Bottom Line: Manufacturers are reaching a new level of results in 2018 because they have clearer, more actionable insights based on real-time manufacturing and quality metrics than ever before.
Quality Metrics Enable Customer-Driven Manufacturing Networks
The future of manufacturing is being written today across the millions of shop floors which have chosen to excel at analytics, metrics and Manufacturing Intelligence. And this focus on measurable results is attracting private equity firms who are focused on building integrated manufacturing networks. Analytics and metrics are shifting manufacturer’s focus from factory excellence alone to grow their businesses based on customer-driven outcomes. The shift to customer-driven manufacturing is attracting private equity firms who see the value of creating their own highly integrated manufacturing networks. For additional details on private equity’s priorities are in a manufacturing ERP system please see the post 10 Most Valuable ERP Features to Help Private Equity Firms Manage Their Manufacturing.
What Success Looks Like In A Customer-Driven Manufacturer
Able to flex in response to short-notice production runs without sacrificing product quality or gross margins, knowing in real-time what every machines’ and production line OEE levels are, and having a Perfect Order Performance level over 95% are the sure signs of a customer-driven manufacturer excelling at what they do.
Metrics are the mile markers manufacturers are using to move away from being reactive to being more customer-driven and responsive. They’re able to balance the three conflicting demands of Quality, Speed and Scale shown in the Venn diagram to the right and excel by using metrics to find out where and when to integrate processes end-to-end across manufacturing operations. They’re using metrics as a roadmap to attain true customer-driven manufacturing by integrating each production process together for the greater speed, scale, and quality possible.
Higher production performance, greater customer satisfaction, higher profitability, and growth are all the results of relying on analytics, quality metrics and Manufacturing Intelligence that enables customer-driven manufacturing to scale from a single plant to a network. Manufacturing Cycle Time, Overall Equipment Effectiveness (OEE), Perfect Order Performance, Supplier Quality and Yield Rates are the five metrics that the majority of manufacturers are using to break out of being in reactive and become more responsive to customers’ current and future preferences and requirements.
Quality Metrics Defining the Future of Manufacturing
The five quality metrics defining the future of manufacturing include the following:
- Manufacturing Cycle Time – An excellent measure of how efficient a production operation is, Manufacturing Cycle Time measures how long it takes to assemble, inspect, stage and convert materials and components into a finished product delivered to the customer. Doing a time series of this metric over time also is useful for tracking the supply chain performance and latency. The majority of discrete, make-to-stock and build-to-order manufacturers have this on their dashboards as it provides an immediate measure of how well orders are progressing across the shop floor and if any prerequisite tasks from suppliers are holding up progress or not.
- Overall Equipment Effectiveness (OEE) – OEE is calculated by multiplying machine Availability by Performance by Quality. Stabilizing machinery performance is the factor that drives the majority of manufacturers first to adopt OEE. As individual machines and production lines stabilize, OEE reflects manufacturing reliability. The following is a roadmap of how to improve Overall Equipment Effectiveness (OEE).
- Perfect Order Performance – Also referred to by many manufacturers as the Perfect Order Index (POI), this metric is invaluable in measuring how well supply chain collaboration, production operations, and fulfillment is meeting customer expectations. Perfect order performance is calculated by taking the (% of orders delivered on time) * (% of orders complete) * (% of orders damage free) * (% of orders with accurate documentation) * 100. ERP systems based on a single database architecture can scale and provide comparative perfect order performance metrics and analysis by each plant. Private equity firms are looking to create customer-driven integrated manufacturing networks built on a single software platform capable of providing an enterprise-wide perfect order performance metrics while also having the agility to drill down into plant data. IQMS’ EnterpriseIQ is purpose-built to scale across multiple plants, as it’s designed on a single, secure database architecture that’s been proven in production at over 1,100 customers.
- Supplier Quality Acceptance Rate – This metric is an excellent indicator of how effective the Supplier Quality Assurance (SQA) systems and processes are in a manufacturer. The goal of an SQA is to provide suppliers with an accurate, reliable framework for delivering the highest quality raw materials, components, subassemblies and assemblies used in production. Manufacturers often create a time series of this metric to quantify how effective supply chain collaboration is across suppliers, buyers, procurement, and The Supplier Quality Acceptance Rate quantifies the percentage of raw materials, components, subassemblies and assemblies that pass minimum quality standards and are then used in production. Many manufacturers evaluate this metric every 90 days with all suppliers to evaluate relative quality levels by product and sourcing area.
- Yield Rate by Production Line – Defines the percentage of products that are produced on a given production line correctly the first time, ready for delivery to a customer. Manufacturers often use a combination of manual and automated methods to get accurate yield rates by production line and plant. What makes this one of the key metrics changing manufacturing today is the role of advanced production machinery has in measuring yield rates in real-time and reporting them via real-time monitoring. Advanced manufacturing machinery can also calculate scrap and re-work figures in real-time as well, providing valuable data to base OEE measurements on.
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