sourcing played a crucial role. This phase aimed to maximise value while minimising expenses.

The average procurement department also achieved an annual saving of 6.7% in the last reporting cycle, sourced 52.6% of its addressable spend, and has a contract compliance rate of 62.6%.

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Procurement 3.0: Automation and Digitalization: With the advent of robotics, automation, and computerized systems, Procurement 3.0 emerged. Manual labour decreased, processes became more streamlined, and e-procurement platforms facilitated transactions. Data started accumulating, setting the stage for the next leap.

Procurement 4.0: The AI Revolution

?Data Utilization: The vast data generated by e-procurement systems holds immense potential. AI algorithms can analyse this data, uncover patterns, and provide actionable insights. From spend analytics to supplier performance, AI transforms raw information into strategic intelligence.

Predictive Analytics: AI-driven predictive models forecast demand, supply chain disruptions and market trends. Imagine having a crystal ball that anticipates risks and opportunities. Procurement professionals armed with predictive analytics make informed decisions, mitigating risks and seizing advantages.

Cognitive Procurement: AI isn't just about automation; it's about cognitive capabilities. Natural language processing (NLP) enables chatbots, virtual assistants, and smart contracts. Conversations with suppliers, contract reviews, and compliance checks become more efficient and accurate.

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How Innovators and early adopters are using this technology:

According to a 2021 report by McKinsey, businesses that use AI in their procurement processes can reduce costs by up to 20%. Additionally, they can achieve 10-15% improvements in efficiency and a 30% reduction in supply chain disruptions.

Source-https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution

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Walmart's Supply Chain Revolution: Walmart leverages AI-driven demand forecasting to reduce stockouts by 30% and improve inventory turnover by 20%. These gains enhance customer satisfaction and contribute to a sustainable supply chain1.

Apple's Supplier Relations: Apple ships over 400 million products annually, with iPhones dominating the lineup. In 2022, 98% of Apple's direct spend for materials was influenced by AI-driven insights, ensuring efficient supplier relations.

Source- https://www.apple.com/supply-chain/

MTN: The telco has been using AI extensively in its procurement and supply chain functions for US$ 12 Billion+ telecom sourcing annually. GSSC, its procurement arm, implemented award winning AI solutions in identifying supplier risks for more than 15000 suppliers, accurate demand forecasting, ESG compliance, negotiations with suppliers and automated contract analytics. ?The organization is also well known for implementing AI in recognizing supplier risks well ahead in context of geo-politically volatile markets or disruptions.

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Procurement 4.0: A Marriage Between Data Science & Procurement

Procurement 4.0 represents the convergence of traditional procurement practices with cutting-edge technologies. Let's explore how AI fits into this landscape:

1. Automating Category Management: AI, including generative AI (genAI), accelerates category management. Spend categorization algorithms create cleaned spend cubes seamlessly, while demand forecasts become more accurate. Gen AI interfaces allow procurement leaders to query data, uncovering insights related to climate events, geopolitical risks, and alternative sourcing options.

2. External Drivers of Profitability: By combining internal data with external market reports, AI helps procurement teams analyse commodity price trends. Machine learning algorithms uncover patterns, enabling timely decisions and cost-cutting opportunities.

3. Supply Market Analysis and Strategy Optimization: AI identifies high-risk sources and automatically finds alternatives within defined parameters. Machine learning analyses usage patterns, generating sourcing scenarios based on inventory intelligence.

4. Gen AI in speeding up paperwork: The procurement function deals with a lot of documents, including RFx, contracts and Purchase orders (PO). A huge amount of human effort is in the form of creation, maintenance and updating of these documents. It was impossible to even summarize a text paragraph using old forms of machine learning. Today, Gen AI can understand the context of documents and do the paperwork, helping us speed up a lot of documentation.

How do you start?

The right data strategy is a key that open doors for AI?implementation.

Communication in procurement currently takes place through emails, processes in ERPs, meetings, presentations and memos. Humans have adapted to these forms of communication for decades or centuries. However, machines invested in the last 50 years may not understand discussions happening in these ways until we give them context. This context must be created digitally by bringing different forms of data in procurement (proposals, contracts, purchase orders) together and connecting them so that they start “talking” with each other.

"Not having a data strategy is analogous to allowing each person within each department of your organization to develop their own chart of accounts and use their own numbering scheme” – Sid Adelman, Data Warehousing Expert (Adelman, Moss, & Abai, 2005)

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This article was written by Riyaj Shaikh, who is part of a leading team of super data scientists at GSSC- ?a procurement arm of MTN Group based in Dubai. ?

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