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Spend analysis data in procurement planning


Spend analysis data is one of the three data elements necessary for an effective procurement plan. After undertaking a market analysis to understand the market in general in relation to your needs, there is a need to analyse how goods, services and works had been historically procured by the organisation.


The two pieces of information help in identifying the needs of an organisation that form the procurement plan.

Historical data on what was spend is necessary to shape what the future requirements are most likely to be. Without the visibility of spend data, it would be very difficult to focus accurately purchases for the future.

Spend analysis is the process for analysing the organisational historical spend, excluding expenditure associated with salaries. The analysis will then disclose issues around spend visibility, contract compliance and control.

The activity is very essential in that spend analysis promotes the alignment of the organisational procurement strategy with the overall corporate strategy. This alignment allows the attainment of the corporate vision.

The procurement practitioners should gather the spend data to inform all future procurement plans and strategies, so as to ensure upcoming contracts leverage significant benefits for the organisation, adds value and allows the recognition of savings, where possible, as well as innovation where it can be achieved.

Some define spend data management as the process of aggregating, classifying, and leveraging spend data for the purpose of reducing costs, improving operational efficiencies and ensuring compliance.

They identify five attributes of effective spend data analysis programmes as extraction of spend data from internal and external sources; validation to ensure accuracy and completeness; cleansing and classification to eliminate errors and discrepancies; enhancement of data with related business information and analysis with advanced analytics.

There are general principles that must be followed when collecting spend data.

It must be timely, accurate and compliant with minimum data requirements for the organisation. Executives ordinarily trying to avoid costs, and to counter that, most systems mandate structured procedures for fiscal accountability, documented business controls, procedures for tracking compliance and auditing. Insight into accurate spend data will be vital for compliance, with such regulations. The spend data should be capable of being applied in multiple business areas.

In addition to the link between spend data and supplier management, accurate data is critical for other business objectives such as inventory management, budgeting and planning, compliance management, and product development.

There is, however, a crisis around spend data despite its critical role in supply management strategies.

Common challenges around spend data management and analysis is caused by disparate data sources; inaccurate or incomplete data; incongruent vendor, product and service naming; labour intensive data cleaning and classification processes; insufficient commodity expertise and limited analytics capabilities.

Disparate data sources arise from the reason that data is located in multiple systems across the organisation that includes accounts payable, the general ledger, the enterprises resource planning (ERP) and other business systems such as procurement and stores management.

The information is also available from other systems external to the organisations such as trade associations, financial institutions and other government departments such as tax departments. Aggregation of such data has always been historical, manual and time consuming.

The challenge of incomplete or inaccurate data emanates from the fact that most ERP systems were designed for transaction processing and control and not for reporting and analysis.

The data necessary for spend data analysis is often found in unstructured data within the ERP and other business systems. Such information is often prone to errors or missing critical fields necessary for data analysis.

Most systems have challenges with respect to incongruent names, where one institution such as the State Procurement Board may be addressed in the same system as SPB, or S P B or S.P.B, resulting in numerous entries being created for a single vendor.

The same applies to products and services, where one item may be called by different names resulting in multi-codes for one item. All these scenarios cause a challenge on data analysis.

Efficient data analysis is achieved when appropriate applications are used to aggregate and classify the data. Most institutions use basic spreadsheets that provide insufficient insight and often inaccurate analysis resulting in fragmented procurement strategies that fail to leverage the firm’s purchasing power.

This situation is coupled by the lack of experts within organisations resulting in limited analytical capabilities.
There is, therefore, need to put emphasis on the systems that should collect procurement data for the organisation.

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