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Data-driven Procurement strategies

As technology and data analytics capabilities continue to evolve, procurement departments in large companies are increasingly able to make more informed decisions than ever before. By analysing their data, companies can develop data-driven sourcing strategies that can revolutionise their procurement processes. In this article, we will walk you through what a data-driven procurement strategy is, illustrated with real-life examples and best practices. We will explore how companies can leverage their data assets to make more effective purchasing decisions and improve their bottom line.

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The benefits of data-driven procurement strategies:

1. Better decision-making

Data-driven procurement strategies enable procurement professionals to make more informed decisions by analysing data on spending patterns, supplier performance and market trends. This allows organisations to identify areas for improvement and make data-driven decisions that lead to better results.

2. Cost savings

Data-driven procurement strategies result in cost savings. By analysing spend data, organisations can identify areas of overspend and implement cost-saving measures. In addition, they can negotiate better contracts with suppliers and identify opportunities to consolidate suppliers, resulting in further cost savings.

3. Better supplier relationships

By assessing and analysing supplier performance, companies can identify areas for improvement and work with suppliers to improve them. This leads to better supplier relationships and improved collaboration, resulting in better prices, shorter delivery times and higher quality. Procurement professionals can identify potential risks in the supply chain and take action to address them, improving the company's productivity.

4. Increasing efficiency

Data-driven procurement strategies can increase employee efficiency by automating data processing and analysis, reducing the time and resources needed to manage procurement activities. Instead of manually collecting and processing data, procurement experts can focus on analysing the data to uncover insights.

5. Improved compliance

Data-driven procurement strategies improve compliance with legal requirements and corporate regulations. By tracking and analysing procurement data, organisations can identify instances of non-compliance and take corrective action, reducing the risks of non-compliance.

Key steps to implement data-driven procurement strategies:

1. Data collection and storage

The first step in implementing a data-driven procurement strategy is the efficient collection and storage of data. This involves collecting and continuously updating relevant data sources such as contract data, spend data, supplier performance data and market data. It also includes the implementation of systems (data warehouses) to collect and store data in a central location.

2. Processing data, ensuring data quality

A good data-driven procurement strategy is based on good quality data. It is very rare for a large group of companies to have a consistent supplier master data across the company and even rarer to have a consistent taxonomy for the items it purchases. For this reason, data processing must include the consolidation of supplier and invoice data. Advanced artificial intelligence-based data processing tools can be used to produce the consolidated supplier data and invoice data grouped by taxonomy needed to analyse spend in a single view. Once the master data is produced, there is no shortage of ongoing data quality control and data governance processes to ensure that the data remains accurate, complete and consistent.

3. Data analysis

Once the data has been collected, the next step is to analyse it to identify trends and insights. This involves the use of advanced data analytics tools to identify hidden patterns in spend data, supplier performance data, enabling procurement professionals to make data-driven decisions that lead to better outcomes.

By analysing the data, you can answer business questions such as:

  • how much we spend on the same supplier within the group,

  • what categories we buy from different suppliers,

  • which categories do we see savings potential in purchasing centrally for the group as a whole, which categories do we see savings potential in purchasing from one supplier?

4. Exploiting technology

Technology plays a crucial role in data-driven sourcing strategies. Procurement professionals need to harness the power of technology to automate processes, such as creating purchase orders or collaborating and exchanging information with suppliers more efficiently. Their IT developments should also address the efficient management and analysis of large amounts of data.

5. Monitoring key performance indicators (KPIs)

Procurement areas should establish KPIs linked to organisational goals and objectives to measure the success of the data-driven procurement strategy.

6. Building a data-driven culture

In order to ensure the success of data-driven procurement strategies, organisations need to develop their culture. It is important that decision makers understand the value and role of data in decision making. Professionals need to learn to use the tools needed to analyse data effectively. We need to develop a governance system within the company that ensures secure access to data, appropriate data quality and knowledge sharing about the meaning of data.

Real examples of data-driven procurement strategies

1. Coca-Cola

Coca-Cola has implemented a data-driven procurement strategy by analysing spend data and identifying cost-saving opportunities. They consolidated suppliers and renegotiated contracts, saving hundreds of millions of dollars over three years.

2. Procter & Gamble

Procter & Gamble implemented a data-driven sourcing strategy by analysing supplier performance data and identifying areas for improvement. They worked with suppliers to address identified issues, resulting in improved supplier relationships and supply chain improvements.

3. Walmart

Walmart has implemented a data-driven purchasing strategy by analysing market data and exploring ways to optimise inventory management. They used predictive analytics to forecast demand and optimize inventory levels, resulting in fewer rejects and higher quality to meet demand.

Data-driven sourcing strategies can revolutionise the procurement process and improve business performance. By adopting best practices and leveraging technology, organizations can efficiently collect, manage and analyze their data to make more informed decisions, reduce costs, increase efficiency and improve supplier relationships and regulatory compliance.

The real-world examples above also demonstrate the success that can be achieved with data-driven sourcing strategies. Want to know how Stratis can help your company cut procurement costs? Contact us!

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Source:

https://www.coca-colahellenic.com/en/about-us/what-we-do/supply-chain

https://marketplace.walmart.com/linnworks-automated-inventory-management/

https://www.scmr.com/article/Insiders-view-of-Proctor-and-Gamble-supply-chain-success

About author

Gaspar Sandor Szerzo
Sándor Gáspár

Head of the Artificial Intelligence Competence Centre

Analytics - Data Solutions

Gáspár Sándor has been leading Stratis' artificial intelligence division since 2020, bringing over 20 years of experience in data science. Together with his team, he develops machine learning-powered decision support solutions for large enterprises, leveraging our clients' existing data assets. Additionally, he assists in automating our clients' existing processes using deep neural networks-based NLP and machine vision solutions.

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