Product quality is the responsibility of all parties involved in the procurement, manufacturing, packaging, and distribution of raw materials, intermediates, and final products. Ultimately, the brand owner is responsible for assuring that all parties in the supply chain fulfill their responsibilities for delivering quality goods to the customer.
In today’s increasingly cutthroat global marketplace, particularly in the electronics sector, a number of trends are dramatically changing the supply base and challenging the ability of brand owners to manage their supply chains and ensure quality. Principal among these are greater reliance on component suppliers, outsourcing of subassemblies and offshore manufacturing.
Their impact has been to erode brand integrity, propagate downstream warranty issues, and complicate quality management. As brands and their quality have become increasingly outsourced, the traditional methods used to manage the supply chain have proven ineffective.
What’s more, access to data is unpredictable across the supply chain. The brand owner gets a number of reports, but they are often incomplete, in inconsistent formats, and after the fact. It is difficult to interpret the data and take effective action, and quality inevitably suffers.
Historically, a number of approaches have been used to manage the supply chain and ensure quality. In the “trust” approach, manufacturers provided a specification and had faith that their suppliers would meet it. This proved unrealistic, creating another place for the phrase, “trust but verify.”
The need for verification resulted in inspections, where the brand owner’s quality engineering teams traveled to suppliers. While costly and exhausting, this met with some success; but over time—and in increasingly far flung locations—it proved that quality was fine when the quality engineering team was on site, but degradation often occurred after they left.
This experience underscored the need for quality trend analysis. An individual “quality snapshot” may be fine, but what became essential was continuous monitoring of quality, assuring that specs were met with exactitude and no corners were cut.
Some organizations developed extensive internal quality monitoring, from incoming inspection teams that retest supplied components to expensive proprietary monitoring systems that could cost tens of millions of dollars to develop.
All these methods have proved less than satisfactory, but the advent of cloud computing has provided a powerful new means to regain control. It has enabled the implementation of supply quality networks that provide continuous supplier performance monitoring, automatic data collection embedded in test equipment or from simple data entry at a supplier’s test station, automatic notification for lot acceptance, and lot rejection if minimum standards are not met.
Applying a cloud-based solution to establish a supply quality network entails the execution of four basic steps:
- 1) Capture the data. Some suppliers have automated production equipment and may even be using manufacturing execution systems. Some operate manually. Most companies have data in Excel that can be captured. If they are using pen and paper, then Web-based forms that are available in their native language are a big improvement and can improve the accuracy of the data.
- 2) Upload the data. Captured data is aggregated, synchronized, and retained. The incoming formats are standardized and stored on servers where the supplier and the brand owner have secure access to viewing it. A quality issue will be visible immediately – before shipment.
- 3) Analyze the data. Analytics must be applied to gain insight into quality issues and provide the ability to act on real-time and historical data immediately. Compare trends over time to isolate and preemptively address critical-to-quality issues.
- 4) Provide insight from the data. The source of a problem can be elusive. An intelligent, multidimensional pattern recognition tool identifies the data clusters where anomalies and issues are, as well as areas that do not require attention. Because of the sheer volume of data, this insight saves time and money while preventing problems.