Critical thinking case analysis. Case and questions attached
Dow Chemical Co.: Big Data In Manufacturing W17696 DOW CHEMICAL CO.: BIG DATA IN MANUFACTURING R. Chandrasekhar wrote this case under the supervision of Professors Mustapha Cheikh-Ammar, Nicole Haggerty and Darren Meister solely to provide material for class discussion. The authors do not intend to illustrate either effective or ineffective handling of a managerial situation. The authors may have disguised certain names and other identifying information to protect confidentiality. This publication may not be transmitted, photocopied, digitized, or otherwise reproduced in any form or by any means without the permission of the copyright holder. Reproduction of this material is not covered under authorization by any reproduction rights organization. To order copies or request permission to reproduce materials, contact Ivey Publishing, Ivey Business School, Western University, London, Ontario, Canada, N6G 0N1; (t) 519.661.3208; (e)
[email protected]; www.iveycases.com. Copyright © 2017, Richard Ivey School of Business Foundation Version: 2017-11-17 It was September 2012. At his office in Texas City in the United States, Lloyd Colegrove, data services director at the Dow Chemical Company (Dow), was reviewing the results of a pilot study initiated by his team in one of the company’s U.S. manufacturing plants. The six-month study consisted of testing a data analytics module being used at the plant’s research and development (R&D) lab for its applicability to the shop floor. Attached to a laboratory information management system (LIMS), the module collected and analyzed data, in real time, from instruments tracking the quality parameters of finished goods at the plant, and acted upon them to maintain high quality yields. The objective of the pilot study was to test whether the basic structure of the LIMS could be supplemented and extended, plant-wide, into a larger system known in the bourgeoning analytics industry as enterprise manufacturing intelligence (EMI). EMI was both a management practice and a software tool for manufacturing. It contained more sophisticated analytics than the LIMS and provided easier and faster aggregation of different data types. It also incorporated visualization tools to summarize key insights and post them on different dashboards for plant engineers to monitor and act upon. The possibility existed to scale up EMI company-wide, in all 197 of Dow’s manufacturing plants across 36 countries. However, Colegrove had several issues on his mind: The pilot [study] shows that plant engineers are working for the data; the data is not working for them. There is clearly an opportunity to reverse the trend through EMI. But the opportunity has also opened at least three dilemmas for me. How do we access data at the points of origin, [in] real time? Can we gain user acceptance of the proposed EMI and, if so, how? What are the metrics with which we could measure the return on investment on EMI? GLOBAL CHEMICAL INDUSTRY The chemical industry often served as a forerunner of the state of an economy, since its products were consumed at early stages in the supply chains of user industries. Its business consisted of processing raw materials to produce chemicals used, in turn, as raw materials by other industries. The business was cyclical, relying on basic commodities such as oil and gas, and volatile. Of the 20 largest chemical For the exclusive use of P. Saunders, 2020. This document is authorized for use only by Paula Saunders in ISM 6026 Summer 2020 OMBA Cases taught by JAHYUN GOO, Florida Atlantic University from May 2020 to Aug 2020. mailto:
[email protected] http://www.iveycases.com/ Page 2 9B17E014 companies operating 25 years ago, for example, only eight remained in operation in 2012. The rest did not survive for three reasons: they were not making incremental and necessary changes to stay competitive; they were not ensuring a regular pipeline of new products, and they were not investing in the regular generation of patents covering proprietary manufacturing processes. The global chemical industry was witness to a churn in relation to business models. By 2012, three models were evident worldwide. The first was characterized by ownership of resources such as feedstock (as the raw materials serving as inputs for the chemical industry were known). The companies following this model focused on securing low-cost positions through economies of scale since they used a large asset base to process largely commodity-like chemical products such as petrochemicals. The second model was characterized by niche positioning. The companies following this model were leaders in specific technologies and sought to protect their intellectual property through quality, innovation, and strong relationships with customers that purchased their niche products. The companies following the third model, such as Dow, were characterized as solutions providers. They understood end-to-end value streams in different user industries, developed strategic partnerships with customers to drive innovation, and responded to market changes faster than their industry peers.1 This space was getting increasingly competitive as oil producers, for example, sought to generate new revenue streams by moving into the space that companies such as Dow traditionally