In April’s edition of Ground Support Worldwide I remarked upon the relevance of engaging with customers to understand of how they run their business and learn how they measure quality of service. This month’s article focuses on the relevance of accurate data as a key contributor to the success of a maintenance organization’s Quality Management System (QMS).
The International Organization for Standardization (ISO) best describes all the characteristics and requirements of a QMS. Simply put (and slightly paraphrased), a QMS is an enabler which allows organization a better ability to “….identify, measure, control and improve the various core business processes”. If your organization is serious about using a QMS to satisfy customers and you have a role or responsibility than you’re going to need timely and accurate data to accomplish all of that.
We can agree on the premise that it is difficult to place a definitive value on the quality of data that a company uses. Anyone involved in measuring and managing based on Key Performance Indicators (KPIs) will undoubtable tell you that timely and accurate data is an invaluable intangible asset.
According to sources, “A majority of organizations use only a fraction of their enterprise information to gain the kind of actionable insight needed to facilitate superior business performance”. Admittedly I’ve been associated in the past with maintenance organizations that have well-trained mechanics, use proven work methods and well-written procedures, and yet they struggle in terms of timely and accurate data entry. Resultantly there is failure in the ability to effectively utilize any form of process -based QMS to improve business performance and customer satisfaction.
Case for Better Data
Maintenance organizations of all kinds make decisions and service customers based on the data they have at their disposal. The crux of that data comes from the mechanics; whether it be in the form of putting pen to paper to document their work or direct data entry into a maintenance management system.
Translating mechanic effort into accurate data requires discipline. Very few fleet maintenance mechanics that I know of started out with a clear understanding of how to properly document their efforts, or fully appreciate the bearing that their “paperwork” (i.e. data entry) has on an organization’s ability to measure quality of service to its customers. Therefore, as part of a QMS mechanics must be trained how to complete their paperwork and must be held accountable for the accuracy of data entry.
Quality Assurance is achieved by the maintenance organization when steps are incorporated into the maintenance procedures that require the mechanic doing the work to accurately document their efforts, and the person supervising the work approves the quality of workmanship as well as the associated documentation and data entry. These measures also allow for strong partnership to exist between the shop and the business unit(s) that govern the data and that are ultimately responsible for establishing the business rules or procedures.
Quality assurance of data is also a prerequisite to ‘Quality Control’. Essential to quality control is the review data for routine reappraisal of the effectiveness of an organization to meet customer requirements. This involves taking time and discipline to go back at the completion of work and routinely review baseline readings for condition monitoring of quality of service. Steps can then be taken for changing the focus from just getting baseline readings to quality control of the work being done.
Don’t underestimate the value of accurate data. Those organizations that train and hold mechanics accountable for the accuracy of their paperwork and routinely review the data are in a much better position to satisfy a customer than those that don’t.
References:
Unknown Author, (2015)ISO 9001:2008 Quality management systems – Requirements. Found at: http://www.iso.org/
Geiger, Jonathan G. (2014) Data Warehousing, Management and Quality, Data Quality Management The Most Critical Initiative You Can Implement, Paper 098-29, Intelligent Solutions, Inc.
Staff, (April 30, 2014) EnterpriseAppsToday.com, Best Practices for Data Quality Management. found at: http://www.enterpriseappstoday.com/data-management/5-best-practices-for-data-quality-management.html
