DAMAIC Project Due

projectfilesFall2020.zip

Home >Applied Sciences homework help >DAMAIC Project Due

Data_Analysis_Tool_list_wDMAIC_3e_rev2.doc

Data Analysis Tools

Tool	What is it?	When do I use it?	Example	DMAIC
Affinity Diagram	A method to gather and organize brainstormed ideas (language data) into groupings based on the natural relationship between items.	When you need to organize and consolidate large amounts of qualitative data in order to support a concept or solution.	http://en.wikipedia.org/wiki/Affinity_diagram	Measure, Analyze, Improve, Control
Box and Whisker Plot	A graphical way to compare the medians and the variation between groups of data. It can also help to identify outliers.	When you need a quick visual look at one or more sets of data. They graphically show different types of populations without any assumptions of the statistical distribution.	Discovering Stats 3e – pg.173-176	Measure, Analyze, Improve
Cause and Effect / Fishbone / Ishikawa Diagram	A structured problem solving technique that graphically displays / organize all possible brainstormed causes relating to a problem (focusing on causes, not symptoms).	When you have a large number of factors that could influence your process and you need support for a resulting solution.	Slides posted in Coursework	Analyze
Check Sheets	Used for counting and accumulating data in a logical format during observation. This is a straightforward and easy way to answer the question, how often are certain events happening?	When quantifying frequency / counting data on number of occurrences (eg. defects), information on variables (eg. weight, size, length, or defect location, etc.).	http://web2.concordia.ca/Quality/tools/6cksheet.pdf	Measure
Chi-Square – Test for Independence	This is a procedure used to determine if two classifications variables are related, testing the statistical independence of two random variables. It compares the number of observed counts against the expected number of counts to determine if there is a difference in output counts.	It is a good method to apply to before and after data to prove that a process improvement made an effective change. Use this method when you have nominal data in a table and you need to know if the output counts differ for two or more categories.	Discovering Stats 3e – pg. 646-651	Analyze, Improve
Confidence Intervals	A range of values in which we expect the population parameter to occur. A population parameter is likely to occur at a specified probability. It is constructed from sample data.	Confidence intervals determine if a process is centered where it is expected. They are used to identify a shift or change in the process (mean) and to identify a difference in two populations (eg. does Vendor 1 and Vendor 2 give us the same dimensioned part?)	Discovering Stats 3e – pg. 426-484	Measure, Analyze, Improve
Control Chart	Can serve as a statistical tool for problem identification as well as, ongoing monitoring of a process (graphically) over time. It can assist in distinguishing random variation (noise) from assignable variation (signal).	When you need to recognize and eliminate sources of variation in a process so that a process performs consistently and predictably.	Understanding Variation by D.J. Wheeler	Measure, Analyze, Improve, Control
Correlation Coefficient	Correlation coefficient R is a measure of the strength and direction of the linear relationship between 2 random variables. R falls on or between the numbers -1 and 1. Coefficient of determination R2 is a measure representing the percent of variability in “y” that can be accounted for by the variable x.	Although correlation does not explicitly imply causation, establishing a correlation between two variables is necessary (but not sufficient) to establishing a causal relationship.	Discovering Stats 3e – pg. 192-199	Measure, Analyze, Improve
Fault Tree Analysis	It is a visual tool that logically and graphically presents the various combinations of possible events or failures that could occur in a process or product. It is an inverted tree with the trunk (noting the failure or undesired event) of the tree at the top of the diagram and the branches are the contributing causes of the failure.	When a simple visual is needed for determining (and presenting) the root cause of a failure.	http://en.wikipedia.org/wiki/Fault_tree_analysis http://www.weibull.com/basics/fault-tree/	Measure, Analyze, Improve
Force Field Analysis	Method to present the “positives” and “negatives” of a situation so that they can be compared, the positives can be reinforced and the negatives can be eliminated.	When the desired outcome is “making a change” in the midst of forces /barriers restraining movement towards the ideal state.	http://www.mindtools.com/pages/article/newTED_06.htm	Measure, Analyze, Improve
Histogram	A graphical method of displaying the distribution of data by bar graphing the number of units of a particular category (illustrates process centering, spread and shape).	When displaying large amounts of data that are difficult to interpret in tabular form.	Discovering Stats 3e – pg. 65-66	Measure, Analyze, Improve
Hypothesis Testing	Hypothesis testing is a process (inferential method) that uses sample data from a population to confirm or refute some statement or claim about that population.	Hypothesis testing can tell us if two sets of data are really different from each other. Determines statistically whether or not there is a cause for concern or if our conclusion is simply due to random variation. It can be used to determine whether a population parameter (mean, variation, etc.) is statistically different than a standard or set value.	Discovering Stats 3e – pg. 486-555, 574-629	Analyze, Improve
Linear Regression Analysis	This tool will help you to understand the relationship between the process output and any process input that could affect it. It is a way to model or predict the relationship between those variables.	When you suspect there is a relationship between an input and output variable. It is especially useful when the output variable is difficult or expensive to measure and the input variables are not.	Discovering Stats 3e – pg.186-237, 743-759	Analyze
Measurement System Analysis (MSA)	A process that includes identifying, quantifying and reducing measurement errors. Measurement systems (a decision making tool) play a large part in process improvement activities.	On every measurement system that a decision is based upon.	http://www.6sigma.us/MeasurementSystemsMSA/measurement-systems-analysis-MSA-p1.html Slides posted in Coursework	Measure, Analyze
Measures of Center (Descriptive Statistics -Measure of Location)	The behavior of the middle (or central portion) of the population of process data. 3 measures are: Mean = arithmetic average Median = middle value Mode = most frequent value	When you need a quantitative measure that summarizes an important characteristic of a population / process, the center of your data.	Discovering Stats 3e – pg.108-117	Define, Measure, Analyze, Improve, Control
Measures of Variability (Descriptive Statistics -Measure of Dispersion or spread)	A measure of how the data is spread around the mean. 3 Measures are: Range = difference between the largest and the smallest data point Standard Deviation = measure takes into account each data point and its distance from the mean Variance = standard deviation squared	On any set of data - all populations and processes have some degree of variability.	Discovering Stats 3e – pg.126-137	Define, Measure, Analyze, Improve, Control

Pareto Chart/Diagram	A descending bar and cumulatively increasing line chart used to separate the vital few from the trivial many. The vital few are the few factors accounting for the largest part (%) of a problem or condition. Pareto Principle: 20% of the sources cause 80% of the problem.	When you need to focus on the key problem(s) - when solved will have the greatest impact.	Discovering Stats 3e – pg. 43-44	Define, Measure, Analyze, Improve
Process Map (or Process Flow chart)	A graphical tool for documenting a process. Each step or activity is mapped out as it occurs in the real-live process.	When improving or creating a process	http://en.wikipedia.org/wiki/Business_process_mapping	Define, Measure Analyze, Improve, Control
Run Chart (Time Series Plot)	A graphical tool that can show and track trends or patterns over a specified time period.	When you need to do the simplest possible display of a trend over time.	Discovering Stats 3e – pg.89-91	Measure, Analyze, Improve
Scatter Plot/Diagram	A graphical tool to visualize the possible relationship between two variables and relative strength of that relationship.	When you need to display what happens to one variable when another variable changes (visualize a relationship between two variables).	Discovering Stats 3e – pg.188-192	Measure, Analyze, Improve
SIPOC (Supplier / Input / Process / Output / Customer)	A simple way to document (or map) a process in the “as is” (current) state by listing the suppliers, inputs, outputs and customers. The “process” should be identified by a high level flow chart. This sets the scope for the value-stream map.	This is a quick way to document your process (and start to analyze) when parts of the process are not clear or consistent (ie. Who supplies inputs to the process? Who are the true customers of the process? Is there a customer for each output? What are the requirements of customers?)	Slides posted in Coursework http://www.isixsigma.com/library/content/c010429a.asp	Define, Measure
Stem and Leaf plot (Stemplot)	A graphical technique that shows the shape (distribution) of the data like a histogram but displays all of the individual values within an interval rather than just the frequency for each interval.	When evaluating the shape of the data with the ability to maintain visibility to the original raw data points.	Discovering Stats 3e – pg.68-71	Measure, Analyze, Improve
Solution Selection Matrix	A matrix that helps identify the best solution among several solutions by weighting the impact of each solution on established criteria (ie. cost, time to implement, impact on quality, etc.), hence measuring the effectiveness of solving the problem with that solution.	Use this when you have a couple solutions to choose from and want to make your decision fact based, taking more factors into consideration.	http://www.whatissixsigma.net/six-sigma-dmaic-improve-phase/	Improve
Thought Process Map (TPM)	A logical, visual representation or map of someone’s thought process flow (of questions, tools used to help answer the question, related actions and related decisions) that shows how a process or problem was/is attacked / addressed.	Use this for any situation. This is an “evergreen” document that can be used as a communication tool for where you are, where you’ve been and where you are going.	Slides posted in Coursework	Define, Control

