Using Big Data to Avoid Value Chain Planning Mistakes
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Using Big Data to Avoid Value Chain Planning Mistakes Document Transcript

  • 1. Using Big Data to Avoid Value ChainPlanning MistakesAuthorSalil AmonkarDirector Consulting Services, Bodhtreesamonkar@bodhtree.comCo-AuthorManju DevadasVP Consulting Services, Bodhtreemdevadas@bodhtree.com
  • 2. Take a well-known brand and check the relevant social mining data: Facebook Likes, 5-star vs. 1-start ratings, blog comments with the words ‘hate’ or ‘awesome’ in the same sentence as ‘brand X,’ or the words ‘consistent’ and ‘flaw’ as part of one phrase in a user review. The chances are that the data are telling you something about perceptions of product quality, comparisons to competitors or the reach of a social marketing campaign. But how does all of this relate to Value Chain Planning? Consider the case of after-sales support. Most interactions between support and the customer are unstructured – telephone conversations, video tutorials, user forums, etc. – and therefore not easily handled by traditional data capture and management solutions. Consequently, invaluable insights to customer experience never make it up the value chain. If captured and analyzed effectively, these interactions could trigger improved reliability, product ease-of-use and effective prioritization of R&D investments. Business Performance Management illustrates another angle of the potential value unstructured data offers to Value Chain Planning. Signals from social and internal data mining reflect Demand, Supply and Pricing behaviors embedded in all aspects of the value chain. Most companies today rely on manual human analysis to factor in these elements, predict demand cycles and decide product pricing, often resulting in extreme misalignment between supply and demand. But can enterprises leverage machines for this planning rather than requiring humans to sort, search and analyze the unstructured data? Automating strategy is a game changer, and it has become a reality with Big Data. With today’s technology, it is not only possible to capture and manage unstructured data but also run language analysis processes that can distill the essence of the customer’s meaning (i.e. emotive component). This aspect is missed by traditional structured data analysis.Using Big Data in Value Chain Planning
  • 3. In the support example above, a simple usability or quality issue may never be identified and reported to Value Chain Planning, leaving vulnerability for competitors to exploit. The silos inherent to the traditional data approaches fail to “connect the dots” that convey the full story of the issue or opportunity being faced. Figure 1 Traditional Data Analysis Challenge The two key factors in Value Chain Planning, demand and supply, rely on price, or perceived value, to maintain their delicate balance. Human and technological advancements are constantly in a race to optimally balance Demand, Supply and Price, a premise referred to as the DSP problem. Today’s enterprise resource planning solutions approximate demand using structured data and historical information. By augmenting this data with customer preferences from unstructured sources via Big Data Analytics, a far more accurate predictive model emerges for Demand Planning. This is illustrated in the figure 2.Using Big Data in Value Chain Planning
  • 4. Figure 2: The Big Data Effect on Demand Analysis and Enterprise Supply Chain Why is Big Data Needed in Value Chain Planning? Automotive Recalls – Leveraging Big Data to Avoid or Mitigate a Costly Business Event The recent past offers multiple examples of automotive recalls that cost manufacturers billions of dollars and required several years to resolve. A consistent element among these impactful events is the reactive nature of the recalls, creating a multiplier effect on cost. Every automobile has a prescribed service schedule with regular maintenance visits. During these service interactions, maintenance centers download large amounts of data from the car’s sensors. However, in today’s structured data management environments, the total information captured is so enormous and unwieldy that it generally remains at the local level and is never utilized by manufacturers to gain insight to the fleet’s overall status, including any emerging safety and reliability issues.Using Big Data in Value Chain Planning
  • 5. But this sensor data is not the only feedback channel available to automobile manufacturers. Customers, service workers and industry experts create millions of posts on social media, product reviews, and blogs expressing their perspectives and preferences surrounding the product. Traditional data analysis approaches are not equipped to analyze these massive quantities of unstructured data. The Potential of Big Data/Data Mining and Supply Chain Management Gartner and other research firms have stated the following with regard to data mining and social media: • Using data mining and social media for returns, customer service, feedback and collaboration holds the key to improving value chain ROI. • Using social media to analyze procurement community reviews and reactions holds substantial promise for effective procurement within the supply chain • Companies using Big Data analytics for Supply Chain Management have shown consistent improvement in Supply Chain performance. Big Data offers multiple routes for Business and IT to navigate the vast quantity of information involved in capturing these opportunities and provides actionable insights for quick wins that validate long-term strategies. Information that has traditionally remained in silos (either corporate or across the value chain) can be aggregated to provide an end-to-end real-time view of value chain processes, unlocking latent potential for revenue increase and cost-savings leading to higher profits. The Big Data Difference Extending the automobile recall case study, the following scenario illustrates how Big Data can be pivotal in altering the costly progression of events. The enormous amount of structured information downloaded at each service center could be combined into a Big Data management solution and processed with Big Data analytics. Additionally, the unstructured social media information from customer reviews, service blogs, social media posts, etc. can be captured into the same Big Data system revealing customer preferences, perceptions and other expectations important to predicting demand cycles.Using Big Data in Value Chain Planning
  • 6. The structured portion of the data can be used to uncover quality trends, especially during the period following a new model or design. These insights enable the manufacturer to proactively prevent or mitigate issues that could otherwise result in massive recalls several years down the road. Another recent report describes how a well-known worldwide fast food chain franchise applied a similar solution to proactively maintain product quality among it’s more than 34,000 restaurants worldwide (See the article cited in the reference section for the full case study, including success metrics for the Big Data Strategy). This is illustrated in Figure 2 shown earlier and reproduced below:Using Big Data in Value Chain Planning