held. More recently, some American companies, including Dow, were creating new production capacities in the United States because of the availability of low-cost shale gas on which to run their plants. Together, these companies committed up to US$110 billion2 of direct investment in advanced manufacturing factories in the United States.3 The North American chemical industry was also witness to the phenomenon of reshoring, resulting from low energy costs in the United States. In a telling example of the cost advantage being offered in that country, Methanex Corporation, a Canadian company and the world’s largest producer of methanol, was planning to close a plant in Chile and relocate to Louisiana in 2012.4 Process variations were a major characteristic of the chemical industry. The amount of output per unit of input, known as yield, also often varied for no immediately apparent reason. Both of these inconsistencies had effects on product profitability. To ensure both quality and yield, it was common for a chemical plant to use statistical process control systems. These systems collected data from sensors on equipment and measurement instruments, such as meters and gages embedded within the process, to regularly monitor the statistical performance—and variations from “normal”—of equipment, processes, and materials. Depending on the product, over 200 measures and variances were being monitored during the chemical transformation process by sensors, devices, and instruments on the shop floor. For example, gas chromatography instruments measured the various chemical elements in a sample as it passed through a process. The chemical inputs or outputs from a process had to be within certain parts per million to conform to normal specifications. Temperature was another type of measure. To improve yields, chemical companies applied management concepts such as lean manufacturing and Six Sigma. These companies were always looking for granular approaches to diagnose and correct process flaws. 1 Deloitte, The Talent Imperative in the Global Chemical Industry, September 2015, accessed June 1, 2016 https://www2.deloitte.com/content/dam/Deloitte/us/Documents/manufacturing/us-mfg-talent-imperative-global-chemicals- industry.pdf. 2 All currency amounts in the case are in U.S. dollars unless otherwise specified. 3 Andrew N. Liveris, “Keynote – A Revolution by Design: Building an Advanced Manufacturing Economy,” MIT Industrial Liaison Program, September 20, 2013, accessed December 1, 2016, http://ilp.mit.edu/videodetail.jsp?confid=77&id=901. 4 Danielle Levy, “Is it Time to Play the ‘Nearshoring’ Boom?,” CityWire, August 21, 2013, accessed December 1, 2016, http://citywire.co.uk/wealth-manager/news/is-it-time-to-play-the-nearshoring-boom/a697719. For the exclusive use of P. Saunders, 2020. This document is authorized for use only by Paula Saunders in ISM 6026 Summer 2020 OMBA Cases taught by JAHYUN GOO, Florida Atlantic University from May 2020 to Aug 2020. http://ilp.mit.edu/videodetail.jsp?confid=77&id=901 Page 3 9B17E014 BIG DATA ANALYTICS The term “big data analytics” referred to the analysis of data originating from different sources to identify patterns and trends, according to which managers could make informed, rather than intuitive, decisions. Consulting firm McKinsey & Company defined big data as “data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.”5 Research company Gartner defined it as “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”6 Big data analytics differed from conventional analytical approaches such as data warehousing and business intelligence in four ways: volume, velocity, variety, and veracity. The volume of data processed in typical data warehousing and business intelligence installations was measured in petabytes (i.e., 1,000 terabytes), whereas big data analytics processes dealt with volumes up to geobytes.7 By way of comparison, one petabyte was the equivalent of text requiring a storage capacity of 20 million filing cabinets of the kind used in a typical office. The velocity of big data installations supported real-time, actionable processes. A retailer, for example, could use geolocation data from mobile phones to ascertain how many customers were in the store’s parking lot on a given day, making it feasible to estimate the sales for that day even before the retailer had recorded those sales. The variety of sources from which big data could be mined included both structured data, such as database tables, and unstructured data originating from diverse sources such as mobile phones, social networks, sensors, video archives, radio- frequency identification (RFID) chips, and global positioning systems. Finally, the veracity of data referred to the inherent discrepancies in the data collected, bringing the reliability and trustworthiness of the data itself into question. Any data that could be captured in a digital form had the potential to be analyzed using big data tools. The key algorithm in big data was known as MapReduce. The algorithm was part of the back-end analytics platform developed by Google in 2000. Having by then indexed 20 billion web pages amounting to 400 terabytes, Google faced three major challenges: the data were largely unstructured, the volume of data was huge, and the overall size of the digital universe was doubling every two years.8 Google engineers