EXAMPLE_storyboard_example_students.ppt

MBC638

Data Analysis

DEFINE

MEASURE

Web Metrics Cost Reduction

Team

Launch

Define

Measure

Analyze

Control

Improve

Key Dates --->

ANALYZE

IMPROVE

Process owner: John

CONTROL

Joe, Pat and Dave

8/28

Fee for web

Analytics Service up over 20%

Annual Budget

is at risk of overrun

Cpk = 0.382

9/3

9/5

9/7

Invoice cost increasing

Page view volume increasing

Page view volume increase

starts with data collector changes

One data

collector

per page

Invoice cost lowered 15%

10/15

Data collector tag instructions

added to operational definitions

Page view volumes are reviewed

Monthly

- Invoices are reviewed monthly

- Control charts are maintained

More than one data collector found on some pages

DEFINE

MEASURE

Process Improvement Project – Cycle Time Reduction

Team Launch

8/23

Define

9/08

Measure

10/16

Analyze

10/24

Control

On-Going

Improve

10/31

Key Dates --->

ANALYZE

IMPROVE

Process owner: Dan

CONTROL

PROJECT TEAM: Dan  Mary  Karen  Linda  Peter

BUSINESS CASE: $54,000 in annual processing costs

It takes 43 days to process a grant application. Only 8% of applications are being processed within 30 days of receipt. The time to process the application has lead to unhappy applicants and staff who are finding more and more of their daily work time being devoted to “grant administration.” The funding levels available to applicants and the number of applications are expected to increase in the near future, which has the potential to compound the problem.

Defects/delays are inherent in the current process. Current SQL is 1.9

The Number of applications received is increasing.

The time to complete a process cycle is also increasing.

Problem: Incomplete and inaccurate applications were identified as the primary factor leading to defects in the process cycle.

Solution: New Application process incorporating drop down menus

New Application Procedure =

Less Mistakes & Quicker Cycle Time

The defect rate reduced from 93% to 32%

Monthly monitor and review procedure is in place. Out of control signal = action plan.

↑ Number of Applications + ↑ Cycle Process Time __Tough Times Ahead

DEFINE

MEASURE

Closing the Gap in Incoming Material Analysis

Team Launch

Define

Measure

Analyze

Control

Improve

Key Dates --->

ANALYZE

IMPROVE

Process owner: Bob

CONTROL

Buyer (myself), Vendor, Quality, Accounting, Manufacturing

September 8

October 10

November 10

January 10

September 14

January 15

Longer lead time for vendor payment.

Additional time reconciling invoices.

Increased inventory levels waiting for third party analysis.

Animosity in relationship.

25% discrepancies between Receiving & Vendor lead to:

reproducibility

= .0022

Precision-to-total ratio = .4315

Capability ratio = .66

Unacceptable

Measurement

System

Define how much discrepancy is defined by the process

Create clearer operational definitions

Modified Process Map to catch defects

Create Standard Operating Procedures

Expand improvements to other vendors

Compare analysis on identical samples (DOE)

Maintain lot identity

Standardize sampling and analysis

The “variation” Receiving found

FY08 high volume vendors

Chart4

BS261/191	BS261/191
BS261/192	BS261/192
BS261/193	BS261/193
BS261/194	BS261/194
BS261/195	BS261/195
BS261/196	BS261/196
BS261/197	BS261/197
BS261/198	BS261/198
BS261/199	BS261/199
BS261/200	BS261/200
BS261/201	BS261/201
BS261/202	BS261/202
BS261/203	BS261/203
BS261/204	BS261/204
BS261/205	BS261/205
BS261/206	BS261/206
BS261/207	BS261/207
BS261/208	BS261/208
BS261/209	BS261/209
BS261/210	BS261/210
BS261/211	BS261/211
BS261/212	BS261/212
BS261/213	BS261/213
BS261/214	BS261/214
BS261/215	BS261/215
BS261/216	BS261/216
BS261/217	BS261/217
BS261/218	BS261/218
BS261/219	BS261/219
BS261/220	BS261/220
BS261/221	BS261/221
BS261/222	BS261/222
BS261/223	BS261/223
BS261/224	BS261/224
BS261/225	BS261/225
BS261/226	BS261/226
BS261/227	BS261/227
BS261/228	BS261/228
BS261/229	BS261/229
BS261/230	BS261/230
BS261/231	BS261/231
BS261/232	BS261/232
BS261/233	BS261/233
BS261/234	BS261/234
BS261/235	BS261/235
BS261/236	BS261/236
BS261/237	BS261/237
BS261/238	BS261/238
BS261/239	BS261/239
BS261/240	BS261/240
BS261/241	BS261/241
BS261/242	BS261/242
BS261/243	BS261/243
BS261/244	BS261/244
BS261/245	BS261/245
BS261/246	BS261/246
BS261/247	BS261/247
BS261/248	BS261/248
BS261/249	BS261/249
BS261/250	BS261/250
BS261/251	BS261/251

Beralt

OSI

Beralt - OSI Ore Comparison

0.7215

0.733

0.7289

0.741

0.7298

0.735

0.731

0.715

0.7318

0.72

0.7312

0.711

0.7295

0.726

0.731

0.734

0.7305

0.733

0.728

0.731

0.728

0.73

0.712

0.7295

0.719

0.73

0.727

0.7315

0.726

0.7285

0.729

0.7318

0.733

0.731

0.727

0.7312

0.714

0.7292

0.725

0.7272

0.727

0.7278

0.729

0.7269

0.735

0.728

0.734

0.7278

0.728

0.727

0.73

0.732

0.734

0.7312

0.734

0.7325

0.731

0.7318

0.733

0.7328

0.741

0.7322

0.737

0.7335

0.736

0.735

0.728

0.7356

0.733

0.7342

0.727

0.7345

0.738

0.7338

0.729

0.7348

0.737

0.7365

0.734

0.739

0.736

0.7382

0.722

0.7375

0.732

0.7362

0.725

0.7358

0.734

0.736

0.741

0.7348

0.734

0.733

0.734

0.7335

0.73

0.7322

0.731

0.7328

0.74

0.7325

0.738

0.7329

0.731

0.7261

0.739

0.7315

0.74

0.73

0.736

0.735

0.739

0.731

0.741

0.7312

0.738

0.732

Suppliers FY08

Supplier	STUs	Cumulative %	%
Beralt	121.9	23.11%	23.11%
DLA	111.2	44.20%	21.08%	65.21%
Spot	80	59.37%	15.17%
Cantung	72.3	73.08%	13.71%
Dynacor	71.4	86.61%	13.54%
Heemskirk	46.8	95.49%	8.87%
KMT	16.8	98.67%	3.19%
Other	7	100.00%	1.33%
	527.4
	Pareto

Suppliers FY08

0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0

Page &P

STUs

Cumulative %

STUs

Tungsten Ore Suppliers FY08

Dynacor


lot	Dynacor Analysis	OSI Analysis	Difference	Population		Sample of last 20 lots
1	73.50%	74.50%	1.00%
2	75.36%	75.10%	-0.26%
3	75.78%	75.20%	-0.58%	Dynacor
4	75.68%	75.20%	-0.48%
5	75.23%	75.20%	-0.03%	Mean	0.7531547619
6	75.15%	75.60%	0.45%	Standard Error	0.0005393807
7	75.39%	75.70%	0.31%	Median	0.7535
8	74.69%	75.50%	0.81%	Mode	0.7549
9	75.49%	75.60%	0.11%	Standard Deviation	0.0034955862
10	75.71%	75.60%	-0.11%	Sample Variance	0.0000122191
11	75.40%	75.30%	-0.10%	Kurtosis	17.8674511313
12	75.27%	75.60%	0.33%	Skewness	-3.6031269153
13	75.34%	75.60%	0.26%	Range	0.0228
14	75.34%	75.60%	0.26%	Minimum	0.735
15	74.96%	75.10%	0.14%	Maximum	0.7578
16	75.01%	75.40%	0.39%	Sum	31.6325
17	75.28%	75.50%	0.22%	Count	42
18	75.49%	75.40%	-0.09%
19	75.31%	75.30%	-0.01%
20	75.30%	75.60%	0.30%
21	75.40%	75.00%	-0.40%	OSI
22	75.08%	75.20%	0.12%
23	75.28%	75.50%	0.22%	Mean	0.7530238095
24	75.27%	75.50%	0.23%	Standard Error	0.000464148
25	75.49%	75.60%	0.11%	Median	0.7535
26	75.46%	75.70%	0.24%	Mode	0.756
27	75.20%	75.10%	-0.10%	Standard Deviation	0.0030080226
28	75.28%	75.30%	0.02%	Sample Variance	0.0000090482
29	75.29%	75.50%	0.21%	Kurtosis	-0.1311559736
30	75.48%	74.80%	-0.68%	Skewness	-0.7805090956
31	75.35%	74.90%	-0.45%	Range	0.012
32	75.31%	75.60%	0.29%	Minimum	0.745
33	75.52%	75.50%	-0.02%	Maximum	0.757
34	75.57%	74.80%	-0.77%	Sum	31.627
35	75.43%	75.50%	0.07%	Count	42
36	75.33%	74.90%	-0.43%
37	75.35%	74.70%	-0.65%
38	75.41%	75.30%	-0.11%
39	75.49%	74.90%	-0.59%
40	75.39%	75.30%	-0.09%
41	75.58%	75.40%	-0.18%
42	75.61%	75.10%	-0.51%
43			-0.01%
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105

