Numerous cost-saving opportunities are lying across the company’s supply chain. For many procurement managers, the cost incurred by the organization are often inevitable. Also, they cannot quite figure out which expenses are avoidable and which costs are contributing to the business success. That is the exact reason why procurement and sourcing managers are increasing their dependency on spend analysis to collect, classify, and analyze expenditure data in order to increase procurement efficiency and cost-savings opportunity. Currently, all the major organizations are using spend analysis to improve efficiency, reduce maverick spend, and increase cost-savings. Companies have to look out for innovative tools and practices in spend analysis to increase their competitiveness and improve their cost structure. The rise of artificial intelligence (AI) is helping procurement organizations gain new insights to shape effective strategy with spend analytics approaches.
Companies resort to using historical data during their supplier selection process. However, using historical data alone would not be the best option to base a strategic sourcing decision. Using latest machine learning methods, procurement teams can run a spend analysis before, during, and after a sourcing event to evaluate supplier performance. With the use of AI, the organization can view produce demand breakdown, market analysis, cost component analysis, and supplier performance analysis even before a sourcing event begins. Extensive knowledge about supplier and market condition enables procurement teams to analyze spend patterns with selected suppliers.
Selecting the right supplier is just one of the process in the supply chain. The supplier performance has to be constantly monitored and evaluated to ensure that the relationship is adding value to the company. Spend analysis program focuses on analyzing the spends against suppliers in terms of individual suppliers, purchase volume, mode of shipment, and mode of payment. Since all data points are not expressed in absolute terms, with some in the form of semi-structured data, procurement teams need to turn to AI for procurement analytics needs. For instance, instead of manually going through purchase orders and invoices, AI and procurement analytics can help them determine inventory overhead costs and predict stockouts by looking at purchase orders and invoices.
Procurement organizations are continually monitoring their strategies to increase their efficiency. However, not everything goes as per plan. So, the strategic management process needs to incorporate an additional factor, risk. All modern analytical engines can accurately provide insight into past performance and predict future outcomes. However, what sets AI apart from traditional data analytics is its ability to include external factors to predict risks. Leading analytics platform can perform analysis by integrating factors such as market prices, commodity data feeds, and numerous complex data sets including financial risk scores, CSR scores, and third-party data sources related to risk. With such analysis, procurement teams can perform a robust spend analysis to ascertain spending limit with each supplier factoring in risk elements.
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