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Requirements:

• A summary of the key issues and your thoughts about important aspects of an analysis or recommendation.
• A contextually grounded inquiry into aspects of the case that you would like to know more about. For example, your reflection could articulate questions you have about the technologies involved, the organizational response to the problem, or the industry context in which the case is set.

This article discusses a pilot study conducted by the Dow Chemical Company to test the applicability of a data analytics module in one of their manufacturing plants. The study aimed to determine if the module, which collected and analyzed real-time data from instruments tracking the quality parameters of finished goods, could be integrated into a larger system called enterprise manufacturing intelligence (EMI). EMI would provide more sophisticated analytics, easier data aggregation, and visualization tools for plant engineers to monitor and act upon. The article highlights the challenges faced by Lloyd Colegrove, the data services director, including accessing real-time data, gaining user acceptance of EMI, and measuring the return on investment. The chemical industry's cyclical nature and reliance on basic commodities are also mentioned. The global chemical industry has undergone significant changes in business models over the years. Many companies did not survive due to a lack of necessary changes, a lack of new product development, and a lack of investment in patents. Three main business models emerged in the industry. The first model focused on owning resources, such as feedstock, and achieving low-cost positions through economies of scale. The second model involved niche positioning, where companies focused on specific technologies and protected their intellectual property through quality, innovation, and strong customer relationships. The third model, exemplified by companies like Dow, involved being solutions providers and understanding end-to-end value streams in different industries. The industry also faced increasing competition as oil producers entered the market traditionally held by companies like Dow. Additionally, some American companies were creating new production capacities in the United States due to the availability of low-cost shale gas. The North American chemical industry also experienced reshoring due to low energy costs. Process variations and yield inconsistencies were common in the chemical industry, leading to the use of statistical process control systems to monitor equipment and processes. Chemical companies applied management concepts like lean manufacturing and Six Sigma to improve yields and correct process flaws. Big data analytics refers to the analysis of large and diverse data sets to identify trends and make informed decisions. It differs from conventional analytical approaches in terms of volume, velocity, variety, and veracity of data. Big data analytics processes deal with much larger volumes of data, up to geobytes, compared to petabytes in traditional data warehousing and business intelligence. It also supports real-time analysis and can handle structured and unstructured data from various sources. The veracity of data refers to the reliability and trustworthiness of the collected data. The key algorithm used in big data analytics is MapReduce, which was developed by Google to handle the challenges of processing large amounts of unstructured data. This article discusses the challenges faced by chemical plants in utilizing big data for manufacturing operations and the need to contextualize and derive valuable insights from collected data. Chemical plants have been collecting and storing large amounts of data since the 1980s, but they have struggled to use it effectively. Most of the success in using data has been in dealing with local issues rather than understanding the whole process. Engineers would troubleshoot based on postmortem data analysis after a problem had occurred, but they would often find themselves in the same situation later on. The article emphasizes the importance of contextualizing data and extracting actionable insights from it. The challenge lies in making the data actionable, rather than simply generating more data. The goal is to create an integrated Enterprise Manufacturing Intelligence (EMI) system that can sample, collate, and analyze both structured and unstructured data from various sources in real time, focusing on finding value in existing data. Dow Chemical is a global chemical company founded in 1943. It has manufacturing sites in 197 locations in 36 countries and was the fourth-largest chemical company in the world by revenue in 2011. Dow has five business divisions and is the largest producer of ethylene. The company focuses on providing solutions to global challenges and has expanded into various sectors, including food and automotive. Dow has a strong commitment to innovation and has been hiring experts in statistics, data science, and big data. The average age of employees has decreased, and the company has a high employee retention rate. Dow has a unique engineering culture that values professional autonomy and designs solutions around processes. This passage describes the challenges faced by Dow in extracting and analyzing data. Dow plants had access to a lot of data stored in various sources such as instrument software, the LIMS, process historians, and enterprise systems. However, extracting data from these systems carried a time lag and required manual processes. Data had to be transferred to a spreadsheet and then routed to a statistical tool for analysis. This process was prone to errors and time delays. Dow was mostly using its R&D lab and engineers to track tags and data, but the analysis was mostly done after problems had occurred rather than in real-time. The article also mentions that Dow deployed outdated data analysis practices and that there was a gap between core engineering skills and data management capabilities. The launch of an EMI pilot project in 2012 aimed to bridge this gap and improve data analysis in the polymer division. This article discusses the integration of EMI software with the LIMS system in the polymer division. The pilot test showed that production engineers were better at collecting and storing data than interpreting it. The challenge was getting buy-in from the production engineers for EMI. The potential benefits of EMI included cost reduction, developing new products, maintaining consistency in practices, and meeting regulatory requirements. The article also mentions the role of the research and development lab in innovation and exploration. The author wanted the plant engineers to do the analysis themselves rather than relying on other resources. The challenges of scaling the EMI pilot project include the need to access and analyze data at the point of origin, avoiding data silos and complex databases, and ensuring real-time access to data. Accessing data at the source equipment/process and in real time eliminates the need for parallel data spaces and allows for direct interactions among plant engineers. Real-time tracking of data is particularly important in continuous processing. The analytics platform should incorporate mission-critical metrics and graphical dashboards relevant to different levels of shop floor management. Adoption of big data innovation at Dow requires the buy-in of plant engineers, which can be achieved through simplification and integration. Some engineers found the system complex because they had to read and interact with the data in real time. This passage discusses the importance of simplifying and modularizing energy management systems at Dow Chemical plants. It mentions that energy is a major cost element at Dow, and the company has set specific targets for energy intensity reduction. Implementing a plant-wide Energy Management and Integration (EMI) initiative could provide real-time information and assist in making decisions to reduce energy consumption more quickly. Other possible modules mentioned include waste recovery and recycling, in line with the growing importance of environmental sustainability. The passage also highlights the need for simplicity in installing the modules on individual workstations and ensuring ease of use for operators. Integration of data from processes, equipment, and legacy systems is crucial for mass acceptance by Dow plant engineers. The passage concludes by mentioning the challenge of measuring the impact of the EMI initiative and identifying tangible evidence to make a compelling case for EMI at Dow. This passage discusses the dilemmas faced by Colegrove and his team in deciding how to proceed with implementing an EMI initiative at Dow. They have gained insight from a pilot project and believe that Dow could benefit from an EMI initiative. However, they are unsure about the best approach. They consider continuing the pilot project to gather more specific metrics, modularizing the initiative, or rolling it out on a broader scale for engineers to learn on their own. They also question whether it is the right time to make demands of plant engineers or if it would be better to keep it in R&D. Additionally, they wonder if they should wait until there is a problem to be solved, in case EMI is just a passing trend