Open Access Conference Proceedings

Decision making processes in modern industrial management benefit from big data processing. This paper presents selected tools for processing huge amounts of data, which can support decision makers in following areas: process control, industrial logistics, and urban public transportation planning. We begin with the Statistical Process Control (SPC) tools which can be used to control the entire production or service process. Dealing with the variability of large systems requires using statistical approach to prevent errors and to evaluate the quality of system performance. In the second part we concentrate on logistic flow planning, especially on tools for lot sizing and scheduling, which enable transforming aggregated data on the total demand into feasible and operable production schedules. It is an important aspect of manufacturing control and production management, as it can result in minimizing the total cost of set-up and processing and the total inventory cost. Finally, we focus on transport planning and management which plays a critical role in the development of modern cities and industry. We review the most popular approaches and tools for the transfer synchronization and the interval synchronization, and we discuss their capability to process big data. This paper contributes with the review of analytical methods conducted from the perspective of their usefulness for big data analysis. We show that tools commonly used in managerial practice in process control, industrial logistics, and urban public transportation planning can support decision making process.

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