Дослідження системи операційного менеджменту організації, на прикладі Apple Computer, Inc

The object of investigation is the process of managing of operating activities of Apple, Inc. The aim of the work is to formulate theoretical approaches and to develop practical recommendations on directions of improvement of operating management at the organization. Research methods cover methods of analysis, synthesis, comparison, detailing, system approach. This master’s research paper analyzes the operational management of Apple, Inc. and provides recommendations for it’s improvement. In particular, the main directions of solving the problems of operational management of the company have been outlined, the proposals on improvement of expansion distribution network and organization of innovative activity of the Apple Inc. have been made.Об'єкт дослідження ‒ процес управління операційною діяльністю компанії Apple, Inc. Мета дослідження - формування теоретичних підходів та розробка практичних рекомендацій щодо напрямів вдосконалення системи операційного менеджменту компанії Apple, Inc. Методи дослідження: методи аналізу, синтезу, порівняння, деталізації, системний підхід. У роботі проведено аналіз операційного менеджменту Apple, Inc., а також викладені рекомендації щодо його вдосконалення. Зокрема, окреслено основні напрями вирішення проблем операційного менеджменту компанії, внесено пропозиції щодо розширення дистриб’юторської мережі, а також вдосконалення організації інноваційної діяльності Apple Inc.Introduction 6 CHAPTER 1 THE THEORETICAL FRAMEWORK OF OPERATIONAL MANAGEMENT 8 1.1 Meanings and definition of operational management 8 1.2 Principles and methods of operations management 12 1.3 Factors affecting the Operations activity of Apple Inc. company 21 CHAPTER 2 RESEARCH AND ANALYSIS 31 2.1 Сompany introduction 31 2.2 SWOT - analysis of Apple Inc. Company 46 2.3 Analysis of operation management at Apple Inc 50 CHAPTER 3 RECOMMENDATIONS FOR IMPROVING OF OPERATIONAL MANAGEMENT AT THE APPLE INC 63 3.1 The main directions of solving operational management problems of the company 63 3.2 Recommendations concerning improvements of Distribution in the organization 65 3.3 Recommendations concerning improvements of innovative activity at the organization 67 CHAPTER 4 SPECIAL PART 73 4.1 Current trends in the field 73 4.2 Company policy in the market 75 CHAPTER 5 RATIONALE FOR RECOMMENDATIONS 77 5.1 Statement for recommendations at Company 77 CHAPTER 6 OCCUPATIONAL HEALTH AND SAFETY AT THE ENTERPRISE 79 6.1 The aim of occupational health 79 6.2 Organization of occupational health and safety at the enterprise 86 CHAPTER 7 ENVIRONMENTAL ISSUES 92 7.1 Environmental issues in the field 92 7.2 Еnvironmental factors 94 Conclusions 96 References 98 Appendices 10

Huawei-ZTE : one feared response to today’s telecommunications equipment market

After the global crisis that started after June 2008, companies have become more wary about maintaining solid financial positions and liquidity. This has made them start to build piles of cash, which are now very large. In addition to this, the recovery of economic growth and the increased global competition created incentives to M&A transactions to happen. Huawei and ZTE are two Chinese Telecom Equipment companies that have comparable background and compete with each other. By engaging on a merger, they would be able to enhance their revenues and cut the costs and be in a better position to face the fiercer than ever competition.Depois da crise global que teve início em Junho de 2008, as empresas passaram a preocupar-se mais com a manutenção de uma posição financeira sólida e líquida. Isto causou uma grande acumulação de meios financeiros líquidos. Além disso, a recuperação económica e o aumento da concorrência a nível global criou incentivos para integração económica. Huawei e ZTE são dois fabricantes chineses de equipamento de telecomunições, partilhando um passado semelhante. Ao procederem a uma fusão entre as duas, estas empresas poderiam potenciar receitas e reduzir custos, conseguindo colocar-se numa melhor posição para fazer face a uma concorrência mais forte do que nunca

