Information and Analysis

In God we trust; all others, bring data.” - Dr J M Juran

Managers now understand that reliable, appropriate data are the lifeblood of a quality improvement system.  Without systematic data collection, companies simply cannot know how their processes are performing, what progress has been made, what needs improving, or what the future holds.  Without data, gains in quality improvement cannot be translated into that most elusive statistic, precise cost savings – a statistic that captivates top management and dramatizes the success of quality improvement projects as nothing else can.  In my personal opinion, data analysis is the single largest deficiency in Indian industry today.

We all know that many companies still do a poor job of gathering relevant data, and many more do not analyze or use collected data.  The fourth criterion of the IMC Ramkrishna Bajaj National Quality Award is asking you if your company is the exception to this generalization.  This criterion, Information and Analysis, examines the range and variety of your company’s data and information; your processes for analyzing and managing them; and the role they play in your planning process.  It also asks about your company’s approach to benchmarking – establishing quality improvement targets based on best practices.

Companies that score well in this criterion are easy to spot.  Examples: Infosys, HDFC.  They are the ones that perform frequent evaluations, that continually validate and update their data and information bases, and that actively analyze and use data to inform their planning, decision-making and improvement processes; that is, they “manage by fact”, not by personal fancy, feel, or whim.  These companies are also relentlessly single-minded in finding and using world-class benchmarks for performance.  On the other hand, companies that do not score well are just as easily identified.  They do not, as a rule, have much of a statistical orientation.  Too often, these companies fall back on management by opinion, not by fact.

Continuous improvement requires many kinds of data – data about company performance and internal operations, customer-related data, benchmarks from other companies, and financial and cost-of-quality calculations to identify and evaluate new opportunities.  The primary unit for measuring service performance at Federal Express is called Service Quality Indicator (SQI).  It is derived from twelve attributes of service quality – such as missed pickups, late deliveries, damaged packages, and other critical operating data.  Together they provide the means for the company to monitor customer satisfaction or dissatisfaction.  Federal Express calculates the impact of failure per day for each component by multiplying the number of daily occurrences for that component by its assigned importance weight.  The SQI is the sum of the failure points for all 12 components, and it is tracked, compared against targets, and reported on a weekly basis with monthly summaries.  Corporate SQI presentations are then delivered to executives and senior operational managers and evaluated using several different perspectives: root-cause analysis, understanding the flow and process of the problem being worked on, involvement of first-line employees, and results.

Apart from planning, a continuous, ongoing review of the way in which data is analyzed is essential for continuous improvement.  Cycle-time reduction, such as the cycle-time from design to introduction, is a major focus for improvement and a key source of competitive advantage.  A major issue is which cycle-times to focus on?  I recommend those that have the greatest bearing on your business’s ability to satisfy customers.  Various cycle times differ in importance from industry to industry.  In the fashion industry the design-to-market cycle is critical, and billing cycles may be less important. In automobile manufacturing, the strategic planning cycle may be most critical.

Managers now understand that data gathering can either be a very rewarding or very wasteful experience.  The science of data gathering is based on management’s ability to ask the right questions.  Further, they should know what information will enable them to answer the questions.  Managers also know that data that is analyzed transforms to information.  But is the analysis effective?  To my mind, analysis is the key issue.

The means for analysis is application of the right (combination of) tools.  Quality tools that are a must include flowcharting, cause-and-effect diagram, histograms, Pareto charts, scatter diagrams, stratification, and control charts.  Management tools such as break-even analysis, make-or-buy decision, cost of poor quality, customer satisfaction measurement, benchmarking, employee satisfaction measurement, and quality function deployment should also be known, at the level of basic quality literacy, by world-class organizations.  In addition, I recommend a healthy mix of several statistical, reliability and creativity tools to analyze data for Six Sigma process performance.  There is no short-cut to information and analysis, for excellence.