Manufacturing industries handle multiple sets of data, which needs to be continuously captured and analyzed to streamline business applications and optimize business resources. The industry handles routine structured in-factory data, along with analog data, images, raw sensor data, and information churned out from applications inside the factory and other sources, which include Enterprise Resource Planning (ERP) systems, manufacturing execution systems, time and attendance logs, supplier information, and various process automation and control systems.
With the manufacturing systems becoming increasingly sophisticated and software-driven, it is imperative for data scientist to use effective data quality tools. All the associate systems such as Customer Relationship Management (CRM), ERP as well as Supply Chain Management (SCM) would have an adverse effect and will suffer owing to poor data quality ingested into these systems. Moreover, maintaining data quality would be of significant importance to optimize supply chain and induce efficiencies in the manufacturing processes. Effective data management program could be highly helpful in reducing the product development costs as well. Having a robust data management program within the processes would help in countering numerous types of risks such as economic, technical, and supplier risks.