Mergers and acquisitions : ARM takeover by Intel

By the end of 2009 a lot had already been talked about ARM prospects for the future and its sustainability. Several rumors surfaced that the company could be one of the intervenients in the most interesting deal of the last years among IT companies. Among others, Intel was pointed as the most obvious candidate for the acquisition, however Intel had the company of Google, Apple and Samsung, just to name a few. In this thesis we analyzed the hypothesis of a deal between Intel and ARM, what value would be created for Intel and ARM shareholders, and how much competition would Intel face in the case it decided to move forward. We concluded that the deal is feasible and would create value for Intel and ARM shareholders, at the expense of the current ARM customers and, most likely, final consumers. There seems to be no other firm with capabilities and reasons to go for such deal, and Intel would face no competition, offering a premium of 55% over ARM market value. This would leave value for Intel as we calculated the synergy at 73% the actual ARM market value. Finally as a separated note, it should be kept in mind that such a deal would probably face a tough time being approved by regulators, such is already the dominant position Intel has in the market

Introduction for the special Issue on BIG DATA

Today we live in the era of Big Data Revolution, overwhelmed with data, information and knowledge that is spread all over the web, in social media sites, in smartphones contributing so in creating a large base of big data. In their best seller book ‘Big Data: A Revolution That Will Transform How We Live, Work, and Think’ (2013) Mayer-Schönberger and Cukier argue that thanks to the internet, social networking, smartphones and credit cards, more data is being collected and stored about us than ever before – and this has created an opportunity for firms and managers to easily and cheaply capture and store massive amounts of data in a way that was simply impossible before. In this scenario the winners will be those that have the abilities, the intelligence, the creativity and the tools to elaborate these data for grasping insights and knowledge from the available data and to be able to use and exploit them for continuous innovation, for improving firms performance, for creating new products and services as well as for establishing new innovative business models. According to a study realized by McKinsey  (Manyika  et al, 2011) to analyze the impact of big data analysis for  innovation, competition, and productivity they argue that there are 5 broad ways in which using data can create value: Big data can unlock significant value by making information transparent and usable at much higher frequency. As organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance.Big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Sophisticated analytics can substantially improve decision-making. Big data can be used to improve the development of the next generation of products and services. On the other hand, the availability of big data has created a new era also for data analysis and elaboration to discover unknown patterns, to find out what customers want, what they evaluate,  to get closer to customers, to gain a wealth of information about their behaviors and preferences, as well as to identify new market trends and new opportunities to remain competitive. The ability of firms to aggregate, elaborate and analyze the data is becoming a key competitive advantage resource. Different researches have evidenced the role and the outcomes that the big data analysis could bring to firms in terms of innovation, efficiency, productivity, quality and customer satisfaction. In a survey study of more than 3,000 business executives, managers and analysts from organizations, MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value found out that executives are oriented toward managing the business based on data-driven decisions and it is the use of business information and analytics that differentiates them within their industry (Lavalle et al, 2010). Moreover, the content and information that customers create in web 2.0 platforms constitute a valuable asset for firms to directly tap into the customer’s preferences and needs, as the most valuable source for attaining direct and reliable market information. In this environment, more and more firms are building their competitive advantage on their ability to collect, analyze and act on data. Therefore, the capability of firms to tap into data, to analyze and interpret them to gain insights and to ensure a more effective decision making process has become an essential ingredient towards innovative thinking and creativity. Therefore, there is a need of matching the analytical capacities with the creativity in order to interpret big data in an innovative way. It is in this aim that we have realized these special issue of the journal to publish some  research articles that use statistical and analytical models to elaborate big data for a large range of issues and sectors and for establishing new innovative insights. The issue presents four research papers that focus on providing practical cases of exploiting big data for grasping new insights and opportunities. In particular: The article on ‘Big Data and Knowledge-intensive entrepreneurship: trends and opportunities in the tourism’, the authors Del Vecchio et al., focuses on the growing relevance of Big Data as valuable source of knowledge impacting on the creation and execution of knowledge-intensive entrepreneurship. The article provides a detailed description regarding the opportunities offered by Big Data by demonstrating, with practical applications from the tourism field, how the large amount of knowledge distributed in the web can support the conception and execution of an entrepreneurial process more aligned with the customers' needs and focused on the actual market's trends.   Carpita and Simonetti in the article ‘Big Data to Monitor Big Social Events: Analysing the mobile phone signals in the Brescia Smart City’ present the implications that big data analysis  could provide for Municipal administration to plan future events, and more generally to develop policies for the ‘smart city. They use the statistical methods to process and assess really high mass of data and information extracted from mobile phone signals in order to  improve the quality of the big social events that take place in the city as well as to create the conditions for developing useful reports for territorial marketing. The paper on ‘Ontological analysis for dynamic data model exploration’ by Hobbs et al., explores the expressive approaches to data analysis. The authors provide an aggregate model that utilizes ontological tools to create domain models in a way that it allows for a distributed and parallel implementation necessary for big data analysis. In the article ‘Implementation of a Web-based Application for Predicting Best Training Recommenders for Princess Norah University Employees’ the authors Mohammad and  Alhaidey propose the realization of  a recommender system that would help in the decision making process and planning of the training course offered by organizations for their employees. The recommender system is based on using data mining techniques that allows the observer to discover specific patterns and knowledge from large databases and carrying out predictions for outputs.

