Accenture and MIT Alliance in Business Analytics Innovation Virtual Event. June 10, 2014

Transcription

1 Accenture and MIT Alliance in Business Analytics Innovation Virtual Event June 10, 2014

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3 Accenture and MIT Alliance in Business Analytics Innovation Virtual Event The first virtual event for the Consortium took place on June 10, 2014 and featured: A welcome and brief introductions, led by David Simchi-Levy, MIT The Analytics Journey, presented by Terry Hemken, ABI An Analytics Problem Statement, presented by Srinivas Reddy, P&G Consortium Input to the October Event Agenda, led by Brian McCarthy, Accenture Glimpse into Fraud Detection Research, presented by Abel Sanchez, MIT The following Consortium participants, speakers and facilitators attended: Gautam Bose, National Australia Bank Vicente Chimeno, Gas Natural Fenosa Damien Daly, Bank of Ireland Chris Drunsic, Nike Terry Hemken, Anheuser-Busch InBev Bob Palermo, Shell Giovanni Pepicelli, Enel Srinivas Reddy, Procter & Gamble Julie Trowbridge-Dillman, Travelers Abel Sanchez, MIT Leslie Sheppard, MIT David Simchi-Levi, MIT Andrew Fano, Accenture Brian McCarthy, Accenture 2

4 Accenture and MIT Alliance in Business Analytics Innovation Virtual Event Welcome and Introductions After facilitating introductions from Consortium attendees across the world, David Simchi-Levi opened the session by providing a brief history of the 18-month analytics research program between Accenture, MIT and industry leaders. The collaboration has resulted in twelve active research projects, many with a transformative effect on the sponsoring companies. To this end, he encouraged participants in the virtual event to think about problems or issues that they currently have which might be translated into future research initiatives or topics for consortium discussion. The Analytics Journey Terry Hemken of Anheuser-Busch InBev highlighted initiatives associated with his Business Intelligence team s major areas of focus, including big data, visualization, and insight. In order to manage data originating from commercial and wholesaler activities, surveys, social media, and other sources, Mr. Hemken s group leveraged Hadoop to establish a very flexible Data Lake architecture. In terms of integration, reporting is structured for Excel and SQL access, and a sandbox environment was created to protect live servers and their data. Visualization is a core priority for Mr. Hemken s team, particularly in support of establishing self-serve opportunities for business intelligence, and efforts to get rid of the PowerPoint frame of mind. For example, newly developed mobile reporting for the senior leadership team was so well-received that the effort was expanded to the next level of management within the organization. Participants in the virtual event were curious about whether the transition to mobile reporting was a top-down, or a bottom-up effort. Mr. Hemken acknowledged that it began as a mandate from senior leadership and was initially not widely adopted. As a result, his team subsequently made the effort to work with senior analysts and managers on providing more relevant output, and since then, the reports have been quite popular. Mr. Hemken also highlighted some of his team s efforts to drive insight through ABI s partnership with University of Illinois, which has culminated in the creation of Bud Lab. Students there are given access to ABI data, and they work with the business to drive innovation and insight. One success story resulting from this collaboration is the creation of an out-of-stock prediction tool. Using data from more than 20 internal systems, retailer point-ofsale information, and external data about, for example, sporting events and weather patterns, the team built a mobile app for field staff, who now use that information to work with retailers to ensure that they stock enough product for peak sales windows. At the end of the presentation, members asked for additional information about the configuration of ABI s BI team, which Mr. Hemken then described as a federated model, centralized on the technology side, and organized by vertical to support the business. One member asked about the metrics used to determine the success of BI initiatives, and Mr. Hemken responded that either cost savings or top-line growth must be built into any new endeavor at ABI. At another member s request, he shared the tactics he used to rapidly grow the organization s initial investment in his team and technologies, including an effort to acquire talent for Bud Lab. 3