Dynacor

0.735	0.745
0.7536	0.751
0.7578	0.752
0.7568	0.752
0.7523	0.752
0.7515	0.756
0.7539	0.757
0.7469	0.755
0.7549	0.756
0.7571	0.756
0.754	0.753
0.7527	0.756
0.7534	0.756
0.7534	0.756
0.7496	0.751
0.7501	0.754
0.7528	0.755
0.7549	0.754
0.7531	0.753
0.753	0.756
0.754	0.75
0.7508	0.752
0.7528	0.755
0.7527	0.755
0.7549	0.756
0.7546	0.757
0.752	0.751
0.7528	0.753
0.7529	0.755
0.7548	0.748
0.7535	0.749
0.7531	0.756
0.7552	0.755
0.7557	0.748
0.7543	0.755
0.7533	0.749
0.7535	0.747
0.7541	0.753
0.7549	0.749
0.7539	0.753
0.7558	0.754
0.7561	0.751

Dynacor Analysis

OSI Analysis

W Content by lot

Beralt

Lot	Beralt	OSI	Difference	Rec'd date
BS261/191	72.15%	73.30%	1.15%	9-Feb
BS261/192	72.89%	74.10%	1.21%	9-Feb
BS261/193	72.98%	73.50%	0.52%	16-Feb
BS261/194	73.10%	71.50%	-1.60%	27-Feb
BS261/195	73.18%	72.00%	-1.18%	27-Feb
BS261/196	73.12%	71.10%	-2.02%	2-Mar
BS261/197	72.95%	72.60%	-0.35%	16-Mar
BS261/198	73.10%	73.40%	0.30%	16-Mar
BS261/199	73.05%	73.30%	0.25%	15-Mar
BS261/200	72.80%	73.10%	0.30%	23-Mar
BS261/201	73.10%	72.80%	-0.30%	27-Mar
BS261/202	73.00%	71.20%	-1.80%	30-Mar
BS261/203	72.95%	71.90%	-1.05%	2-Apr
BS261/204	73.00%	72.70%	-0.30%	2-Apr
BS261/205	73.15%	72.60%	-0.55%	13-Apr
BS261/206	72.85%	72.90%	0.05%	20-Apr
BS261/207	73.18%	73.30%	0.12%	19-Apr
BS261/208	73.10%	72.70%	-0.40%	2-May
BS261/209	73.12%	71.40%	-1.72%	17-May
BS261/210	72.92%	72.50%	-0.42%	10-May
BS261/211	72.72%	72.70%	-0.02%	10-May
BS261/212	72.78%	72.90%	0.12%	16-May
BS261/213	72.69%	73.50%	0.81%	21-May
BS261/214	72.80%	73.40%	0.60%	21-May
BS261/215	72.78%	72.80%	0.02%	29-May
BS261/216	72.70%	73.00%	0.30%	7-Jun	lots that were sent to run twice in random order:
BS261/217	73.20%	73.40%	0.20%	12-Jun
BS261/218	73.12%	73.40%	0.28%	13-Jun
BS261/219	73.25%	73.10%	-0.15%	19-Jun
BS261/220	73.18%	73.30%	0.12%	19-Jun
BS261/221	73.28%	74.10%	0.82%	27-Jun
BS261/222	73.22%	73.70%	0.48%	28-Jun
BS261/223	73.35%	73.60%	0.25%	9-Jul
BS261/224	73.50%	72.80%	-0.70%	9-Jul
BS261/225	73.56%	73.30%	-0.26%	23-Jul
BS261/226	73.42%	72.70%	-0.72%	23-Jul
BS261/227	73.45%	73.80%	0.35%	26-Jul
BS261/228	73.38%	72.90%	-0.48%	2-Aug
BS261/229	73.48%	73.70%	0.22%	2-Aug
BS261/230	73.65%	73.40%	-0.25%	14-Aug
BS261/231	73.90%	73.60%	-0.30%	15-Aug
BS261/232	73.82%	72.20%	-1.62%	16-Aug
BS261/233	73.75%	73.20%	-0.55%	16-Aug
BS261/234	73.62%	72.50%	-1.12%	22-Aug
BS261/235	73.58%	73.40%	-0.18%	22-Aug
BS261/236	73.60%	74.10%	0.50%	24-Sep
BS261/237	73.48%	73.40%	-0.08%	3-Oct
BS261/238	73.40%	73.40%	0.00%	2-Oct
BS261/239	73.30%	73.40%	0.10%	15-Oct
BS261/240	73.35%	73.00%	-0.35%	15-Oct
BS261/241	73.22%	73.10%	-0.12%	17-Oct
BS261/242	73.28%	74.00%	0.72%	23-Oct
BS261/243	73.25%	73.80%	0.55%	22-Oct
BS261/244	73.29%	73.10%	-0.19%	7-Nov
BS261/245	72.61%	73.90%	1.29%	19-Nov
BS261/246	73.15%	74.00%	0.85%	19-Nov
BS261/247	73.00%	73.60%	0.60%	20-Nov
BS261/248	73.50%	73.90%	0.40%	20-Nov
BS261/249	73.10%	74.10%	1.00%	3-Dec
BS261/250	73.12%	73.80%	0.68%	4-Dec
BS261/251	73.20%	73.20%	0.00%	30-Nov
			-0.06%

Beralt

0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0

Beralt

OSI

Beralt - OSI Ore Comparison

Process Map

Vendor notifies Towanda of shipment and ETA Vendor provides tungsten content

Ore is weighed sampled and analyzed

Container arrives in Towanda

Ore is not removed from quality hold without vendor approval Ore is transferred to the dept for consumption

Vendor invoices Towanda based on their weight and analysis for tungsten content

Invoice entered into SAP for payment

Vendor invoice is reconciled to Towanda weight and tungsten content

Vendor issues credit or debit memo

Credit or debit memo is entered into SAP

Stratification


	Data Stratification Tree
Questions About Process		Stratification factors		Measurements
		X Variables
Can the reconciliation process be eliminated?
How can discrepancies be tracked?		Supplier W content
Is the supplier performing the same analysis to determine tungsten content?		Osram W content	* Discrepancy between W content from supplier,
How much of a discrepancy would constitute a third party analysis?	Payment based on W content	Third Party W content	Osram and third party by lot.
Can the tungsten concentrate be held if a discrepancy evolves?	(output y1)	Invoice
Would going to strictly third party analysis resolve the pain in the process?		Credit/Debit Memo
Could a pre-sample be air shipped to shorten the lead time in determining a discrepancy?		Purchase Order
Which analysis is more advantageous to use for future contracts?

DEFINE

MEASURE

Process Improvement Project – Graphing Time Reduction

Mike – MBC 638

Team Launch

5/11/08

Define

5/19/08

Measure

5/26/08

Analyze

6/6/08

Control

On-Going

Improve

7/4/08

Key Dates --->

ANALYZE

IMPROVE

BUSINESS CASE: $18,943 Annual Cost Reduction if Implemented in Engineering Department

Extensive graphing is required for good data analysis of lab qualification testing

350 Engineer hours in the department are spent on repetitive graphing procedures within Excel. This equates to $52,471/year

A 30% reduction in graphing time could result in a $15,741 annual savings.

Sigma Quality Level

Increased from 1.72

to 3.26

$18,943 annual cost reduction!

