How data driven decisions can enhance procurement profitability

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The use of data-driven decision making drives sustainable procurement-value in the long term. However, there are different levels of maturity when it comes to handling spend data. Sourcing & procurement organizations can be classified into four categories of maturity levels:

Data denial: Organizations distrust the spend data residing in their systems and avoid using it. This could happen due to various reasons, including improper or non-existent spend classification systems, legacy systems, messy data, etc.

Data indifference: Organizations ignore the data and feel that they have no need for it. This could be a result of disintegrated systems or legacy systems being used for sourcing, contract management, procurement and accounts payables, due to which it becomes difficult for them to arrive at the true category spend. Spend data collation, cleansing and classification might be considered as a long drawn, time-consuming process. Hence, decisions tend to rely more on intuition rather than the spend data which exists in various silos.

Data informed: In the words of Ronald Coase, “If you torture the data long enough, it will confess to anything.” Even if the spend data and analytics processes are valid, the data may be deliberately presented in a misleading manner to support a pre-decided agenda.

Data driven: W. Edward Deming said, “In God we trust, all others must bring data”. Data driven organizations follow this to the core. These organizations value category management and sourcing & procurement decisions that can be backed up with verifiable spend data which is considered as a valuable asset.

The success of the data-driven approach is reliant upon the quality of the data gathered, the effectiveness of spend analytics and interpretation. Data should be converted into information which provides valuable actionable insights for category and sourcing managers, facilitating decision making. The process for doing so would comprise of the following five broad steps:

•  Understand the objective for which the data is being collated. Identify the spend areas/processes which would be impacted •  Create an analytics plan which would include the fields of spend data required (scope of required data), cleansing & classification requirements, systems/tools in which the data resides (source/s of required data)
• Coll ect & collate the data, carry out cleansing and harmonizing activities
• Gather insights as per the objectives of the exercise, create visualizations which would emphasize the message rather than distract from it
• Make recommendations supported by insights through spend data analytics

However, integrating massive amounts of data from different sources of spend (such as contract, PO and invoice data) and combining it to derive actionable information in real time, is easier said than done. Errors can creep into data analytics processes at any stage of the process. The data could be incomplete, inaccurate, not current, or may not be a reliable indicator of what it is intended to represent. Hence, in case spend data seems to be indicating something that doesn't make logical sense or just seems wrong, it's time to re-examine the source data and the methods of analysis.

Having comprehensive and reliable data analytics, enables informed & better decision making in areas (not limited to) such as:

Purchasing power: Analytics helps buyers gain a competitive edge by providing insights on historical spend patterns, forecasting, opportunity assessment, supplier profiling and competitiveness, conditional discounts and alternate bid comparisons. For example - a potential service provider can present two pricing options based on transaction-based pricing and fixed pricing. As a buyer, you would need to have insights about the historical spending pattern and the future volume forecasts to arrive at the best pricing option for your organization.

Improved spend management: Spend data analytics can provide information on how (procurement mode), what (material/service, quantity, price), where (location), from whom (supplier: preferred/non-preferred) you are buying. This helps provide insights on your buying process efficiencies and also on how compliant you are to the procurement policies.

Supplier & item rationalization: Analytics can help in determining the optimal mix of material/service-supplier combination and therefore lead to a decrease in costs. The exercise would involve analyzing the spend data, specification standardization & categorization of suppliers (strategic, tactical, one-time) and then moving on to derive insights on opportunities the information presents.

Risk mitigation: Anomalies in the buying pattern can provide insights on possible procurement frauds, whereas supplier performance and risk data analytics can help firm up your sourcing strategies regarding alternate sources, if needed.

Robotic process automation: Process analytics can help identify tasks in the procurement process which can be automated thereby reducing human error and enabling procurement professionals to focus on more strategic tasks. Activities in areas such as vendor enablement, contract compliance check, duplicate invoice identification, helpdesk, etc. are some examples where automated assistants could help.

Let data driven decisions drive you to the path of new opportunities and new efficiencies, thereby increasing procurement profitability. So how does your organization utilize the data residing in your purchasing systems?

By Deepa M.K.  Principal Consultant-Sourcing & Procurement-Infosys BPO

The thoughts and opinions shared here are of the author.