C O N T E N T S. Brief introduction to Six-Sigma. Case development and hands-on exercises. Conclusions. August 9, 2013 Santiago, Chile

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1 TheApplicationof Six-Sigma DMAIC to a Distribution System Edgardo J. Escalante, Ph.D. ITESM México Pan-American Advanced Studies Institute on Modeling, Simulation and Optimization of Globalized Physical Distribution Systems (PASI) August 9, 2013 Santiago, Chile 1 C O N T E N T S Brief introduction to Six-Sigma Case development and hands-on exercises Conclusions 2 1

2 The Scientific Method The quality movement and continuous improvementcan be seen as the application of the Scientific Method a tool to obtain newknowledge Box (199) 3 TheMeaningof SixSigma Itisa metric, aworkingphilosophy,and atarget 2

3 The meaning of Six Sigma As a metrics way of measuring the performance of a process As a working philosophy continuous improvement of processes As a target world class performance process (3. part-per-million opportunities) SIX SIGMA is a BUSINESS STRATEGYto satisfy CUSTOMER requirements 6 3

4 DR. GENICHI TAGUCHI S CONCEPTS Taguchi (1987) An important product quality dimension is the total loss to society In a competitive economy, quality continual improvement and cost reductions are needed to survive A quality continual improvement program includes a constant variation reduction of a product s characteristics with respect to their target value 7 Classic definition of Quality Conformance to specifications for fitness for consumer use Lower specification limit (LSL) Upper specification limit (USL) Non conforming product Conforming product Target value Non conforming product 8

5 Modern definition of Quality Uniformity around a target value Sullivan (198) Lower specification limit (LSL) Upper specification limit (USL) Non conforming product Non conforming product Target value 9 Dr. Taguchi s Definition of Quality The loss a product causes to society if it s not performing at its target value (m) LSL USL LSL USL Bad Good m Good Bad m Taguchi (1987) 10

6 Two important things regarding process control and improvement: Process variation Process mean 11 Definition of variation For Shewhart(1931), sampling variation or fluctuations are defined as differences between things even if produced under presumably the same conditions. 12 6

7 Importance of Variation and Centering Service evaluation in a bank in ten occasions: Bad service Excellent service Variation Variation Is reducing variation enough? Bad service Excellent service 1 7

8 What about centering too? Target Bad service Excellent service 1 The planning of statistical experiments is used to identify the optimum values of the parameters that reduce variation QUALITY 16 8

9 Graphical meaning of Six Sigma σ isa Greekletterusedtorepresenta measureof a process variation LSL Cp=Cpk=1 USL σ process σ σ Graphical meaning of Six Sigma LSL Cp=Cpk=2 Centered 6σ process USL σ σ 18 9

10 Graphical meaning of Six Sigma Cp=2, Cpk=1. Official 6σ process LSL 1.σ USL σ 19 World class standard evolution σ Process capability PPM 308,37 66,807 6, Defects per million opportunities Historic standard US companies New standard 20 10

11 Distinctive characteristics CEO s direct involvement and leading Six Sigma Project evaluation, approval and following by the finance function Integration of existing techniques into a structured methodology Six Sigma is an important part of the individual performance evaluation Of immediate application to GB or BB projects It can be applied to any area within an organization 21 DMAIC Process EstablishControlson the critical Xsso the improvements will be maintained Identify ways to improve the process and validate the solution X 1 X 2 X 3 X PROCESS Y 1 Y 2 Y 3 Metrics (Ys) linked to CTQs Define the Problem Project Objective Project Goal MeasureandAnalyzedata and process performance to determine the critical variables and root cause of the problem Little (2002) 22 11

12 Overall Approach Practical Problem Statistical Problem y = f ( x1, x2,..., x k ) Practical Solution Statistical Solution Zinkgraf S. y Snee R. (1999) 23 SIX SIGMA-DMAIC phases Previous activities Identify project, champion and project owner Team defined and fully trained DEFINE Define customers and CTQs Define project charter Title, Business Case (Problem definition, COPQ, baseline and entitlement), objective and goals, scope, resource requirements, financial benefits, project approval team members and estimated time Define project plan Develop a high-level process map 2 12

13 MEASURE Develop a detailed process map Identify inputs and outputs Perform measurement system analysis Establish process capability baseline and entitlement ANALYZE Identify potential critical inputs Determine the critical inputs Adjust the process Evaluate new process capability 2 IMPROVE Optimize critical inputs Generate and test possible solutions Select the best solution Design implementation plan Evaluate new process capability CONTROL Develop a monitoring and control plan Verify final process capability Obtain owner sign-off Elaborate a final report 26 13

14 DMAIC FLOW D M Define problem Describe process Stable process Y N Eliminate special causes M M A Stable/capable measurement Determine & validate significant variables. Adjust process N Improve Evaluate process capability & stabilityr (baseline/entitlement) Y I C Capable process N Optimize Control process Y A Evalute process stability & capability C Improve continuously 27 Case development Rent A Linen company Products: bed sheets, towels, medical linen, etc. Customers: major hospitals in a large metropolitan area and in neighboring towns One important customer satisfaction indicator (CTQ): Response time to customer s orders (OTD), target no more than 8 hours since order reception 28 1