Eliminated wasteful, repetitive steps that can be automated with Excel Macros

Pareto showed that 80% of graphs consisted of 8 variables or less

95% confidence interval that true graphing time is 57 +/- 5.16 seconds

Hypothesis test indicates 88% confidence that new process meets the goal of at least a 30% improvement in graphing time

Identified Critical Inputs and Outputs to Measure

Measurement Systems Analysis using X-bar, R Charts and Precision-To-Total Ratio

R Charts in control.

Xbar Charts show ability to

measure differences

PTR = 0.22

Control

Maintain Revision Control on original spreadsheet macros

Provide to Engineering Department

Survey engineers for usage in 3 months

Chart1

1	1	1	1
2	2	2	2
3	3	3	3
4	4	4	4
5	5	5	5

xbar

Xbar2

UCL

LCL

Run #

Xbar

Day 1/Operator 1 - Xbar Chart

59.1

66.244

51.956

59.5

59.1

66.244

51.956

59.1

66.244

51.956

46.5

59.1

66.244

51.956

61.5

59.1

66.244

51.956

DataStratification

Data Stratification Tree
Questions about Process	Output	Stratification Factors	Measurements
		X		Output
Is y affected by the number of data points recorded in a test?		X1 = Total Data points collected	Number of data points collected
Is y affected by the total number of columns graphed?	Y = Graphing Time = f (X)		Number of different variables measured
Is y affected by the person creating the graphs?			Name of engineer
Is y affected by the quality of the graph required?			Title, labels, presentation ready
Is y affected by additional graphs required for comparison?			Number of graphs
Is y affected by the total number of columns of data available?			Total number of available variables

Costs


Cost Reduction
	Current	Improved
Tests/month/engineer	60	60
Tests/year/engineer	11100	11100
Engineers	4	4
Total tests/year	44400	44400
$/hour	150	150
Graphs/test	1	1
Graphs/year	22200	22200
Time per graph (sec)	57	36
Time spent graphing/year (hr)	350	224
Annual Cost Reduction	$ 52,471	$ 33,529
% Percent Annual Cost Reduction	36%
	$ 15,741.41

Initial Data Collection

Run	Number of Data Points Collected	Total Number of Different Variables Included in Graphs	Total number of available variables	Name of Operator	Title, Labels, Presentation	Number of Graphs	Time (sec)	Time per Graph (sec)			Number of data points collected
1	308	4	129	MB	Yes	1	51	51	Column1		Number of different variables measured
2	31	4	160	MB	Yes	1	58	58.0			Name of engineer
3	820	8	160	MB	Yes	2	158	79.0	Mean	56.7258064516	Title, labels, presentation ready
4	31	1	155	MB	Yes	1	43	43.0	Standard Error	2.6336276364	Number of graphs
5	820	7	160	MB	Yes	1	95	95.0	Median	53	Total number of available variables
6	760	7	160	MB	Yes	1	83	83.0	Mode	51
7	61	24	155	MB	Yes	1	53	53.0	Standard Deviation	14.663418099
8	362	4	162	MB	Yes	1	56	56.0	Sample Variance	215.0158303465
9	362	6	162	MB	Yes	2	109	54.5	Kurtosis	0.5911961314
10	482	16	131	MB	Yes	2	140	70.0	Skewness	1.0232211831
11	308	8	129	MB	Yes	3	174	58.0	Range	57
12	743	3	160	MB	Yes	1	70	70.0	Minimum	38
13	30	2	164	MB	Yes	1	54	54.0	Maximum	95
14	30	29	164	MB	Yes	1	69	69.0	Sum	1758.5
15	30	8	125	MB	Yes	1	43	43.0	Count	31
16	31	6	159	MB	Yes	3	119	39.7	Confidence Level(95.0%)	5.3785796423
17	30	3	164	MB	Yes	1	52	52.0	n=	31
18	30	4	168	MB	Yes	1	67	67.0	x bar (sec) =	56.7		39.7080645161
19	173	6	156	MB	Yes	2	118	59.0	s =	14.7
20	30	4	168	MB	Yes	1	44	44.0	1- alpha =	0.95
21	31	6	129	MB	Yes	2	112	56.0	alpha =	0.05
22	143	2	30	MB	Yes	1	48	48.0	alpha/2 =	0.025
23	31	12	123	MB	Yes	2	105	52.5
24	31	13	123	MB	Yes	2	79	39.5	U=	61.89
25	362	8	162	MB	Yes	2	87	43.5	L=	51.56
26	30	8	168	MB	Yes	1	51	51.0	95% Confidence Interval for the true average graphing time
27	451	4	218	MB	Yes	1	88	88.0	51.56 <= Population Mean <= 61.89
28	121	6	218	MB	Yes	3	156	52.0	57 +/- 5.16 seconds
29	31	6	218	MB	Yes	2	105	52.5
30	175	4	218	MB	Yes	2	76	38.0
31	141	13	218	MB	Yes	3	118	39.3	Find SQL
									SQL Baseline
									Population Mean =	56.7
									Std Deviation =	14.7
The sample size I chose for my initial baseline estimate of the population statistics was based on time constraints and the Central Limit Theorem. For almost all populations, the sampling distribution of the mean can be approximated closely by a normal d									X2 (Upper Spec Limit)	60
									Z2 =	0.223
									P(X>60) =	0.412
									P(X is out of spec) =	0.412
									DPM =	411655
									SQL =	1.72
Number of Variables	1 to 4	5 to 7	8 to 10	11 to 13	>13
Total	12.0	8.0	5.0	3.0	3.0
%	38.7%	25.8%	16.1%	9.7%	9.7%
Cumul Freq	38.7%	64.5%	80.6%	90.3%	100.0%

Initial Data Collection

0	0
0	0
0	0
0	0
0	0

cumulative Frequency

Number Variables

Cumulative Frequency

Pareto Diagram of Number of Variables Graphed

Process Improved Data

			Run	Number of Data Points Collected	Total Number of Different Variables Included in Graphs	Total number of available variables	Name of Operator	Title, Labels, Presentation	Number of Graphs	Time (sec)	Time per Graph (sec)
Determine Sample Size That would be required			1	308	4	129	MB	Yes	1	33	33
			2	31	4	160	MB	Yes	1	25	25.0
Before process improvement			3	820	8	160	MB	Yes	2	54	27.0
U / L =	86.48 +/- 13.05		4	31	1	155	MB	Yes	1	14	14.0
After process Improvement			5	820	7	160	MB	Yes	1	54	54.0
Desire 95% confidence interval that produces an interval half width of only 5 seconds			6	760	7	160	MB	Yes	1	53	53.0
			7*	61	24	155	MB	Yes	1	53	53.0
Z =	95%		8	362	4	162	MB	Yes	1	29	29.0
1- alpha =	0.95		9	362	6	162	MB	Yes	2	62	31.0
1- alpha/2 =	0.975		10*	482	16	131	MB	Yes	2	140	70.0
Confidence = Z(.975) =	1.96		11	308	8	129	MB	Yes	3	79	26.3
Est Pop Std Deviation =	13.48		12	743	3	160	MB	Yes	1	35	35.0
n =	28		13	30	2	164	MB	Yes	1	25	25.0
			14*	30	29	164	MB	Yes	1	69	69.0
			15	30	8	125	MB	Yes	1	40	40.0
Column1			16	31	6	159	MB	Yes	3	71	23.7
			17	30	3	164	MB	Yes	1	34	34.0
Mean	36.247311828		18	30	4	168	MB	Yes	1	45	45.0
Standard Error	2.4214650296		19	173	6	156	MB	Yes	2	56	28.0
Median	33		20	30	4	168	MB	Yes	1	36	36.0
Mode	25		21	31	6	129	MB	Yes	2	59	29.5
Standard Deviation	13.4821466974		22	143	2	30	MB	Yes	1	22	22.0
Sample Variance	181.7682795699		23*	31	12	123	MB	Yes	2	105	52.5
Kurtosis	0.6312076295		24*	31	13	123	MB	Yes	2	79	39.5
Skewness	1.003498986		25	362	8	162	MB	Yes	2	69	34.5
Range	56		26	30	8	168	MB	Yes	1	47	47.0
Minimum	14		27	451	4	218	MB	Yes	1	29	29.0
Maximum	70		28	121	6	218	MB	Yes	3	76	25.3
Sum	1123.6666666667		29	31	6	218	MB	Yes	2	61	30.5
Count	31		30	175	4	218	MB	Yes	2	47	23.5
Confidence Level(95.0%)	4.9452862403		31*	141	13	218	MB	Yes	3	118	39.3
n=	31		* Graphs calling for # variables > 8 used previous recorded time. Process fix works on up to 8 variables
x bar =	36.2
s =	13.5
1- alpha =	0.95
alpha =	0.05
alpha/2 =	0.025
U=	40.99
L=	31.50
95% Confidence Interval for the true average graphing time
31.5 <= Population Mean <= 40.99
36 +/- 4.7 sec
		`
SQL Improved Process
Population Mean =	36.2
Std Deviation =	13.5
X2 (Upper Spec Limit)	60
Z2 =	1.762
P(X>60) =	0.039
P(X is out of spec) =	0.039
DPM =	39052
SQL =	3.26

Hypothesis Testing


The goal of the project is to reduce the time it takes to create graphs by 30%. This means we want to reduce the estimated population mean from 57 seconds to 40 seconds. This forms the bases of a One-Sided, One-Sample Hypothesis test of the mean.
H0: mu >=	40
H1: mu <	40
Acceptable Level of Risk=	10%
alpha =	0.10
n =	31
New Process Mean =	36.2
New Process Variance =	181.8
Zo =	-1.549759392
P = 2* Z()	0.12	0.2423986059
Confidence =	87.9%
Since [P =b24] < [alpha = .10] reject Ho and conclude H1) with (1-b24)*100% confidence. We can be X % confident that the new process has reduced the time it takes to create graphs by 50%.

