MANAGING FOR QUALITY PROCESS IMPROVEMENT

1. 1 MANAGING FOR QUALITYPROCESS IMPROVEMENT DR. YONATAN RESHEF UNIVERSITY OF ALBERTA SCHOOL OF BUSINESS EDMONTON, ALBERTA CANADA T6G 2R6

2. 2 STABLE SYSTEM/PROCESS A PROCESS WILL BE IN STATISTICAL CONTROL WHEN, THROUGH THE USE OF PAST EXPERIENCE, WE CAN PREDICT, AT LEAST WITHIN LIMITS, HOW THE PROCESS WILL BEHAVE IN THE FUTURE

3. 3 PROCESS IMPROVEMENT IMPROVEMENT OF A STABLE PROCESS CANNOT BE DONE BY TAMPERING WITH OUTPUT (E.G., MANAGING BY RESULTS) ACTION BASED ON RESULTS CAN ONLY BE APPROPRIATE IN THE PRESENCE OF SPECIAL CAUSES

4. 4 CAUSES OF VARIATION SPECIAL CAUSES (SIGNAL) – PROBLEMS ATTRIBUTABLE TO INDIVIDUALS WHO ARE OUT OF STATISTICAL CONTROL COMMON CAUSES (NOISE) – PROBLEMS ATTRIBUTABLE TO THE SYSTEM (I.E., MANAGEMENT)

5. 5 VARIATIONTWO COMMON MISTAKES OVER-ADJUSTMENT – ASCRIBING VARIATION OR A MISTAKE TO A SPECIAL CAUSE WHEN IN FACT THE CAUSE BELONGS TO THE SYSTEM DOING NOTHING – ASCRIBING VARIATION OR A MISTAKE TO THE SYSTEM WHEN IN FACT THE CAUSE IS SPECIAL

6. 6 TAMPERING WITH A SYSTEM TAKING ACTION ON A STABLE PROCESS IN RESPONSE TO PRODUCTION OF A FAULTY ITEM OR A MISTAKE (OVER-ADJUSTMENT)

7. 7 INSPECTION, OR NO INSPECTION IF PROCESSES ARE IN STATISTICAL CONTROL, THERE ARE ONLY TWO CHOICES: NO INSPECTION OR 100% INSPECTION IF PROCESSES ARE IN CONTROL, A SAMPLE FROM A BATCH CONTAINS NO NEW INFORMATION CONCERNING THE UNINSPECTED ITEMS IN THAT BATCH THE CHOICE BETWEEN THE TWO ALTERNATIVES – WHETHER TO INSPECT OR NOT – IS MADE ON THE BASIS OF ECONOMICS, SAFETY, ETC.

8. 8 CHAOS A “STATE OF CHAOS,” THAT IS WHEN PROCESSES ARE OUT OF CONTROL, DESERVES CONSIDERATION OF 100% INSPECTION

9. 9 LESSONS FROM THE RED BEAD EXPERIMENT THE PROCESS TURNED OUT TO BE STABLE – THE VARIATION AND OUTPUT WERE PREDICTABLE ALL THE VARIATION CAME ENTIRELY FROM THE PROCESS ITSELF. THERE WAS NO EVIDENCE THAT ANY WORKER WAS BETTER THAN ANOTHER

10. 10 LESSONS THE WORKERS COULD DO NO BETTER. “BEST PEOPLE DOING THEIR BEST” DOES NOT ALWAYS WIN THE DAY UNDER SUCH CIRCUMSTANCES, RANKING IS WRONG, AS IT ACTUALLY MERELY RANKS THE EFFECT OF THE PROCESS ON PEOPLE

11. 11 LESSONS PAY FOR PERFORMANCE CAN BE FUTILE. THE PERFORMANCE OF THE WORKERS WAS GOVERNED BY THE PROCESS DIVIDED RESPONSIBILITY – THE INSPECTORS WERE INDEPENDENT OF EACH OTHER (A POSITIVE PRACTICE).

12. 12 LESSONS KNOWLEDGE ABOUT THE PROPORTION OF RED BEADS IN THE INCOMING MATERIAL (20%) WOULD NOT ENABLE ANYONE TO PREDICT THE PROPORTION OF THE RED BEADS IN THE OUTPUT. THE WORKLOADS WERE NOT RANDOM DRAWINGS. THEY WERE EXAMPLE OF MECHANICAL SAMPLING

13. 13 SAMPLING EVERY BEAD MUST HAVE A CHANCE TO BE IN THE SAMPLE IN OTHER WORDS, RANDOM SAMPLING MUST BE INDEPENDENT OF ANY PHYSICAL ATTRIBUTION OF THE EXPERIMENT COLOR OF THE BEADS SHAPE OF THE PADDLE ANGLE OF THE RAISING OF THE PADDLE SIZE OF THE SAMPLING BOWL

14. 14 LESSONS Acceptable Defects: Rather than waste efforts on zero-defect goals, Dr. Deming stressed the importance of establishing a level of variation, or anomalies, acceptable to the recipient (or customer) in the next phase of a process. Oftentimes, some defects are quite acceptable, and efforts to remove all defects would be an excessive waste of time and money.

15. 15 LESSONS THERE WAS NO BASIS FOR MANAGEMENT’S SUPPOSITION THAT THE 1-2 BEST WORKERS OF THE PAST WOULD BE BEST IN THE FUTURE RIGID/PRECISE PROCEDURES ARE NOT SUFFICIENT TO PRODUCE QUALITY NUMERICAL GOALS CAN BE MEANINGLESS