  • 7. Other Relevant Use Cases for the Big Data Insights A similar solution can be used to determine customer preferences – invaluable feedback, which can be incorporated into product design and engineering to improve desirability and expand the target market. The same customer preferences can be fed into Big Data analytics to more accurately predict demand cycles and determine the impact on the DSP problem. Usage of Big Data in the Solution What is Big Data? In very simple terms, Big Data is the enabler that helps you find the proverbial needle in the haystack of your universe of structured and unstructured information. Big data refers to very large data sets that are challenging to store, search, share, visualize and analyze and need methods different than traditional databases to capture and manage. The three key components of big data are as follows: • Big Data Storage and Management system, which is typically based on distributed file management system such as one provided by Hadoop (also referred to as HDFS), the associated components for data services management (such as Cloudera Manager) and retrieval (such as NoSQL which stands for Not Only SQL) • Big Data integration mechanisms such as Big Data Connectors, Big Data Loaders and Big Data integrators • Big Data Analytics including Statistical Analytical engines (such as Project R for Open Source based analytics and Oracle R which is provided by Oracle) To illustrate how Big Data would make a difference, we will compare the traditional Supply Chain Management approach to the Big Data-enabled solution. Traditional Approach Traditional solutions use structured data with rigid algorithms to roll up information and produce guidance. These solutions are generally incapable of processing massive information quickly. These usually adhere to the following solution strategy: • Map structured sources of information • Gathers data only provided by partners in supply chain • Analyze • CommunicateUsing Big Data in Value Chain Planning
  • 8. Big Data Approach Big Data enables the mash-up of both structured and unstructured information with the ability to run patterns and statistical models to derive actionable intelligence quickly. Big data solutions apply the following general approach: • Get structured information as in traditional approach • Add unstructured data feeds from value chain • Mash-up this data • Structure mashed-up data using new analytical methods • Mine data for subtle insights far-reaching business impact • Feed information to drive business actions via dashboards and reports In the automotive example above, we would use the mash-up of the information from unstructured and structured channels to determine that a specific sensor pattern was associated with customers reporting engine issues and that both could be linked to gaskets produced at a specific plant in Asia. In its simplest form the solution will use Social Mining and Big Data to determine patterns of consumer behavior across geographies. These patterns can be fed as input to scenarios for demand-supply balancing solutions to determine the most profitable strategies. This is illustrated in the figure 3 below Figure 3 Big Data Implementation ArchitectureUsing Big Data in Value Chain Planning
  • 9. Big Data-Driven Value Chain Planning Solution Approach and Benefits: • Determine value chain unstructured information feed and inventory all unstructured information to be collected from the value chain • Architect Mash-up with traditional structured information: determine the Big Data architecture to bring in the unstructured information and mash it up with the structured information • Statistical patterns determination and data mining – set the patterns required to alert stakeholders to key value chain disruptors (quality-related such as contamination and cost- related such as a holdup in supply chain node) • Proactive crisis management – continuously monitoring the possibility of crisis and taking steps to preempt it, quickly capturing the magnitude of an unfolding crisis to rapidly respond, diffuse and assist. • Keeping tabs on the competition – understanding gray market tactics used by competition to get access to sensitive information Big Data-Driven Approach Addresses These Key Steps: • Simplify and scale your mountain of content. The Big Data platform will help you gather and manage huge quantities of structured and unstructured value chain information • Identify the needle in the haystack of content. Track trends over time and use them to identify important business-impacting patterns. • Lighten your load when it comes to reporting. This platform can aggregate your data so you can easily extract the information you need. Recommended Approach for Success By combining the experience of multiple Big Data implementations with a deep understanding of Value Chain Planning solutions we enable the following: • Determine the right approach for the problem at hand • Architect the Big Data solution in the most seamless and incremental way • Proof of concept for feasibility and establishing confidence in the solution • Incremental roadmap approach that seeks to minimize risk and helps achieve the business benefits incrementally over time The author and his peers bring thought leadership and expertise in both Value Chain Planning solutions and Big Data solutions. Typically they can review the unique situation at your enterprise and put together individual ROI and business value cases, as well as assess how wellUsing Big Data in Value Chain Planning
  • 10. the value chains are aligned with the current business needs. This approach will consider how to modularize it for your business model and to feed the final result into your tactical demand planning, supply planning or pricing applications. Conclusion The core of Big Data analysis is the ability to find the most accurate answer, faster and with minimal cost irrespective of data volumes. Value chain optimization involves finding answers to specific problems that affect your supply and demand well before the actual impact occurs, allowing corrective action with minimal delay. With volumes of data growing for social mining and the machine memory and processing power becoming cheaper, getting inputs to feed into your demand planning (e.g. Oracle Demantra, i2, SAP DP) and supply planning (Oracle ASCP, SAP APO) applications is bound to become critical in the coming years across industry verticals. Executing this process systematically to drive real-time optimization will differentiate the leaders. While the automobile industry served as the topic for the current discussion, the same concepts can be widely adopted across industries and verticals. Unlike traditional approaches that use only structured supply chain management solutions, leveraging Big Data in combination with Value Chain Planning solutions provides the ability to avoid costly mistakes that can directly contribute to deficiencies and/or limitations in the Value Chain Planning process. The same solution can be augmented with Social Mining to make profitable revenue decisions by validating market insights with scenarios for Demand/Supply balancing. References and acknowledgements Gartner’s predictions for Big Data Gartner’s articles on Value Chain Planning and Management From the mail to McDonald’s: Big Data is all around us http://fcw.com/articles/2013/03/12/big-data-all-around.aspx?m=2 In addition the authors would like to thank Krishna Guda (CEO of Bodhtree) for his tremendous insights, support and guidance around usage of Big Data in improving Value Chain Planning.Using Big Data in Value Chain Planning