Measurement System Analysis

1. Establish operational Definitions
An operational definition is a clear concise, unambiguous definition of what time start and stop means.
Start - Time starts when the operator clicks on the spreadsheet to open it.
Stop - Time stops when the closes the file indicating that all graphs are complete.
Completed Graph: A Completed graph includes a title and labeled x and y axis.
2. Kappa technique to assess Measurement system within the operator
Choose 10 different graphs. Do each graph twice.
Use Kappa technique to assess measurement system capability											Column1		Day 1
	Day 1				Day 2				# graphs	variables				Xbar line	UCL	LCL	R bar line	R- UCL
Run #	M1	M2	xbar	R	M1	M2	xbar	R			Mean	57.1	1	59.1	66.2	52.0	3.8	12.4
1	36.0	34.0	35.0	2.0	34.0	33.0	33.5	1.0	1	2	Standard Error	4.6111421122	2	59.1	66.2	52.0	3.8	12.4
2	59.0	60.0	59.5	1.0	56.0	58.0	57.0	2.0	2	4	Median	58	3	59.1	66.2	52.0	3.8	12.4
3	96.0	90.0	93.0	6.0	87.0	93.0	90.0	6.0	3	8	Mode	58	4	59.1	66.2	52.0	3.8	12.4
4	48.0	45.0	46.5	3.0	37.0	35.0	36.0	2.0	1	4	Standard Deviation	20.6216544336	5	59.1	66.2	52.0	3.8	12.4
5	58.0	65.0	61.5	7.0	60.0	58.0	59.0	2.0	2	6	Sample Variance	425.2526315789
		Xbar2 =	59.1			Xbar2 =	55.1				Kurtosis	-0.5588580019
		Rbar =	3.8			Rbar =	2.6				Skewness	0.657122154	Day 2
											Range	63		Xbar line	UCL	LCL	R bar line	R -UCL
Xbar UCL	66.2				Xbar UCL	60.0					Minimum	33	1	55.1	60.0	50.2	2.6	8.5
Xbar LCL	52.0				Xbar LCL	50.2					Maximum		2	55.1	60.0	50.2	2.6	8.5
R UCL	12.426				R UCL	8.502					Sum	1142	3	55.1	60.0	50.2	2.6	8.5
R LCL	0				R LCL	0					Count	20	4	55.1	60.0	50.2	2.6	8.5
											Confidence Level(95.0%)	9.6512343576	5	55.1	60.0	50.2	2.6	8.5
Average of Average Ranges =		3.20	0.22 > 0.10, measurement system not capable? Need to explore why.
Repeatability Std Deviation=		2.83
Reproducibility Std Deviation=		3.54
Measurement Variance=		20.55
Measurement Std Dev =		4.53
Total Variance (All 20 Meas.)=		425.25
Total Standard Deviation =		20.62
Precision to Total Ratio = Measurement Std Dev / Total Std Dev =					0.22
A Rule of thumb used to determine if the measurement system is capable is to see if the precision to total ratio is less than 10%. In this case, 0.27 is greater than .10, so the measurement system is a little out of control. Sigma reproducibility is the
R charts are in control both days, but on Day 1 I exhibited more repeatability variability as evidenced by an average range of 5.2 vs a range of 2.8 on Day 2. Possible reasons might be that on day 2 I had made the graphs before and was more familiar wher

Measurement System Analysis

xbar

Xbar2

UCL

LCL

Run #

Xbar

Day 1/Operator 1 - Xbar Chart

Rbar

UCL

Run #

Day 1/Operator 1 - R Chart

xbar

Xbar2

UCL

LCL

Run #

Xbar

Day 2/Operator2 - Xbar Chart

R bar

R- UCL

Run #

Day 2/Operator 2 - R Chart

UCL=66.2

LCL=52.0

xbar2=59.1

UCL=60.0

LCL=50.2

xbar2=55.1

UCL=12.4

Rbar=3.8

UCL=8.5

Rbar=2.6

Data Stratification Tree

Questions About Process

Stratification factors

X Variables

Measurements

Handicap Index

Ball (Titleist, Nike)

Putter (Titleist, Callaway)

Tempo (Normal, Slow)

(ULTIMATE

Output Y

)

Does Tempo impact my performance?

Does equipment impact my performance on

putting green and consequently my USGA

handicap index?

Stance (Open like Jack N., Normal)

FiskarRuler

Does my stance affect the putting result?

•# of feet to hole

What % of my putts are within 2 feet?

Does music (sounds) impact

performance on the putting green?

Does the distance from the hole matter?

Will randomization show different results?

Is there variability in the measurement?

What is the average distance from the hole from

10, 20 and 40 feet?

Measuring Tape

•# of inches from hole after putt

•Measure variation with music, tempo,

stance from each distance

•% of putts within 2 feet

•repeatability

•reproducibility

•average distance from 10 feet

•Average distance from 20 feet

•Average distance from 40 feet

Putter and ball combination?

Music (On, None)

•Average change in inches further from hole

Is there a financial impact on the result?

Handicap Index as Measure

•Compare USGA index before and after

Inches from Hole

(Output Y

)

Percentage

Within 2 feet

(from 20 feet

And further)

Percentage

Of putts made

(from 10 feet and

Closer)

(Output Y

)

(Output Y

)

HOW DO I IMPROVE MY GOLF GAME

AND LOWER MY HANDICAP?

Questions about ProcessOutputStratification FactorsMeasurements

Is y affected by the number of

data points recorded in a test?

X1 = Total Data points collectedNumber of data points collected

Is y affected by the total number

of columns graphed?

= Variables included in graphNumber of different variables measured

Is y affected by the person

creating the graphs?

= OperatorName of engineer

Is y affected by the quality of the

graph required?

= Graph qualityTitle, labels, presentation ready

Is y affected by additional graphs

required for comparison?

= Number of graphsNumber of graphs

Is y affected by the total number

of columns of data available?

= Total number of available variablesTotal number of available variables

Data Stratification Tree

Y = Graphing Time = f (X)

Y = f(X

)

Questions about ProcessOutputStratification FactorsMeasurements

Is y affected by the number of

data points recorded in a test?

X1 = Total Data points

collected

Number of data points

collected

Is y affected by the total number

of columns graphed?

= Variables included in

graph

Number of different

variables measured

Is y affected by the person

creating the graphs?

= OperatorName of engineer

Is y affected by the quality of the

graph required?

= Graph quality

Title, labels,

presentation ready

Is y affected by additional graphs

required for comparison?

= Number of graphsNumber of graphs

Is y affected by the total number

of columns of data available?

