Big Data in Supply Management: How Big Is the Need?

Introducing SSON's new Advisory Board member: Brish Bhan Vaidya First, what is BIG DATA? "BIG DATA" is not just a buzz word. It’s an integral part of the business, whether in identifying options to generate extra revenue for the organization or opportunities for cost transformation. Big Data at its simplest is defined as, "analyzing data of extreme size, from various sources, of various complexities and diversities, where the output is meaningful business information." It is said that every year the data available within any organization doubles, which means more analysis is required to generate information. As per a recent survey, by 2020, organizations will have 300 times more data available to them than they do today. The challenge will be to use it to its fullest potential, to generate revenue and transform costs. What is the need in Supply Management? According to research by The Hackett Group, Top Performers (i.e., companies that achieve top-quartile performance in a function-specific set of metrics) invest in and use certain techniques much more than typical companies do: 6.8x (580%) more top performers have adopted predictive modelling tools and processes, for example, and 3.1x (207%) more use multidimensional analysis. The challenge for any large organization that has set up across various countries and geographies is that there are multiple tools and systems, where the data is buried. Some of these tools and systems will be as modern as the latest versions of ERP, but there might also be legacy systems set up many years ago. Supply Management – or in simplest terms, Procurement – commonly struggles against this scenario, where, even after all process level changes are implemented, loop holes remain. As a result, even top level management does not know with 100% accuracy: the actual supplier base across the organization, with unique suppliers identified whether they are paying the same price for the same material or services across suppliers These challenges are hindering procurement’s efficiency and effectiveness. In fact, while over half of procurement organizations report that their analytics improve the quality of decision making and ability to identify opportunities for business value, 84% consider their current analytics process to be inefficient (see Fig. 1). This reflects the current difficulty of adequately engaging with the business using data and analytics. Indeed, a 2014 Hackett Group study confirmed that most procurement organizations are deficient in data analysis and modelling as well as strategic thinking and analysis skills. Analytics holds many of the keys to unlocking procurement’s ability to drive improved performance, consult the business strategically and drive innovation and change across the supply chain. It also has the potential to help procurement come into its own as a strategic partner to the business. Some of the major benefits that a Procurement organization can derive from Big Data include: new opportunities for cost efficiencies through Spot negotiations standardizing rates transparency resulting in better defining processes driving compliance and adoption of procurement policies by end users optimizing the supplier base, reducing risk and effective management of supplier base shorter procurement cycle times operational excellence from Planning to Pricing to Inventory Management How to use BIG DATA in Supply Management There is a lot of structured and unstructured spend data available in organizations. To get the best out of it, here are some guiding principles: write down the Key Performance Indicators (KPIs) that you would like to drive out of the information. Examples include: Spend in geography, spend with a particular supplier across the globe, Price for a particular material or service, Turnaround time, Compliance to pricing, etc. benchmark the KPIs based on various industry level reports or historical trending post analysis, look for trends, patterns, deviations, outliers, etc. get into brainstorming sessions both within the department and with the customers (internal & external) identify opportunities and capacities available, and prioritize them develop a plan and utilize the opportunities. Applied to procurement, Big Data can include information like supplier qualification and assessment details, performance data (cost, quality, service, innovation), market intelligence, risk indicators, spend details and contracts. This data is often dispersed across multiple sources and in areas like supplier risk management, and may include a vast array of alerts, news feeds, financial indicators, market reports, etc. Quality control is just one of the many difficulties of managing this much data. Applying Big Data to Procurement (Source: The Hackett Group Research) Area General business definition Application in procurement organizations Self-service analytics and reporting An approach to data analytics that enables business users to access and work with corporate information on a computing device without IT involvement. End users create personalized reports and analytical queries using technology tools, which may be Web-based, delivered through a portal, role-based, and/or support mobile devices. Develop reports and perform analysis on spend, supply base, requisition compliance and other spend-related data. Access may extend to end users, managers or suppliers. Scorecards/performance dashboards An easy-to-read, often single-page, real-time graphical snapshot of current status and historical trends of key performance indicators to enable instantaneous, informed decisions. Illustrate performance outputs of the procurement function on a given set of effectiveness and efficiency metrics. Predictive modelling A technique that models cause-and-effect relationships between variables based on statistical analysis of these variables. Predictive models are used to simulate different business scenarios (i.e., different sets of assumptions for the model variables) to support planning or to predict the most likely business outcomes based on actual conditions. Forecast spend or supply needs based on anticipated changes in the business and pricing based on external factors. Procurement may support supply chain on network design and optimization. Multidimensional analysis ("slice and dice") An analytical technique that allows data to be presented along different dimensions (e.g., sales by region, period, product, demographic) by the end user in a flexible manner within the analytical tool. May be applied to spend analysis in the form of a "spend cube" showing spend by supplier, geography and category. Risk analysis A technique for identifying and assessing factors that may jeopardize the realization of a business goal. This technique also helps define preventive measures to reduce the probability that these factors will occur and identify countermeasures to successfully deal with these risk scenarios when they materialize. Identify business risks related to the supply base. May include geographic risk, price risk, disaster risk, brand risk, compliance risk or any other factor which the business deems significant. Data mining The practice of analyzing large amounts of digitally stored data to find patterns or trends that offer meaningful business insight. May be applied to spend analysis to better understand spending trends; to supplier management to understand supply performance trends; or to pricing data to understand external pricing trends. Some Big Data Analytics Offerings in the Procurement Space Smarter Procurement from T-Systems is an end-to-end Big Data solution. Based on in-memory technology, it helps enterprises cut costs, shorten lead times and protect liquidity – throughout their organization, around the world. Emptoris Strategic Supply Management V10 provides sourcing and procurement professionals with additional help to optimize their business processes to control spending, enhance supplier performance, and manage risk and compliance. Conclusion One of the large consulting organizations recently called Big Data "the frontier for Innovation". It is now down to the CPO and senior leadership of organizations, how quickly they deploy the right tools for Big Data to start reaping the benefits, not just of a competitive edge, but in terms of changing the perception of Supply Management within the organization. [inlinead]

Big Data in Supply Management: How Big Is the Need?

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