Manufacturers require ERP systems that can “learn” what excellent quality looks like.
The cornerstone of every manufacturer’s success is the ability to shift gears from one product generation and business model to the next while finding new ways to excel at quality. Enterprise resource planning (ERP) systems enable manufacturing firms to scale the production of identical or similar products quickly. Traditionally, these systems have been designed to enforce consistency at the product level. However, ERP systems are struggling to keep pace with today’s era of mass customization and the growing customer demand for products that deliver experiences via embedded intelligence, Internet of Things (IoT) sensors, and contextual artificial intelligence (AI).
Often product quality challenges are the first symptoms of an ERP system not scaling to support current and future generations. When product quality and the audits tracking production start to show degradations in yield rates, a good place to look for causes is in a legacy or homegrown ERP system. Too often, these solutions cause more quality problems than they solve because years of software customization have resulted in an inflexibility to change. Moreover, many older ERP systems were designed for production consistency at the expense of meeting the higher quality standards that more complex, configurable, build-to-order products require.
The future of ERP will depend on the ability to bridge the gap between earlier manufacturing requirements and current product demands in an increasingly quality-driven world. Central to this evolution will be the integration of AI, advanced analytics, and machine learning into ERP platforms.
Closing the Quality Gap with Knowledge
Manufacturers need to be able to flex and scale their production operations quickly in response to the dilemmas they face meeting production forecasts and quality levels. That means having exceptional insights and knowledge into what quality strategies are working and which aren’t. These insights need to go beyond transaction-based analytics to showing how production decisions are making a positive or negative contribution to quality levels.
As a result, manufacturers require ERP systems that can “learn” what excellent quality looks like over time for every new product. The ERP system also has to have the ability to aggregate, analyze and extrapolate data to see how production, supply chain, sourcing, and manufacturing decisions impact quality.