= Total number of

available variables

Total number of

available variables

Data Stratification Tree

Y =

Graphing

Time =

f (X)

Y = f(X

)

Chart2

1	1	1
2	2	2
3	3	3
4	4	4
5	5	5

Rbar

UCL

Run #

Day 1/Operator 1 - R Chart

3.8

12.426

3.8

12.426

3.8

12.426

3.8

12.426

3.8

12.426

DataStratification

Data Stratification Tree
Questions about Process	Output	Stratification Factors	Measurements
		X		Output
Is y affected by the number of data points recorded in a test?		X1 = Total Data points collected	Number of data points collected
Is y affected by the total number of columns graphed?	Y = Graphing Time = f (X)		Number of different variables measured
Is y affected by the person creating the graphs?			Name of engineer
Is y affected by the quality of the graph required?			Title, labels, presentation ready
Is y affected by additional graphs required for comparison?			Number of graphs
Is y affected by the total number of columns of data available?			Total number of available variables

Costs


Cost Reduction
	Current	Improved
Tests/month/engineer	60	60
Tests/year/engineer	11100	11100
Engineers	4	4
Total tests/year	44400	44400
$/hour	150	150
Graphs/test	1	1
Graphs/year	22200	22200
Time per graph (sec)	57	36
Time spent graphing/year (hr)	350	224
Annual Cost Reduction	$ 52,471	$ 33,529
% Percent Annual Cost Reduction	36%
	$ 15,741.41

Initial Data Collection

Run	Number of Data Points Collected	Total Number of Different Variables Included in Graphs	Total number of available variables	Name of Operator	Title, Labels, Presentation	Number of Graphs	Time (sec)	Time per Graph (sec)			Number of data points collected
1	308	4	129	MB	Yes	1	51	51	Column1		Number of different variables measured
2	31	4	160	MB	Yes	1	58	58.0			Name of engineer
3	820	8	160	MB	Yes	2	158	79.0	Mean	56.7258064516	Title, labels, presentation ready
4	31	1	155	MB	Yes	1	43	43.0	Standard Error	2.6336276364	Number of graphs
5	820	7	160	MB	Yes	1	95	95.0	Median	53	Total number of available variables
6	760	7	160	MB	Yes	1	83	83.0	Mode	51
7	61	24	155	MB	Yes	1	53	53.0	Standard Deviation	14.663418099
8	362	4	162	MB	Yes	1	56	56.0	Sample Variance	215.0158303465
9	362	6	162	MB	Yes	2	109	54.5	Kurtosis	0.5911961314
10	482	16	131	MB	Yes	2	140	70.0	Skewness	1.0232211831
11	308	8	129	MB	Yes	3	174	58.0	Range	57
12	743	3	160	MB	Yes	1	70	70.0	Minimum	38
13	30	2	164	MB	Yes	1	54	54.0	Maximum	95
14	30	29	164	MB	Yes	1	69	69.0	Sum	1758.5
15	30	8	125	MB	Yes	1	43	43.0	Count	31
16	31	6	159	MB	Yes	3	119	39.7	Confidence Level(95.0%)	5.3785796423
17	30	3	164	MB	Yes	1	52	52.0	n=	31
18	30	4	168	MB	Yes	1	67	67.0	x bar (sec) =	56.7		39.7080645161
19	173	6	156	MB	Yes	2	118	59.0	s =	14.7
20	30	4	168	MB	Yes	1	44	44.0	1- alpha =	0.95
21	31	6	129	MB	Yes	2	112	56.0	alpha =	0.05
22	143	2	30	MB	Yes	1	48	48.0	alpha/2 =	0.025
23	31	12	123	MB	Yes	2	105	52.5
24	31	13	123	MB	Yes	2	79	39.5	U=	61.89
25	362	8	162	MB	Yes	2	87	43.5	L=	51.56
26	30	8	168	MB	Yes	1	51	51.0	95% Confidence Interval for the true average graphing time
27	451	4	218	MB	Yes	1	88	88.0	51.56 <= Population Mean <= 61.89
28	121	6	218	MB	Yes	3	156	52.0	57 +/- 5.16 seconds
29	31	6	218	MB	Yes	2	105	52.5
30	175	4	218	MB	Yes	2	76	38.0
31	141	13	218	MB	Yes	3	118	39.3	Find SQL
									SQL Baseline
									Population Mean =	56.7
									Std Deviation =	14.7
The sample size I chose for my initial baseline estimate of the population statistics was based on time constraints and the Central Limit Theorem. For almost all populations, the sampling distribution of the mean can be approximated closely by a normal d									X2 (Upper Spec Limit)	60
									Z2 =	0.223
									P(X>60) =	0.412
									P(X is out of spec) =	0.412
									DPM =	411655
									SQL =	1.72
Number of Variables	1 to 4	5 to 7	8 to 10	11 to 13	>13
Total	12.0	8.0	5.0	3.0	3.0
%	38.7%	25.8%	16.1%	9.7%	9.7%
Cumul Freq	38.7%	64.5%	80.6%	90.3%	100.0%

Initial Data Collection

0	0
0	0
0	0
0	0
0	0

cumulative Frequency

Number Variables

Cumulative Frequency

Pareto Diagram of Number of Variables Graphed

Process Improved Data

			Run	Number of Data Points Collected	Total Number of Different Variables Included in Graphs	Total number of available variables	Name of Operator	Title, Labels, Presentation	Number of Graphs	Time (sec)	Time per Graph (sec)
Determine Sample Size That would be required			1	308	4	129	MB	Yes	1	33	33
			2	31	4	160	MB	Yes	1	25	25.0
Before process improvement			3	820	8	160	MB	Yes	2	54	27.0
U / L =	86.48 +/- 13.05		4	31	1	155	MB	Yes	1	14	14.0
After process Improvement			5	820	7	160	MB	Yes	1	54	54.0
Desire 95% confidence interval that produces an interval half width of only 5 seconds			6	760	7	160	MB	Yes	1	53	53.0
			7*	61	24	155	MB	Yes	1	53	53.0
Z =	95%		8	362	4	162	MB	Yes	1	29	29.0
1- alpha =	0.95		9	362	6	162	MB	Yes	2	62	31.0
1- alpha/2 =	0.975		10*	482	16	131	MB	Yes	2	140	70.0
Confidence = Z(.975) =	1.96		11	308	8	129	MB	Yes	3	79	26.3
Est Pop Std Deviation =	13.48		12	743	3	160	MB	Yes	1	35	35.0
n =	28		13	30	2	164	MB	Yes	1	25	25.0
			14*	30	29	164	MB	Yes	1	69	69.0
			15	30	8	125	MB	Yes	1	40	40.0
Column1			16	31	6	159	MB	Yes	3	71	23.7
			17	30	3	164	MB	Yes	1	34	34.0
Mean	36.247311828		18	30	4	168	MB	Yes	1	45	45.0
Standard Error	2.4214650296		19	173	6	156	MB	Yes	2	56	28.0
Median	33		20	30	4	168	MB	Yes	1	36	36.0
Mode	25		21	31	6	129	MB	Yes	2	59	29.5
Standard Deviation	13.4821466974		22	143	2	30	MB	Yes	1	22	22.0
Sample Variance	181.7682795699		23*	31	12	123	MB	Yes	2	105	52.5
Kurtosis	0.6312076295		24*	31	13	123	MB	Yes	2	79	39.5
Skewness	1.003498986		25	362	8	162	MB	Yes	2	69	34.5
Range	56		26	30	8	168	MB	Yes	1	47	47.0
Minimum	14		27	451	4	218	MB	Yes	1	29	29.0
Maximum	70		28	121	6	218	MB	Yes	3	76	25.3
Sum	1123.6666666667		29	31	6	218	MB	Yes	2	61	30.5
Count	31		30	175	4	218	MB	Yes	2	47	23.5
Confidence Level(95.0%)	4.9452862403		31*	141	13	218	MB	Yes	3	118	39.3
n=	31		* Graphs calling for # variables > 8 used previous recorded time. Process fix works on up to 8 variables
x bar =	36.2
s =	13.5
1- alpha =	0.95
alpha =	0.05
alpha/2 =	0.025
U=	40.99
L=	31.50
95% Confidence Interval for the true average graphing time
31.5 <= Population Mean <= 40.99
36 +/- 4.7 sec
		`
SQL Improved Process
Population Mean =	36.2
Std Deviation =	13.5
X2 (Upper Spec Limit)	60
Z2 =	1.762
P(X>60) =	0.039
P(X is out of spec) =	0.039
DPM =	39052
SQL =	3.26

Hypothesis Testing


The goal of the project is to reduce the time it takes to create graphs by 30%. This means we want to reduce the estimated population mean from 57 seconds to 40 seconds. This forms the bases of a One-Sided, One-Sample Hypothesis test of the mean.
H0: mu >=	40
H1: mu <	40
Acceptable Level of Risk=	10%
alpha =	0.10
n =	31
New Process Mean =	36.2
New Process Variance =	181.8
Zo =	-1.549759392
P = 2* Z()	0.12	0.2423986059
Confidence =	87.9%
Since [P =b24] < [alpha = .10] reject Ho and conclude H1) with (1-b24)*100% confidence. We can be X % confident that the new process has reduced the time it takes to create graphs by 50%.

