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Leveraging Your Data Report

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Electrical distributors have been collecting data on product sales and customer orders for years now. But, technology now allows for the collection, synthesis and analysis of information like never before. Under the guise of Big Data, many industries are planning and even projecting outcomes. Most distributors are only utilizing ERP data, but at what cost? This white paper walks through how members of the electrical distribution channel can plan and execute big data projects to maximize not only sales, but also stock, logistics and customer satisfaction.

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Leveraging Your Data Report

  1. 1. EDUCATION & RESEARCH FOUNDATION Leveraging Your Data FRANK LYNN & ASSOCIATES
  2. 2. EDUCATION & RESEARCH FOUNDATION Overview Collecting and studying data may provide a path to greater business success. What does that mean for electrical distributors? This project aims to define, assess and recommend further action regarding data and analytics by: Profiling the current state of the data analytics market, specifically in the electrical wholesaling market, but broadly in other markets. Assessing the benefits, costs, barriers, risks, and timing to electrical distributors considering the use of data analytics. Evaluating the potential implications of“big data.” Examining new revenue-generating businesses based on data analytics. Recommending the best ways for electrical distributors to proceed with data analytics.
  3. 3. EDUCATION & RESEARCH FOUNDATION Defining “Big Data” The term“big data”was first used by NASA scientists in 1997. Experts struggle to define it precisely, but it essentially means“information whose size and complexity can provide novel insights, but is beyond the ability of typical software tools to capture, store, manage and analyze.”Industry analyst Doug Laney described big data in terms of the“three V’s,”with a fourth being added later. Source: Manyika, James, and Chui, Michael, et. al. ”Big Data: The Next Frontier for Innovation.” McKinsey & Company. 2011 Mayer-Schönberger, Viktor, and Kenneth Cukier. “Big Data: A Revolution That Will Transform How We Live, Work and Think.” London: John Murray. 2013. Velocity Variety Volume Veracity • Consistency • Completeness • Precision • Timeliness Real Time, Streaming Near Real Time Periodic Batch Table MB GB TB PB Database/ERP Photo Web Audio Social Video Mobile Sensor Unstructured:
  4. 4. EDUCATION & RESEARCH FOUNDATION DefiningDataAnalytics DESCRIPTIVE ANALYTICS “What has happened?” Uses basic statistics, visual presentations and data aggregation to provide insight into the past. Often uses averages, totals or frequencies and perhaps causal relationships. The most common type of analytics in use today. PREDICTIVE ANALYTICS “What could happen?” Uses statistical methods to forecast business outcomes such as revenue, profit, market share or operational results. Relies on modeled relationships between a set of independent variables to project past trends into the future. PRESCRIPTIVE ANALYTICS “What should we do?” Uses optimization and simulation algorithms to take into account new inputs or constraints unique to a given situation. It predicts multiple possible futures and helps decision-makers chart a course to the best possible outcome. This is complex work, not used frequently. COGNITIVE ANALYTICS “What else should we know?” Relies on computers learning from experience to generate hypotheses, recommendations and self-assessed rankings of confidence. Considers context. Cutting-edge, think IBM Watson on Jeopardy or helping doctors to make difficult diagnoses. ED RE
  5. 5. EDUCATION & RESEARCH FOUNDATION How Big Data and Data Analytics (Should) Work Analytics Share results with executives. Allow executives and front-line staff to interact with models. Develop strategies, programs. Implement strategies, programs. 11. 12. 13. 14. Action Identify the big problems and opportunities facing the company. Prioritize. Generate hypotheses, potential strategies. Determine what data is necessary to test hypotheses, refine strategies. Develop algorithms/models to test hypotheses, identify unseen relationships. Revise, improve models. Develop predictive, optimization models. 1. 2. 3. 7. 9. 10. Determine if relevant data is available. Collect, clean, merge data. Create pilot. Run models against data sets. Add program results to data sets. Engage front-line staff in hypotheses generation. 5. 6. 8. 15. 4. Big Data
  6. 6. EDUCATION & RESEARCH FOUNDATION Benefits of Big Data Cost Reduction Faster, Better Decision-Making New Services Previously unknown operational efficiencies Productivity insights More accurate predictions Avoiding“wrong” decisions Analysis of real-time data Faster time-to-market, response times Analysis of previously unexamined data Greater customer intimacy leading to higher customer service and more targeted marketing and sales Supporting others’(e.g. contractors, end-users) uses of big data and analytics Use of big data and analytics to provide monitoring, optimization And other services Internet-of-Things (IoT)-based services
  7. 7. EDUCATION & RESEARCH FOUNDATION The ROI of Big Data and Analytics ROI is extremely difficult to measure for big data and analytics projects. In a recent study researchers found that only 3% of respondents could quantify the ROI business case for big data analytics, while 47%could not and 9% reported“no clear vision.” However, from a P & L (profit and loss) perspective, over half saw a 1-3% increase in revenue and a similar decrease in costs.
  8. 8. EDUCATION & RESEARCH FOUNDATION The ROI of Big Data and Analytics * Source: Rogers, 2015 Impact of Big Data and Analytics on Revenue* 40% 35% 30% 25% 20% 15% 10% 5% 0% 3% or more 1-3% < 1% no gain Don’t know Impact of Big Data and Analytics on Costs* 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 3% or more 1-3% < 1% no gain Don’t know
  9. 9. EDUCATION & RESEARCH FOUNDATION Distributors/Mfgs Level of Impact Over the next five years, what level of impact do you think Data Analytics/big data will have on your company’s ability to achieve the following objectives? Source: NAED, Frank Lynn & Associates, 2016 70% 60% 50% 40% 30% 20% 10% 0% %ofRespondents Improve strategic decision-making Improve efficiency Improve processed Improve/optimize inventory turns Very strong impact - Distributor Very strong impact - Manufacturer Strong impact - Distributor Strong impact - Manufacturer Modest impact - Distributor Modest impact - Manufacturer Little or No impact - Distributor Little or No impact - Manufacturer
  10. 10. EDUCATION & RESEARCH FOUNDATION Use Cases in ED Today ANALYSIS OF COST/ PRICING VARIATION BY BRANCH • Collected and “cleaned” ERP data for selected SKUs for all branches • Evaluating variations in acquisition cost and price charged to customer in each branch • Using analytics to tease out underlying causes of variation ASSET/FLEET UTILIZATION AND MARGIN ANALYSIS • Combining data from ERP system, onboard vehicle sensors and handheld scanners • Evaluating routes, schedules, loads and number of vehicles used • Goal is to optimize* trips and maximize product gross margin per trip PROFIT ANALYSIS BY CUSTOMER TYPE • Using a broad swath of ERP data, coded by customer segment • Identifying situations, segments that stand out in terms of higher margin • Creating algorithms based on that data to predict where prices (and margins) can be safely raised in the future, e.g. “D” items to industrial buyers *Optimizemeansusethefewesttrucks,travellingtheleastmilesdeliveringthegreatestgrossmargin loadswhilemeetingpromisedcustomerdeliverytimes/specifications
  11. 11. EDUCATION & RESEARCH FOUNDATION 6 Steps to a Data Analytics Strategy 1. Define your business strategy 2. Prioritize your analytical needs – what are your key“use cases” 3. Determine data availability, quality 4. Assemble your team, tools 5. Ask the right questions 6. Get the frontline staff engaged STRATEGY
  12. 12. EDUCATION & RESEARCH FOUNDATION Download the full report at naed.org/leveragedata EDUCATION & RESEARCH FOUNDATION

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