15 Problem description The % of late shipments has increased as shown Time Series Plot of % LateShipments 6 %LateShipments 3 2 Nov Dec Jan Feb Mar Apr Month May Jun Jul Aug Sep 29 I Chart of %LateShpments 7 6 UCL= Individual Value 3 1 _ X=.0 LCL= Nov 1 Dec Jan Feb Mar Apr Index May Jun Jul Sep Oct 30 1

16 Xbar Chart of Cap by Stage UCL= _ X=.788 B Sample Mean LCL= E Nov Dec Jan Feb Mar Apr Index2 May Jun Jul Aug Sep 31 Project Charter Project title Reduction of the percent of late shipments Business Case Problem definition/response variable The % late shipments has significantly increased since Feb. Response variable (Y): % Late shipments, measured as the (number of late shipments divided by the total number of shipments)*

17 Business Case (cont.) Cost of poor quality (COPQ) The monthly average % late shipments is.79 (B, baseline) since February. Each late shipment costs $300 and there are an average of 00 shipments/month (100/weekly) The average monthly total cost is 0.079*300*00=$6,98/month or $83,376 a year 33 Goal and target To reduce the % late shipments to no more than 2% (E) (entitlement) by 1st March (6 months). Scope and limitations Applicable to deliveries within the metropolitan area Estimated resources $6,000 (tests, personnel, meeting room, statistical software) Expected economic benefits Expected new COPQ=2%*300*00=$2,00/month or $28,800 yearly The expected yearly benefits are $83,376 $28,800=$,76 Benefits from first year will be $,76 $6,000=$8,

18 Champion (name/signature) Finance approval (name/signature) H. Virtos J. Moredo Process owner (name/signature) Estimated time L. Moranteso 6 months Black Belt (name/signature) Team members (name/signature) R. Martecas R. Mataes, L. Recado, G. Gorid 3 Critical-to-quality characteristics (CTQ) tree Response variable (Y) % Late shipments Customer requirements Orders delivered on-time Measurement (No. late shipments/total No. shipments)*100 Target No more than2% of late shipments 36 18

19 SIPOC DIAGRAM S=Suppliers I=Inputs P=Process O=outputs C=Customers Suppliers Inputs Process Outputs Customers Sales dept. Suppliers of Orders Order Gas, services reception Local and Water Lots of Suppliers of Order neighboring Electricity linens linens processing Linens delivered Suppliers of eq. Chemicals Schedule hospitals & chemicals People delivery HR dept. 37 Quick exercise A tools distribution company has seen a decrease of its on-time deliveries and decided to analyze the situation. Based on a sample of its last 100 shipments the problem-solving team elaborated the following histogram depicting the characteristic (CTD) delivery time. Specs are less or equal 2h and the team set an improvement target of 0%. Briefly define the problem

20 20 HISTOGRAM USL Frecuency Hours 39 DEFINE THE PROBLEM Define the problem using one or more of the following tools: Pareto chart Histogram Run chart Audits Critical-to-Quality (CTQ) Problem Target 0 20

21 MEASURE Develop a detailed process map Identify inputs and outputs Perform measurement system analysis Establish process capability baseline and entitlement 1 Example. Rent A Linen distribution company PROCESS MAP -Quantity & type -Processing time -Delivery time -No. of available trucks Start Order reception Prepare order Schedule delivery Send items -Information accuracy -Scheduled delivery time End? %Late shipments = f (Inf. accuracy, processing t,, No. of avail. trucks) 2 21

22 Measurement System Analysis Reliability and dependability of measurements Make appropiate decisions Studies stability, linearity, bias, repeatability& reproducibility 3 Example. Rent A Linen distribution company The order reception systems is automated and remotely accessed by customers for placing orders. This system is serviced at appropriate time intervals. Order s reception time is set by the customer when he signs an electronic reception sheet connected directly to Rent A Linen. 22

23 Example. Rent A Linen distribution company MEASUREMENT SYSTEM EVALUATION Order # Time 1 {1} Time 2 {2} Gage R&R Study Var %Study Var Source StdDev (SD) (6 * SD) (%SV) Total Gage R&R Repeatability Part-To-Part Total Variation {1} from automated system {2} by hand Process capability baseline and entitlement Basedon 8 previousmonthsof weeklydata I-MR Chart of %LT-week 10 UC L=9.9 Individual Value 8 6 USL _ X= LC L= O bser vation UC L=.31 Moving Range M R= LC L= O bser vation