Measurement System Analysis

1. Establish operational Definitions
An operational definition is a clear concise, unambiguous definition of what time start and stop means.
Start - Time starts when the operator clicks on the spreadsheet to open it.
Stop - Time stops when the closes the file indicating that all graphs are complete.
Completed Graph: A Completed graph includes a title and labeled x and y axis.
2. Kappa technique to assess Measurement system within the operator
Choose 10 different graphs. Do each graph twice.
Use Kappa technique to assess measurement system capability											Column1		Day 1
	Day 1				Day 2				# graphs	variables				Xbar line	UCL	LCL	R bar line	R- UCL
Run #	M1	M2	xbar	R	M1	M2	xbar	R			Mean	57.1	1	59.1	66.2	52.0	3.8	12.4
1	36.0	34.0	35.0	2.0	34.0	33.0	33.5	1.0	1	2	Standard Error	4.6111421122	2	59.1	66.2	52.0	3.8	12.4
2	59.0	60.0	59.5	1.0	56.0	58.0	57.0	2.0	2	4	Median	58	3	59.1	66.2	52.0	3.8	12.4
3	96.0	90.0	93.0	6.0	87.0	93.0	90.0	6.0	3	8	Mode	58	4	59.1	66.2	52.0	3.8	12.4
4	48.0	45.0	46.5	3.0	37.0	35.0	36.0	2.0	1	4	Standard Deviation	20.6216544336	5	59.1	66.2	52.0	3.8	12.4
5	58.0	65.0	61.5	7.0	60.0	58.0	59.0	2.0	2	6	Sample Variance	425.2526315789
		Xbar2 =	59.1			Xbar2 =	55.1				Kurtosis	-0.5588580019
		Rbar =	3.8			Rbar =	2.6				Skewness	0.657122154	Day 2
											Range	63		Xbar line	UCL	LCL	R bar line	R -UCL
Xbar UCL	66.2				Xbar UCL	60.0					Minimum	33	1	55.1	60.0	50.2	2.6	8.5
Xbar LCL	52.0				Xbar LCL	50.2					Maximum		2	55.1	60.0	50.2	2.6	8.5
R UCL	12.426				R UCL	8.502					Sum	1142	3	55.1	60.0	50.2	2.6	8.5
R LCL	0				R LCL	0					Count	20	4	55.1	60.0	50.2	2.6	8.5
											Confidence Level(95.0%)	9.6512343576	5	55.1	60.0	50.2	2.6	8.5
Average of Average Ranges =		3.20	0.22 > 0.10, measurement system not capable? Need to explore why.
Repeatability Std Deviation=		2.83
Reproducibility Std Deviation=		3.54
Measurement Variance=		20.55
Measurement Std Dev =		4.53
Total Variance (All 20 Meas.)=		425.25
Total Standard Deviation =		20.62
Precision to Total Ratio = Measurement Std Dev / Total Std Dev =					0.22
A Rule of thumb used to determine if the measurement system is capable is to see if the precision to total ratio is less than 10%. In this case, 0.27 is greater than .10, so the measurement system is a little out of control. Sigma reproducibility is the
R charts are in control both days, but on Day 1 I exhibited more repeatability variability as evidenced by an average range of 5.2 vs a range of 2.8 on Day 2. Possible reasons might be that on day 2 I had made the graphs before and was more familiar wher

Measurement System Analysis

xbar

Xbar2

UCL

LCL

Run #

Xbar

Day 1/Operator 1 - Xbar Chart

Rbar

UCL

Run #

Day 1/Operator 1 - R Chart

xbar

Xbar2

UCL

LCL

Run #

Xbar

Day 2/Operator2 - Xbar Chart

R bar

R- UCL

Run #

Day 2/Operator 2 - R Chart

UCL=66.2

LCL=52.0

xbar2=59.1

UCL=60.0

LCL=50.2

xbar2=55.1

UCL=12.4

Rbar=3.8

UCL=8.5

Rbar=2.6

Data Stratification Tree

Questions About Process

Stratification factors

X Variables

Measurements

Handicap Index

Ball (Titleist, Nike)

Putter (Titleist, Callaway)

Tempo (Normal, Slow)

(ULTIMATE

Output Y

)

Does Tempo impact my performance?

Does equipment impact my performance on

putting green and consequently my USGA

handicap index?

Stance (Open like Jack N., Normal)

FiskarRuler

Does my stance affect the putting result?

•# of feet to hole

What % of my putts are within 2 feet?

Does music (sounds) impact

performance on the putting green?

Does the distance from the hole matter?

Will randomization show different results?

Is there variability in the measurement?

What is the average distance from the hole from

10, 20 and 40 feet?

Measuring Tape

•# of inches from hole after putt

•Measure variation with music, tempo,

stance from each distance

•% of putts within 2 feet

•repeatability

•reproducibility

•average distance from 10 feet

•Average distance from 20 feet

•Average distance from 40 feet

Putter and ball combination?

Music (On, None)

•Average change in inches further from hole

Is there a financial impact on the result?

Handicap Index as Measure

•Compare USGA index before and after

Inches from Hole

(Output Y

)

Percentage

Within 2 feet

(from 20 feet

And further)

Percentage

Of putts made

(from 10 feet and

Closer)

(Output Y

)

(Output Y

)

HOW DO I IMPROVE MY GOLF GAME

AND LOWER MY HANDICAP?

Questions about ProcessOutputStratification FactorsMeasurements

Is y affected by the number of

data points recorded in a test?

X1 = Total Data points collectedNumber of data points collected

Is y affected by the total number

of columns graphed?

= Variables included in graphNumber of different variables measured

Is y affected by the person

creating the graphs?

= OperatorName of engineer

Is y affected by the quality of the

graph required?

= Graph qualityTitle, labels, presentation ready

Is y affected by additional graphs

required for comparison?

= Number of graphsNumber of graphs

Is y affected by the total number

of columns of data available?

= Total number of available variablesTotal number of available variables

Data Stratification Tree

Y = Graphing Time = f (X)

Y = f(X

)

Questions about ProcessOutputStratification FactorsMeasurements

Is y affected by the number of

data points recorded in a test?

X1 = Total Data points

collected

Number of data points

collected

Is y affected by the total number

of columns graphed?

= Variables included in

graph

Number of different

variables measured

Is y affected by the person

creating the graphs?

= OperatorName of engineer

Is y affected by the quality of the

graph required?

= Graph quality

Title, labels,

presentation ready

Is y affected by additional graphs

required for comparison?

= Number of graphsNumber of graphs

Is y affected by the total number

of columns of data available?

= Total number of

available variables

Total number of

available variables

Data Stratification Tree

Y =

Graphing

Time =

f (X)

Y = f(X

)

DEFINE

MEASURE

Team Launch:

Define:

Measure:

Analyze:

Control:

Improve:

Key Dates

ANALYZE

IMPROVE

Process owner: Landon

CONTROL

September 10

September 17

September 24

October 29

November 19

Landon, Engineering Project Managers, Finance Office, Stakeholders

September 6

Problem Statement

Cycle time for a signature sheet averaged 11.25 days with each project manger spending about 11.25 hours for each purchase over $100,000.

Business Impact

The average wage a project manager earns is $35/hr, therefore it costs $393.75 per project in just gathering signatures! At 144 projects a year this process costs $56,700 annually.

Finance Office will not accept old version of signature sheet.
Purchases tracked in SharePoint
Appraisal rated on compliance

High man-hours

High cycle time

Too many steps!

Need to take PM out of all these steps

Cycle time reduced to 2.88 days!

Man-hours reduced to 0.20 hours!

Cost savings $55,692 annually!

SQL raised from 2.3 to 3.6 and rising!

SQL = 2.3

Hypothesis Test

Ho: mu ≥ 11.25 hr Ha: mu < 11.25 hr

P-value ≈ 0

Electronic Signature Sheet

Project manager signs form

Obtain engineering supervisor’s signature

Submit to Finance

Obtain production supervisor’s signature

Obtain engineering director’s signature

Obtain group director’s signature

Submit form to Finance Department

Wait for Thursday

Obtain finance supervisor’s signature

Is it Thursday?

Is project over $100,000?

DEFINE – 5/15/11

MEASURE - 6/1/11

Finding the Skinny on Thin Film Sensor Reject Rates

Control

Improve

ANALYZE - 7/1/11

IMPROVE - 8/1/11

1) Problem Statement:

Production reject rate of thin film sensors increases after process change.

3) Business Impact:

Reducing/eliminating frequency rejects will prevent reworking of part, extra inventory and labor from 100% testing which could potentially save

The r2 shows that the amount of raw material used from Vendor A explains 46.6 % of the change in reject rate.

