How do you use data and analytics to optimize your purchasing decisions?

1 Supplier selection criteria

Before you can make an informed decision about which supplier to choose, you must first define the criteria that are essential for your purchasing objectives. These criteria may differ depending on the type of product or service, the industry, the market, and the strategic objectives of your organization. Quality, cost, delivery, innovation, and sustainability are all common criteria to consider. Quality refers to how well the supplier meets your specifications and standards, as well as their consistency and reliability. Cost looks at the supplier's price and the expenses associated with acquiring, transporting, storing, and using their products or services. Delivery examines the supplier's timeliness and flexibility, as well as their ability to respond to changes in demand or supply disruptions. Innovation evaluates the supplier's product or service for its adaptability and support of product development and improvement initiatives. Lastly, sustainability assesses the supplier's environmental and social responsibility, and how well it aligns with your sustainability goals and values.

2 Supplier evaluation methods

Once you have identified the criteria for selecting a supplier, you must decide on a method for evaluating and comparing them. There are various approaches and tools available, each with its own advantages and disadvantages. The weighted point method assigns a weight to each criterion based on its importance, and then scores each supplier based on their performance on each criterion. The total score for each supplier is calculated by multiplying the weight and the score for each criterion, and then adding them up. Cost-benefit analysis, on the other hand, compares the total cost and the total benefit of each supplier, and then calculates the net benefit by subtracting the cost from the benefit. Lastly, the analytic hierarchy process uses a hierarchical structure to break down the decision problem into multiple levels of criteria, sub-criteria, and alternatives. It then uses pairwise comparisons to assign relative weights and preferences to each element, and then synthesizes the results to rank the alternatives. Ultimately, the supplier with the highest score, net benefit, or rank is the best choice.

3 Data and analytics tools

To make the most of these methods, it is essential to collect and analyze data from a variety of sources, such as internal records, supplier reports, market research, customer feedback, and industry benchmarks. Data and analytics tools can be used to gather and organize data from different sources and formats, like spreadsheets, databases, web pages, and documents. Additionally, these tools can be used to visualize and explore data with charts, graphs, dashboards, and maps, to detect patterns, trends, and outliers. Furthermore, they can be used to perform calculations and statistical analysis with formulas, functions, and models, to measure and compare performance, risk, and value. Finally, decision support techniques, such as optimization, simulation, and scenario analysis, can be used to test and compare different options and outcomes.

4 Data and analytics best practices

In order to make the most of data and analytics, it is essential to adhere to certain best practices. This includes defining the decision problem and objectives accurately, verifying the quality and accuracy of the data, utilizing consistent data formats, units, and scales, and avoiding duplication and inconsistency. Additionally, it is important to use reliable data and analytics tools that are compatible and interoperable. Finally, it is essential to interpret and communicate the results of data and analytics clearly, and to back them up with evidence and logic.

5 Data and analytics challenges

Data and analytics for supplier selection and evaluation can be a difficult process, as there are a variety of challenges to consider. Accessibility and availability of data may be limited, or the data may be incomplete, outdated, or confidential. Additionally, data complexity and diversity can be overwhelming, as large volumes of data, or data that is heterogeneous, unstructured, or dynamic, must be managed. Furthermore, data and analytics skills and resources may be lacking, making it difficult to collect, analyze, and use data effectively, or to select and operate data and analytics tools. Lastly, ethical and legal standards and regulations, such as data privacy, security, and ownership, must be adhered to.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?