Data Analysis and Thinking

The effectiveness of a supply chain management does not solely depend on the traditional supply chain elements namely sourcing strategy, negotiation strategy, and category expertise as well as procurement implementation. In the big data era, the effectiveness of a supply chain management program is ingrained in the capacity to acquire, organize and carry out analysis on spend data. Timely access to accurate, complete, and comprehensive spend data provides valuable intelligence on cost patterns, compliance, and performance assessments. These insights are crucial to identify the savings opportunities, the creation of sourcing, planning, and product development strategies. Core to the usefulness of spend data are data analysis skills. There are various reasons why firms need to invest in data analysis skills as they seek to leverage the benefits of spend analysis.

The skills to analyze data in procurement creates an opportunity for companies to aggregate, cleanse, and link spend and supplier data across an organization. Data analysis skills can provide chief procurement officers with greater visibility and insights, enabling them to cut costs, ensure compliance, reduce risks, enhance business intelligence, and develop as well as manage suppliers effectively (Pandit & Marmanis, 2008).

The potential of data analysis skills in extracting the benefits associated with spend analysis is so great that the higher-level value proposition which is the focus of many organizations becomes a reality, be it tackling complicated expenditure categories, proactively reducing supplier risks or effectively checking the performance of suppliers. Overall, data analysis skills when applied effectively on spend data can assist procurement to provide more to an organization’s business strategically (Pandit & Marmanis, 2008).

The business environment today is characterized by rapidly increasing competitive and performance pressures. This trend calls for organizations to invest in analytical capabilities that will enable them to collect, cleanse, categorize, and analyze cost data with the objective of cutting procurement, budgeting, planning, and product development costs (Pandit & Marmanis, 2008). The research by American Management Association (2013) found that majority of organizations acknowledge analytical skills in relation to spending data as essential to the enhancement of accountability of results, gaining an upper hand in the competitive business environment, mitigation of risk, and increase in customer data. This study implies that competition, adaptability, and forecasting of risk which are among the core objectives of spend data analysis can be improved through investment in data analytical skills in an organization’s workforce.

The research indicates that data analysis skills are high among leaders, managers, and members of research and development team (American Management Association, 2013). Companies are better suited for overall market success when they utilize spend data to define and provide solutions to problems. This mindset must originate from the top leadership to provide direction for an organization and to reinforce analytical frameworks for understanding and formulating strategic decisions (Pandit & Marmanis, 2008). Due to this, strong data analysis skills and support among top leaders is required for developing a firm’s abilities to utilize spend data effectively.

A paramount benefit of spend analysis is improved decision making and reduction in costs. Better strategic decision making is the leading decision why organizations tap into big data. There is a promise of data analysis capabilities to improve the process of making decision. Therefore, there is a need to grow data analysis skills and capabilities to achieve this objective (American Management Association, 2013).

The research shows that transforming data into insight is so far the greatest challenge of big data. The tremendous concern is on the challenge to convert data into information, database management, security, privacy issues, legal issues, and storage. In other words, the barrier to successful use of big data, including spend data, is the scarcity of data analysis skills. Organizations must, therefore, consider empowering the workforce with data analysis skills (American Management Association, 2013).

How Six Sigma Methodology Can Be Useful to Identify Spending Patterns and Identify Opportunities for Improvement

The core component of Six Sigma methodology is the capacity to identify and rectify errors in design and minimize the amount of deviation in business processes. Six Sigma projects are based on a preset series of steps that have measurable financial objectives, for example, increase in revenue or reduction in costs (Bozarth & Handfield, 2012).

One of the defining features of Six Sigma is the support from top management (Pyzdek, 2003). The engagement of top management could be essential in identifying cost patterns and areas that need improvement. The rationale is that top management bears the responsibility for making critical decisions about expenditures and business strategies. Supplying top management with verifiable data can help them detect areas that warrant improvements.

One of the primary benefits that can be derived from Six Sigma methodology is the development of a Six Sigma culture in an organization. In the Six Sigma culture, key emphasis is laid on business process knowledge and improvements throughout an organization (Pyzdek, 2003). The Six Sigma Methodology constructs dashboards of valuations that assess and measure business processes on a routine basis, for example, in terms of cost of inputs and the value of output, and determines the ones requiring improvements and reduction in deviations. The concepts of Six Sigma can also be applied to the organization’s strategic goals. The Six Sigma culture created by the methodology’s focus on the process performance imparts the importance of quality and continuous improvement of business processes in an organization (Pyzdek, 2003).

Six Sigma Scenarios function as useful planning devices and aid in visualizing what process, cost, and quality events will arise in given different situations. Through the use of scenarios, Six Sigma experts are able to maintain a long-term focus. Scenarios are crucial in design phases, and they help to determine what a customer’s needs and wants are. They help in evaluating if a business process can achieve predictable and stable results, or if it needs to be overhauled to et a firm’s expectations (Pyzdek, 2003).

Functional Areas of the Business that Should Be Involved in Spend Analysis Efforts and the Rationale for the Choices

All functional areas of business that involve decision making should take part in spend analysis. Managers across all organizational levels require data analysis skills in the current big data age. Finance and accounting departments require spending analysis in order to explore particular areas of interest or trace payment specifics (Pandit & Marmanis, 2008). The procurement department needs spend analysis to determine major suppliers and categories of spending in order to identify opportunities for potential savings in the supply chain (Pandit & Marmanis, 2008). The marketing department also needs to be involved in spend analysis. Competitors collect data on a routine basis and analyze it to formulate competitive strategies in the market. To gain a competitive edge in the market, the marketing department must take part in spend analysis since this would enable them to identify areas of the existing marketing strategies than might need revamping, or the need to formulate new strategies (Pandit & Marmanis, 2008).

Most organizations have recognized that data analysis skills are the key to unlocking procurement potential to yield improved performance, strategy development, innovation, and transformation across the supply chain landscape, and other functional areas such as finance, accounting, and marketing. The core objectives of spend analysis are cost reduction, performance improvement, and compliance with contract and regulatory requirements. Analytical capabilities help in the attainment of these objectives by enabling aggregation; classification and control of spend data across multiple business areas, and formulation of competent strategic decisions.