Normal accidents: Data quality problems in ERP-enabled manufacturing

The efficient operation of Enterprise Resource Planning (ERP) systems largely depends on data quality. ERP can improve data quality and information sharing within an organization. It can also pose challenges to data quality. While it is well known that data quality is important in ERP systems, most existing research has focused on identifying the factors affecting the implementation and the business values of ERP. With normal accident theory as a theoretical lens, we examine data quality problems in ERP using a case study of a large, fast-growing multinational manufacturer headquartered in China. Our findings show that organizations that have successfully implemented ERP can still experience certain data quality problems. We identify major data quality problems in data production, storage and maintenance, and utilization processes. We also analyze the causes of these data quality problems by linking them to certain characteristics of ERP systems within an organizational context. Our analysis shows that problems resulting from the tight coupling effects and the complexity of ERP-enabled manufacturing systems can be inevitable. This study will help researchers and practitioners formulate data management strategies that are effective in the presence of certain “normal” data quality problems.

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