Examining the barriers of big data analytics in the logistics sector by using ISM-MICMAC methods

In recent years, big data has attracted attention due to its usefulness in many fields. It is seen that big data analytics helps businesses in many fields of activity as well as helping to have an opinion on any subject. However, it is seen that there are barriers in front of the acceptance and implementation of big data analytics in some sectors. One of these sectors is the logistics sector. Despite the importance and application areas of big data analytics in the logistics sector, there are businesses where big data analytics are not applied. In this study, it is aimed to examine the critical barriers in front of big data applications in the logistics sector and to explain and interpret the relations between them. The Interpretive Structural Modeling Method (ISM) was used to reveal the direction in which the barriers affect each other and to identify the barriers that should be focused on. The MICMAC method was used to classify the barriers in front of big data applications according to their impact and impact from each other. After all, the most important barriers to big data analytics in the logistics industry are; "Complexity in data management" and "business resistance to organizational change" are barriers. These barriers have the ability to affect and be affected within the system. Barriers of "data security and privacy", "computing power", "lack of skilled workforce", "lack of information sharing", "lack of senior management commitment", "existing legacy systems" and "uncommonity of big data applications" are under control. They are the second-order barriers that need to be kept. "Financial insufficiency" and "poor data quality" barriers are in the last place as barriers that do not have much commitment and do not affect the system.