Big Data Demand Forecasting Regarding High Value/Highly Volatile Stock Keeping Units

Sebastian Neumann

• Publisher: GRIN Verlag
• Year: 2020
• eBook ISBN: 9783346234681
• Edition: 0
• Topic: Business
• Subtopic: Information Management
• Index: Business

Academic Paper from the year 2020 in the subject Business economics - Information Management, grade: 1, 2, Technical University of Applied Sciences Mittelhessen, language: English, abstract: This paper gives readers the possibility to brighten and deepen their knowledge in subjects which are closely related to Supply Chain Management.Predictive analytics, in the context of big data demand forecasting, is a revolutionary phenomenon in the modern world, and is expected to remain so in the foreseeable future. In this paper, the conditioning of big data, in the context of organizations that carry out a demand forecast, is differentiated into three parts. Accordingly, the procedure of data acquisition, data classification concepts, and forecasting methods are examined. The review of the literature has yielded an assessment method that supports demand planners to determine how big pools of data can be classified and utilized to carry out a forecast in a compatible manner. The case related application of the developed assessment method in organizations that forecast highly volatile stock keeping units with high monetary value was successful. The following paper is subdivided into a total of six chapters. Accordingly, after the introductory chapter has shed light on the subject, further work on the topic will be conducted as follows: Chapter two will provide insights regarding the relationship between big data and the objective of this paper, by providing information about the acquisition of planning data. Within chapter three, different classification concepts are introduced considering the context of compatibility with specific forecasting methods of chapter four, respectively. This predominantly conducted literature review results in an assessment method that is applied in chapter five, where identified insights are considered in the context of different case studies. Lastly, chapter six states a brief conclusion about essential results found out in this paper.