Essay Sample on Data Analysis Skills in Spend Analysis

Spend analysis involves the processes of collecting, classifying, cleaning, and analyzing data expenditure for the purposes of minimizing procurement costs, enhancing efficiency, and monitoring compliance and controls. Having strong data analytical skills is beneficial to an organization as it enables it to better plan its spending strategies and understands better the pricing proposals for suppliers. While the data is available in the systems, skills are still required to access and analyze it and manipulate it to provide business insights (Bozarth & Handfield, 2016). Mostly, spend analysis projects involve simple database applications that may be tacked via application of in-house solutions such as Microsoft Access, SQL, Cognos, and Business Objects among others. Smaller firms may prefer manual solutions or Excel applications among others.

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Why Data Analysis Skills are Important to Spend Analysis

Data analysis involves working with data to enable organizations to make better business decisions. It involves the use of techniques from a myriad of disciplines such as computer programming, statistics, and mathematics to draw conclusions that describe, predict, and improve business performance (Bozarth & Handfield, 2016). The data analysis skills are vital since they are applicable at every step of the spend analysis process in the following manner:

Identifying and consolidating the spend data. The data is stored and in a variety of places, i.e., using separate budget and accounting systems among departments. Each of these departments may have their own internal processes, i.e., general ledgers, accounts payables, financial software, and eProcurement systems. The skills are vital to identify the invoices and payments from all the sources and consolidate them into one central database (Tobak, 2016). This may involve converting the data into common formats, languages, and currencies while applying programs that are designed to accomplish this.

Cleaning the data. Data analysis skills would help to standardize information from various sources. This includes finding and correcting the errors in the descriptions and transactions while standardizing them for easy viewing. Standardization ensures proper comparisons of entries and the elimination of redundancies in the data.

Identifying the scope of the process of spend analysis. The skills would help to establish a minimal time period, i.e., quarterly, semi-annually, annually or multiple years that would be required to come up with an accurate picture of the current expenditures while still allowing for recurrent expenses.

Grouping or linking suppliers or expenditures for proper supplier management. The data analysis skills would be used with a tag or group designation to pull out and analyze prices, and turnaround times among other considerations (Tobak, 2016). Data analysis skills such as sorting skills would be important to identify the best suppliers and tag them.

The skills would also help to categorize expenses and see where exactly the money goes. They would help to determine whether money is well-spent on vital company aspects that include office supplies, direct and indirect expenditures, legal, marketing and travel and are appropriately categorized. This includes making a category for every major expense, including travel, personnel, legal, outsourced programming, manufacturing supplies, and office supplies among others.

Analysis of spending data. This involves the application of data analysis skills to reduce the number of suppliers in each category and negotiate the best contract deals per supplier after identifying what is spent per supplier (Tobak, 2016). They will help to keep the data up-to-date and make informed decisions about future spending.

Application of Six Sigma Methodology in Spending Patterns and Improvement

Six-sigma disciplines and tools are applied to spend management to identify the inefficiencies in the process, i.e., maverick spending, and contract leakage among others. The process later fixes them to become predictive and eliminate costs like maverick purchases linked to uncertainties. The six-sigma-spend management drivers include savings from its management process, maverick spending as a percentage of the total spend, consistency and predictability of the results by spend category on the basis of office supplies, MRO, IT services, and office equipment, and the percentage off the enterprise spend under management (Shaikh & Kazi, 2015). The DMAIC method is an example of a lean six-sigma method is applied to improve an existing business spending process. The main objective of the LSS and the DMAIC model is to increase the speed and effectiveness of the business processes while maintaining or improving the quality of spending processes within the organization (Shaikh & Kazi, 2015). The result is to increase revenue, reduce spending or improve collaboration within the spending chain. The elements of the DMAIC model include:

Define

This element of the six-sigma model helps to select a task or expenditure that needs improvement, carve it out as a project and highlight that is being solved with the task or expenditure (Shaikh & Kazi, 2015). It involves analysis of the expenditure to identify if it takes too long, eats too many resources, or costs too much for the organization. The step addresses the quality issues that result in repetitive expenditures.

Measure

The process involves outlining the steps required to accomplish a specific task within the shortest time, with minimal resources, and minimal costs. It increases the understanding of what it takes and who does what and when (Shaikh & Kazi, 2015). The key performance indicators are measured for the expenditure and metrics established to be sued to improve the business processes.

Analyze

The step allows for the identification of any gaps or problem areas while forming ideas for improvement and forecasting revenue increments, cost savings, and collaboration points.

Improve

The step involves choosing a path and implementing the established improvement to see if they work. Here, the steps are implemented, new processes tasted, and tasks re-sourced.

Control

This stage involves establishing control measures to ensure that the improvements to control expenditure keep taking place and the tasks stay improved.

Functional Areas of a Business that Should be Involved in Spend Analysis

Some of the functional areas of a business that should implement spend analysis include:

• Human resources (HR) department. The process of recruiting people into a company may be tasking and time consuming as the recruiting managers will have to ensure that they recruit someone with the appropriate skills, experience, and qualifications. Spend analysis processes and analytical skills among the recruitment managers would help to lower the costs of recruitment and increase the attainment of quality skills for companies.
• Accounting and finance department: This is the lifeblood of the company and resources are wasted when balancing accounts receivables, accounts payables, and payroll processing among other financial processes. Spend analysis would help to implement less costly and more efficient accounting systems that can easily be operated by accountants with analytical skills.
• Purchasing department: This department analyses the use of raw materials, equipment, machinery, and supplies. In some companies, i.e., the manufacturing company, the purchasing department is in charge of all the vital materials for production. Spend analysis will, therefore, enable the employees in the department to understand the costs involved in purchasing resources and establish solutions to minimize the purchasing costs and increase company revenue.

References

Bozarth, C. and Handfield, R. (2016). Introduction to operations and supply chain management. Ashford University Library (4th Ed).

Shaikh, S. and Kazi, J. (2015). A review of the six sigma (DMAIC) methodology. International Journal of Modern Engineering, Research (IJMER). 5 (2).

Tabak, R. (2016). Critical success factors for spend analytics. Aalto University School of Business. Retrieved from https://pdfs.semanticscholar.org/310f/1f7fc6c04e42350e48063fcf391138dad961.pdf