Quality here is defined as meeting all your customers' design specifications, standards, needs, and wants. Quality can be quantified in terms of price, measurement, weight, appearance, standardization, functions, life cycle costing, and surface quality. Normally, this has been summarized in manufacturing/engineering as customer design specifications. However, due to innovations in the data science realm, text analytics, and sentiment analysis we are able extract more sublime features of quality.
Total Quality Management
(TQM) at ISS is an all-encompassing corporate process and culture involving:
Quality circle operations that provide a cyclical flow of information with emphasis on continuous improvement.
Recognizing both design specifications of the customer but also the more salient engineering details that also contribute to overall customer satisfaction.
Continuously evaluating and redefining the corporate mission of the organisation.
Involving all personnel in identifying, reporting, and consultation operations on how the goals of the corporation could be better achieved.
Conceptualizing ways in which performance could be approved and validated in terms of feasibility.
Quantifying and measuring, throughout the process, and objectively judging how well performance meets the required customer design specifications.
Critically and fairly investigating customer feedback for veracity, relevance, and patterns of behavior against larger population sets.
Providing actionable and understandable project data to our customers to increase the accuracy, quality, and thoroughness of their feedback.
Using a combination of predictive analytics and prescribed maintenance to anticipate issues and proactively prep business solutions.
Rigorous business specific training that challenges people to grow, innovate, and become inoculated to greater challenges.
TQM is more than a system, it's a corporate philosophy for quality until a company reaches it's full quality potential.
Revisiting Quality Control Legacy Systems with Data Science and AI
Corporations have built quality control systems on the basis of Kaizen, Kanban, Six Sigma, Statistical Process Control (SPC), Lean Manufacturing, Six S's, Total Product Maintenance (TPM), and Key Performance Indicators (KPI). Now with various and affordable breakthroughs in data science, big data applications, SQL, Artificial Intelligence, and Data Mining these processes can be revisited and revamped for higher performance.
This paradigm also correlates to "Smart Manufacturing" which is about connecting machines for higher levels of learning, adaptability, and rapid changes based on feedback control loop systems. In order to keep up with top performers and to beat previous quarterly earnings it will be essential to upgrade legacy systems in quality.