24 Probability Plot of %LT-week Normal Percent Mean.871 StDev N 32 AD 0. P-Value %LT-week USL=target<=02 Process Capability of %LT-week Process Data LSL * Target * USL 2 Sample Mean.8707 Sample N 32 StDev (Within) StDev (O v erall) USL Within Overall Potential (Within) C apability C p * C PL * C PU -1.0 C pk -1.0 O v erall C apability Pp * PPL * PPU Ppk C pm * O bserv ed Performance PPM < LSL * PPM > USL PPM Total Exp. Within Performance PPM < LSL * PPM > USL PPM Total Exp. O v erall Performance PPM < LSL * PPM > USL PPM Total

25 ANALYZE Identify potential critical inputs Determine the critical inputs Adjust the process Evaluate new process capability IMPROVE Optimize critical inputs Generate and test possible solutions Select the best solution Design implementation plan Evaluate new process capability 9 Process variables NOMINAL GROUP TECHNIQUE GROUP MEMBERS Process variables TOTAL Rank Information accuracy Quantity & type Processing time Scheduled delivery time Delivery time No. of available trucks

26 Cause-and-Effect Diagram ROUTES PEOPLE Not up to date Suboptimized Lack of drivers Wrong programming Not standardized Not automated O bsolete Not appropriate Lack of training Lack of motivation Lack of supervision Errors Not enough Broken down Unav ailable HIGH PRO C ESSING TIME PROCEDURES TRUCKS 1 Experiential exercise-product/process optimization One wishes to build a helicopter prototype that has the longest Y=Flying time {Processing time} once dropped from a certain height. A: Wing length (2, 3 ) No. of trucks {a, b} B: Body length (2, 3 ) No. of routes {c, d} What kind of design is it? C: Body width (1, 1. ) No of drivers {e, f} Adapted from Box (1992) 2 26

27 Design Matrix(coded units) StdOrder WINGS BODY WIDTH TIME Design Matrix(uncoded units) StdOrder WINGS BODY WIDTH TIME

28 Material needed: 8 letter-sized sheets 8 scissors 8 small paper clips 2 glue sticks to share 1 chronograph 3 Not to scale A Wings 2 (-1) 3 (+1) 1 (fixed) Cut Fold B Body 2 (-1) 3 (+1) Width C (1 (-1), 1. (+1)) Depending on the dimensions of your prototype it should look like this Glue here Paper clip Final prototype 6 28

29 Study of mean flight time Design matrix in original or uncoded units (inches) in random order using Minitab Factors influencing flying time: Optimum levels to maximize flying time: 7 Dotplot of Processing time, Processing time-new {h} Processing time Processing time-new 2 3 Data

30 I-MR Chart of %LT-week by stage Individual Value Before improvements Observation 29 After improvements UC L=2.9 _ X=1.6 LC L= Moving Range UC L=1.620 MR=0.96 LC L= Observation Process capability after improvements I-MR Chart of %LT-week 3 UC L=2.9 Individual Value 2 1 _ X=1.636 LC L= Observation UC L=1.620 Moving Range MR= LC L= Observation

31 Probability Plot of %LT-week3 Normal Percent Mean StDev N 12 AD 0.21 P-Value %LT-week Evaluate new process capability after adjustments Process Capability of %LT-week Process Data LSL * Target * USL 2 Sample Mean Sample N 12 StDev (Within) StDev (O verall) USL Within Overall Potential (Within) C apability C p * C PL * C PU 0.28 C pk 0.28 Ov erall C apability Pp * PPL * PPU 0.31 Ppk 0.31 C pm * O bserved Performance PP M < LSL * PPM > USL PPM Total Exp. Within Performance P PM < LSL * PPM > USL PPM Total Exp. O v erall Performance P PM < LSL * PPM > USL PPM Total

32 CONTROL Develop a monitoring and control plan Verify final process capability Obtain owner sign-off Elaborate a final report 63 In addition to optimizing the process, some SOP where developed to keep them enforced Control charts were set in place to monitor KPIVs/KPOVs 6 32

33 Final remarks Six Sigma Scientific method Knowledge Improvement ability Quality& productivity improvement Systematic innovation BUSINESS STRATEGY Bisgaard and DeMast (2006); Escalante (2008) 6 REFERENCES Bisgaard S., De Mast J. (2006). After Six Sigma, what s Next?. Quality Progress, January. Box G. (199). Total Quality: Its Origins and Its Future. Report No. 123, CQPI, UW. January. Box G. (1992). Teaching engineers experimental design with a paper helicopter (George s Column). Quality Engineering, Vol., No. 3. Escalante E. (2008). Full Speed Ahead. Six Sigma Forum Magazine, May. Little T. (2002). Six Sigma Executive Overview. Thomas Little Consulting. Shewhart W. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Co. Inc. Sullivan L. P. (198). Reducing Variability: A New Approach to Quality. Quality Progress, July. Taguchi G. (1987). Introduction to Quality Engineering. A.P.O. ZinkgrafS. y SneeR. (1999). Institutionalizing Six Sigma in Large Corporations: A Leadership Roadmap. Quality and Productivity Research Conference