1.1 IntroductionIn recent times, it is observed that there is an ever-continuous change in taste as a result, consumers are becoming more dynamic in their preferences for products and services hence they are demanding to be presented with more choices from which they can choose from. Like consumers, Business to Business (B2B) customers are also becoming impatient and picky with an expectation of access to customized products at affordable prices. Concurrently, demands to decrease production costs, improve product quality, increase productivity, reduce ecological footprint and meet stringent health and safety requirements demanded by all stakeholders has significantly increased.
This has enlarged competition prompting the need for faster market response. Sending grab samples to the Laboratory for analysis which takes time in some instances several hours /days is no longer desirable to cater for these changing trends. Most Laboratory analysis are done offline, hence the results are not real time which affect the ability in taking prompt decisions in volatile situation were process parameters are constantly changing.
Staffing cost to support these routine analysis is increasingly considered as an undue overhead expense leading to increase in production cost. Hence most companies are looking for smart ways to slim their laboratory budget while simultaneously increasing product quality, improve productivity while complying with regulatory requirements. In a bid to address this trend, many companies have been led on the path toward greater use of process analytical equipment to enable them to become more flexibility to produce small lots efficiently to respond to fluctuating customer demands which tends to favor customization. Another reason for the shift to inline measurement is to enable them to offer depth across multiple platforms such as a broad product line, improved delivery time, merchandising, and sophisticated data collection systems. This has resulted in business being inundated with an unprecedented flow of data generated from devices installed both upstream in manufacturing and downstream at the point of sale which has led to a concept called digital obesity. The challenge now is to filter out the useful information from the large mass of available data (Manenti, 2017, p.31).Neglecting technological innovation within the supply chain to address this huge dynamic demand will limit the company’s ability to keep up with changing demands and buying patterns of consumers. The analytical instrumentation industry on the other hand has reached a state of maturity which can be defined by fewer technological leaps at every given time. At a time when most analytical equipment manufacturers’ offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation. Proprietary technologies are rapidly copied, and breakthrough innovation in products or services seems increasingly difficult to achieve (Huffington post, 2017). What is left as a basis for differentiation is for them to execute their business with maximum efficiency and effectiveness. If they are to compete effectively, then they must have some attributes (a distinctive capability) at which they are better than anyone else in the industry. They need to select one or few distinctive capabilities on which to base their strategies, and then apply extensive data, statistical and quantitative analysis, and fact-based decision making to support the selected capabilities. Analytics themselves donґt constitute a strategy but using them to optimize a distinctive business capability certainly constitutes a strategy. Having this distinctive capability differentiates an analytical equipment manufacturer from her competitors hence ensuring their success in the marketplace. The Brewing industry as with other manufacturing industries is continuously being shaped by technology that produces immense volumes of data. This data must be leveraged to make decisions so that businesses can evolve alongside the rapid change in pace of technology. As they embark on this digital transformation journey, big data and analytics can play a key role in them being successful. It is vital to understand how each aspect of their value stream can be optimized to fulfill new digital objectives and growth potential to stay ahead of the competition. Their success in the digital world relies primarily on how well they manage and analyze the data coming from disparate internal systems (operational data) and external channels (Suppliers, Logistics etc.). They need to understand how to innovate and leverage digital data to drive sales and productivity. They will require a data architecture and strategy that can support efficient digital transformation by unlocking the value in all data sources to provide mission-critical insights and informed decision-making (big data analysis). However, it is important to note that the path to becoming an insight-powered organization is not an easy one”embedding analytics into role-based decision-making requires changing the cultural DNA (present ways of work) of the organization. Without evolving the culture, even the top technologies and talent will face problems in getting the best returns from enterprise analytics programs (The 5As of analytics transformation, Accenture analytics).1.2 Goals and objectives The thesis will focus on certain key deliverables which are updated on the theoretical concepts of Laboratory analysis, inline process measurement and digitalization, as applicable to the Brewing industry. The current market trends demand a radical shift from this traditional approach of Laboratory analysis to inline process analysis strongly supported by digitalization. The underlying conceptual framework is addressed using the following six research questions:1. What is the current Brewery business environmental using PESTEL analysis?2. How does the current Laboratory analysis support or hinder the Brewery strategy with regards Productivity?3. What are the possible motivations for a change from the traditional Laboratory analysis to an inline Process analysis4. How will the introduction of inline Process analysis support the Brewery strategy in respect to productivity?5. How can the Brewery utilize the generated data from inline process equipment and digitization to support it decision making?6. How will the Brewing Industry which considers itself as a craft respond to changes brought about by automation and digitalization?Different research methods in this Thesis will be employed to obtain relevant data for analysis in answering the above six questions. These will range from primary sources (direct collection by the researcher) and secondary sources (data already obtained from credible sources). To project a scenario for Brewery future automation utilizing inline quality measurements and digitalization, the author will use a mixed research method (combination of qualitative and quantitative research). Therefore, the research method utilized will be flexible depending on the prevailing situation during the Thesis execution.1.3 Procedures and working Instructions The procedure to be followed will involve analysis of the changing external and internal landscape shaping the Brewing industry and the current challenges it must overcome. With growing interest in Industry 4.0 and Internet of Things, the thesis will examine how these important concepts will affect productivity in the Brewing industry and the critical transformations that the industry will undergo to face challenges in the future. The analysis of the changing landscape of the Brewing industry will be extensively covered in the following chapter.
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