5 An Analytics Problem Statement Srinivas Reddy of Procter & Gamble posed a current business problem to the group, one which could potentially be addressed through a cross-industry Consortium research initiative. Mr. Reddy is the Director of Engineering for Gillette and he works closely with Product Developers to design manufactur-able shaving care goods. He shared that innovation is the lifeblood of P&G, and while many new products are very successful, inventory management for these new market entrants can be challenging. Specifically, demand forecasting for new products is almost always inaccurate, and can lead to higher than desired inventories, supply chain inefficiencies, and at times, customer service issues. Demand forecasting is an iterative process with consumer testing, modeling and customer feedback. The forecast accuracy is improved usually after investments in expensive manufacturing equipment have already been made and it s also too late to affect inventory planning. In order to maintain high levels of customer service, P&G errs on the side of overstocking products, which can be costly. Another challenge is that forecasts must be accurate at the local level globally, since P&G products are bought and sold virtually everywhere in the world. Mr. Reddy described this type of issue as a wonderful opportunity for analytics to improve accuracy of prediction models, and/or response time with regard to consumer testing. He believes that demand forecasting does not need to be 100% accurate. Even a small, incremental improvement in the accuracy of the prediction of sales could have enormous cost savings for a company like P&G. Members from other consumer goods companies represented at the virtual event agreed that accuracy of demand forecasting is an issue with which they grapple. One noted that, We ve tried to drill into the accuracy ad nauseam, to the point where there are 45 million variables, so we re working to either simplify or perhaps get even more precise. David Simchi-Levi of MIT shared that his team had recently completed a study on forecast accuracy, combining internal with external data to make enormous gains in accuracy. Commenting on the crossindustry applicability of this issue, one member from a different industry said, When you re in market with a product, [you need to] quickly adjust and find the patterns, for example, to identify new customer sub-segments. 4

6 Consortium Input to the October Agenda Accenture s Brian McCarthy reviewed the topics discussed by the Consortium during and after the first in-person meeting that took place at MIT in March, including decision science, the digital consumer, and machine-to-machine learning, in order to identify relevant topics for the upcoming October Consortium meeting, to be held at the MIT campus on October 23rd and 24th, The group agreed that in terms of decision science and tools, the focus should be two-fold, including leveraging tools already in use, as well as better understanding emerging trends. One member offered that crowdsourcing is a new decision-making approach that members could potentially apply to corporate, as opposed to governmental, data. The group discussed the potential to evaluate decision-making tools at different phases of organizational development, including varying levels of adoption and maturity. Regarding the digital consumer, one member suggested that text analytics is a buzzword, but still immature, and could be a fruitful area of exploration, specifically with regard to analyzing social media and call center-generated content. Members agreed that in the machine-to-machine learning space, always on streaming data from sensors is the most relevant topic, with one offering that, the number of sensors and end devices is amazing and it continues to grow, so that ll be a huge focus. Another agreed that the internet of things is quite interesting, and something that we struggle with. Glimpse into Fraud Detection Research Abel Sanchez of MIT shared the results of an Alliance-sponsored research project on fraud awareness conducted by MIT and Accenture in collaboration with the Commonwealth of Massachusetts. The tool that the State had been using to detect fraud was a commerciallyprocured package that produced an avalanche of false positives on which the State could not follow up. Therefore, the goal of the project was to reduce false positives in the Commonwealth s fraud detection techniques. To identify true fraud events, the research team had to address the central challenge of detection, which is that the ways in which fraud is perpetrated are continually adaptive: People are smart and so pretty much everything you do, six months later, it s not going to work. The first thing the researchers did was to build a platform built on open source and open protocols, then created a first layer as a non-relational database. On top of that, MIT students built a number of packages to detect fraud, then created an HTML5 interface that is platform-independent, and web-enabled. Accenture supported the effort by providing real-life examples of fraud-detection techniques currently and successfully used in Ireland. Professor Sanchez highlighted the transferability of this type of tool to other industries and similar business issues: Those detectors can be switched on and they can be applied to many other industries or many other problems. 5

7 Next Steps Accenture and MIT have launched the Analytics Innovation Consortium website and chat room at: All Consortium participants are encouraged to register on the site and continue the conversation via the chat room. The next Consortium meeting will a full, two-day live event on the MIT campus on Thursday, October 23rd and Friday, October 24th,

8 About Accenture Accenture is a global management consulting, technology services and outsourcing company, with more than 293,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$28.6 billion for the fiscal year ended Aug. 31, Its home page is Copyright 2014 Accenture All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture