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The impact of rapidly developing technologies on digitalization of production and planning has affected all sectors in a short period. This impact has led to both positive and negative outcomes. While it is helping the decision-makers to make better and more productive choices, it also can create a cluster of information and data that can put an undue burden on processes. Today, we call this Industry 4.0, which is defined as the use of digital technologies, based on data processing, the end-to-end connection of value chains, and ensuring data fluidity. Industry 4.0 consists of technologies such as cyber-physical systems, internet of things, cloud computing, big data and analysis, autonomous vehicles, and augmented reality. With the digitalization of the entire production and planning processes, data-based applications are made; thus it is important to protect data in this context, pointing to the critical importance of cybersecurity. Companies are constantly working on taking the necessary cybersecurity measures to prevent exposure to any cyberattacks. One of the biggest steps toward the development of production and planning is undoubtedly the integration and adaptation of blockchain technology. The use of blockchain technologies has been a major breakthrough for the sector in order to effectively meet customer needs, ensure information security, reduce costs, and achieve rapid growth. With digital transformation, production and planning must comply with the principle of transparency. In this context, integrating blockchain technologies into the production and planning ecology for data security will provide companies with a serious competitive advantage.

This study discusses the evolution of supply chain and the growing importance of dual marketing strategies. To understand the concept of dual marketing, we present a scenario faced by property developers in Malaysia on the impact of public policy on dual marketing strategy. This study is to uncover (1) the reasons why and how property developers adopt dual marketing strategy, (2) the impact of housing policy on the developers' dual marketing strategy and how they strategise to cope with the policy.

The purpose of this study is to review the capital budgeting literature over the past decade.

Specifically, over the years 2004–2013, we review works appearing in the major academic journals in accounting, finance, and management. Further, we review the specialized academic journals in management accounting. We examine the frequency of articles by journal and year published, the type of research method applied, and the topic area studied. We then review the research findings by topic area.

We find 110 articles appearing in the selected journals. While the articles increase in frequency, the research methods applied are predominantly analytical and archival in nature with relatively few experiments, case studies, or surveys. Some progress is observed for capital budgeting techniques and new methods for structuring uncertainty. The studies find that the size of capital budgets is about right for companies with high financial reporting quality, for liquid companies, during periods of normal cash flow, when the budget is financed by equity, for companies when they first go public or first go private. Tax rates and financial reporting methods for depreciation and tax expenses distort capital budgets. Organization structure and performance measurement can distort capital budgeting. Individual differences, especially optimism and honesty, can influence capital budgeting decisions.

This review is limited to the major journals in accounting, finance, and management; and the specialized journals in management accounting. There is much research to be done on capital budgeting, especially case studies of actual practice and experiments related to individual and group decision processes.

To fully accommodate the correlations between semiconductor product demands and external information such as the end market trends or regional economy growth, a linear dynamic system is introduced in this chapter to improve forecasting performance in supply chain operations. In conjunction with the generic Gaussian noise assumptions, the proposed state-space model leads to an expectation-maximization (EM) algorithm to estimate model parameters and predict production demands. Since the set of external indicators is of high dimensionality, principal component analysis (PCA) is applied to reduce the model order and corresponding computational complexity without loss of substantial statistical information. Experimental study on certain real electronic products demonstrates that this forecasting methodology produces more accurate predictions than other conventional approaches, which thereby helps improve the production planning and the quality of semiconductor supply chain management.

Developing and developed countries are working to step up the process of digitalization according to their existing capacities. Digital transformation affects all departments from production to marketing and is correlated with important outcomes such as productivity, performance and efficiency. It is thought that this revision can bring disadvantages along with its many advantages. When the studies are evaluated, it's seen that the revision has an effect on employment. Compulsory changes in management and organization structures, necessity of developing new models in product and production alterations are some of the consequences of the risk due to Industry 4.0. Customer satisfaction, cyber-security dangers, monopolization and tax loss in the country must not be ignored. Within this scope, this chapter focusses on dystopic and Industry 4. It is thought that the dystopic effects of Industry 4 will allow the organizations to provide the necessary preliminary preparations. Major basic factors such as employment technical infrastructure and financial resources, and with the right strategies, can be managed to make it easier for a revolutionary change.