Tree Country

Tree Country is an employee newsletter released by the South Carolina Forestry Commission

Predicting Personality Traits Using Smartphone Sensor Data and App Usage Data

Human behavior is complex -- often defying explanation using traditional mathematical models. To simplify modeling, researchers often create intermediate psychological models to capture aspects of human behavior. These intermediate forms, such as those gleaned from personality inventories, are typically validated using standard survey instruments, and often correlate with behavior. Typically these constructs are used to predict stylized aspects of behavior. Novel sensing systems have made tracking behavior possible with unprecedented fidelity, posing the question as to whether the inverse process is possible: that is, inferring psychological constructs for individuals from behavioral data. Modern smartphones contain an array of sensors which can be filtered, combined, and analyzed to provide abstract measures of human behavior. Being able to extract a personal profile or personality type from data directly obtainable from a mobile phone without participant interaction could have applications for marketing or for initiating social or health interventions. In this work, we attempt to model a particularly salient and well-established personality inventory, the Big Five framework. Daily routines of participants were measured from parameters readily available from smartphones and supervised machine learning was used to create a model from that data. Cross validation-based evaluation demonstrated that the root mean squared error was sufficiently small to make actionable predictions about a person's personality from smartphone logs, but the model performed poorly for personality outliers

Zara's case study: the strategy of the fast fashion pioneer

The Fashion Industry has been changing among the years, and fashion companies are changing the way they do business. They are focusing each day more on the consumers, and what they are looking for at that moment. Fast Fashion concept is gaining ground in the industry because now it is more important to always follow the latest trends and produce according to that, than to have just two collections per year for a higher price. Now, consumers are always looking for different products and those companies who can give them that will achieve the success more easily. Consumers not only are looking for different products, but even more, are using different channels to shop. The online shopping is winning users day by day because now people search for convenience and comfort. However, this behaviour can be different from the different generations, and it is important to know exactly which target it is important to reach and satisfy their desires. This way, fashion companies need to adapt their business and turn it more technological, to make the shopping experience the more enjoyable as possible. This Case Study has the objective of study Zara’s strategies, from their supply chain until their marketing and communication strategies, and the strategy in the physical stores versus at the online store. Then the study will focus on the consumer: their behavior according to the different generations and the brand perception according to them. A Market Research, through an online survey, will be conducted in order to help the development of this study.A indústria da Moda tem vindo a mudar ao longo dos anos e as empresas de moda têm vindo a alterar a maneira como fazem negócio. Cada vez mais se focam no consumidor e naquilo que ele está a procura no momento. O conceito de "Fast Fashion" tem vido a ganhar terreno na indústria, porque agora é mais importante seguir as últimas tendências e produzir de acordo com isso do que apenas lançar duas coleções anuais por um preço mais elevado. Agora, os consumidores estão constantemente a procurar produtos diferentes, e as empresas de moda que são capazes de lhes dar isso irão alcançar o sucesso mais facilmente. Os consumidores não procuram apenas produtos diferentes, mas cada vez mais usam diferentes canais para realizar as suas compras. As compras "online" estão a ganhar utilizadores de dia para dia, pois cada vez mais eles procuram conveniência e conforto. Mas este comportamento pode ser diferente de acordo com as diferentes gerações, e é importante saber exatamente o tipo de "target" que interessa para assim satisfazer as suas necessidades. Desta forma, as empresas de moda precisam de adaptar o seu negócio e torná-lo mais tecnológico, de forma a tornar a experiência de compra o mais agradável possível. Este Caso Pedagógico tem como objetivo estudar a estratégia da Zara, desde a cadeia de abastecimento até as estratégias de marketing e comunicação, e também comparar a estratégia nas lojas físicas com as da loja "online". Depois o estudo irá focar-se no consumidor: o seu comportamento tendo em conta as diferentes gerações e a sua perceção de marca. Um estudo de mercado, através de um inquérito "online", será realizado para ajudar o desenvolvimento deste estudo