8) Probable Cause 2 – Evaporation Fixture Geometry

7) Probable Cause 1 - Raw Material Supply

The sensors are held in a fixture positioned over a evaporation source that coats them with metal. I performed a test run to measure baseline performance. The data revealed that the metallic coating has too much variation in thickness w/ a mean of 2235 Å, but the range should be 500 Å. This could be caused by the position of the source, size of mask or angle of the holding fixture.

2) Work on largest category of defect for MAXIUM IMPACT

Before

After

4) Out-of-Control:

Process is highly variable to begin w/ but much worse after change.

DPMO of 31,934,Ouch!

5) Change of Focus

The change did cause an increase in variability, but the process is not very good to start w/ a DPMO of 19,263! Finding the root cause of the inherent process variability should solve the new issue.

6) Identify Primary Inputs (Y)

Separated wheat from chaff

A second run was done to test if a centered evaporation source would decrease thickness variability (Ha). A

one-tail test was performed & the P value was high,

thus it did not significantly improve the process. This points to the mask size & fixture as the root cause of

the variation.

Z = -1.19 P = 1-Z = 1-1.19 =0.86 =86%

Ho: Test 1 thickness variability ≤ Test 2

Receipt of material from Vendor A

was halted. A comparison of their measurements vs. ours found a 7.6 KHz difference!

They recalibrated their instruments & next shipment was markedly improved with a mean very close to the center of our specification range of 6.055 as shown on this histogram.

9) Solution to Probable Cause 1

10) Solution to Probable Cause 2

I’m working with engineering to

develop a new fixture that will

improve the geometry.

Scatter plot reveals that using raw materials from Vendor A has a strong positive correlation of 0.7 with the reject rate.

Constructing a control chart of measurements taken by QC of frequency illustrates that the vendors process is out of control.

10) Changes to be Made:

QC technician does acceptance testing of raw materials w/ zero tolerance.

Vendor supplies Certificate of Analysis w/ test statistics.

Control chart created for raw materials.

New fixture for more uniform thickness to prevent any frequency rejects.

Control – 8/8/11

Box Plot

Pareto Chart

C&E Matrix

Control Chart

Scatter Plot

Hypothesis Test

Histogram

Rick, Steve & Production Staff

Cause & Effect Matrix
Scoring:1=low, 3 = med, 5=high, Importance to Customer (sensors w/ correct frequency) = 1
Process Inputs (X)	Effect	Rating	Probability	Score
Vendor frequency sorting quality	Allow accurate calculation of thickness	5	High, makes adjustments when providing thickness data to techs	25
Fixture Geometry	Even coating thickness	5	High, location determines the thickness of the coating.	25

34

35

36

Invoice Cost Increase

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

$16,000

Jun-07Jul-07Aug-07

Invoice Cost

Invoice (X)

Mean

UNPL

LNPL

Page Views Increase

8,000,000

9,000,000

10,000,000

11,000,000

12,000,000

13,000,000

14,000,000

15,000,000

16,000,000

17,000,000

18,000,000

May-07Jun-07Jul-07

Pageviews

Mean

UNPL

LNPL

Invoice Cost After Improvement

$9,500

$9,700

$9,900

$10,100

$10,300

$10,500

$10,700

$10,900

$11,100

$11,300

$11,500

Aug-07

Sep-07

Oct-07

Number of Complete Applications

888

1111

1313

Jul, 2006

Sept

Nov

Jan

Mar

May

Jul, 2007

Date

Number

Series1

Linear (Series1)

Time to Complete Proccess Cycle

Jul-06AugSeptOct NovDecJanFebMarchAprilMayJuneJul-07

Date/Time/Period

Time (Days)

Data 1

Median

Goal

Total Application Errors by Type

132

75.0%

86.9%

94.9%

100

120

140

160

Incomplete/Incorrect ApplicationsStalled Approvals In The Log-in

Phase,

Stalled Approval At The

Managerial Level

Reworks

Type

Defects

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Base Group

62.06UCL

42.50CL

22.94LCL

16111621263136414651566166717681869196101106111116121126

Units

Cycle Time- Days

New "Improved" Process

UCL 44.59

CL 28.56

LCL 12.53

12345678910111213141516171819202122232425

Units

Cycle Time

Beralt - OSI Ore Comparison

69.50%

70.00%

70.50%

71.00%

71.50%

72.00%

72.50%

73.00%

73.50%

74.00%

74.50%

BS261/191

BS261/195

BS261/199

BS261/203

BS261/207

BS261/211

BS261/215

BS261/219

BS261/223

BS261/227

BS261/231

BS261/235

BS261/239

BS261/243

BS261/247

BS261/251

Beralt

OSI

Tungsten Ore Suppliers FY08

100

120

140

Beralt

DLA

Spot

CantungDynacor

Heemskirk

KMT

Other

STUs

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

STUs

Cumulative %

Questions about Process

Output

Stratification Factors

Measurements

Is y affected by the number of

data points recorded in a test?

X1 = Total Data points

collected

Number of data points

collected

Is y affected by the total number

of columns graphed?

= Variables included in

graph

Number of different

variables measured

Is y affected by the person

creating the graphs?

= Operator

Name of engineer

Is y affected by the quality of the

graph required?

= Graph quality

Title, labels,

presentation ready

Is y affected by additional graphs

required for comparison?

= Number of graphs

Number of graphs

Is y affected by the total number

of columns of data available?

= Total number of

available variables

Total number of

available variables

Data Stratification Tree

Y =

Graphing

Time =

f (X)

Y = f(X

)

Day 1/Operator 1 - Xbar Chart

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

0123456

Run #

Xbar

Day 1/Operator 1 - R Chart

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0123456

Run #

Pareto Diagram of Number of Variables Graphed

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 to 4

5 to 7

8 to 10

11 to 13

>13

Number Variables

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

Cumulative Frequency

Current

Improved

Time per graph (sec)

Time spent

graphing/year (hr)

350

224

Annual Cost Reduction

52,471

33,529

% Percent Annual Cost

Reduction

Cost Reduction

36%

Population Mean =

56.7

Std Deviation =

14.7

X2 (Upper Spec Limit)

Z2 =

0.223

P(X>60) =

0.412

P(X is out of spec) =

0.412

DPM =

411655

SQL =

1.72

SQL Baseline

Population Mean =

36.2

Std Deviation =

13.5

X2 (Upper Spec Limit)

Z2 =

1.762

P(X>60) =

0.039

P(X is out of spec) =

0.039

DPM =

39052

SQL =

3.26

SQL Improved Process

H0: mu >=

H1: mu <

Acceptable Level of Risk=

10%

alpha =

0.10

n =

New Process Mean =

36.2

New Process Variance =

181.8

Zo =

-1.55

P = 2* Z()

0.12

Confidence =

87.9%

x bar (sec) =

56.7

s =

14.7

1- alpha =

0.95

alpha =

0.05

alpha/2 =

0.025

61.89

51.56

95% Confidence Interval for the true average graphing time

51.56 <= Population Mean <= 61.89

57 +/- 5.16 seconds

240

Rubric_Process Improvement Project.xlsx

Blank

Process Improvement Project – Feedback –
Content Requirements	Possible Points	Points Earned	Comments
Project
A) An executive summary is provided in the storyboard format including: Is the storyboard presented in 1 PowerPoint slide? Follows DMAIC? Are tools/graphs/charts used and clearly visible? Do they support findings and conclusions Are arrows, call-out boxes, etc. used to summarize, highlight questions and key learnings? Are expected results clear? And next steps noted?	5
B)Is it a cohesive presentation opening with the business process and problem statement? The submission is 5-15 slides.	2
C) Was the success measure clearly identified, operationally defined and baseline identified? (Was the data identified as continuous or discrete, includes SQL?)	3
D) Was the data measurement plan or data stratification tree included?	1
E) Was the data collection method identified?	1
F) Was there rationale for the sample size taken? Use of the formula? Is there any reference to measurement error and how to minimize?	2
G) Are at least 5 different tools and techniques clearly identified? Are the tools linked/ pertinent to the data analysis?	4
H) Does the data analysis clearly tie to the problem conclusion? Is the “discovery” clear to the reader?	2
Total possible 100 points	20	0.00

storyboard_template_2.ppt

DEFINE

MEASURE

Name of your project

Team

Launch

Define

Measure

Analyze

Control

Improve

Key Dates --->

ANALYZE

IMPROVE

Process owner: your Name

EXAMPLE

CONTROL

DMAIC_courseflow_.pptx

Description:

Clearly identify the business problem / performance gap (output measure), customer, scope, goals and resources.

Key Concepts:

y = f(x)

Types of data

Descriptive statistics and soft tools

Project:

Complete Problem Definition Worksheet

Tools:

Process map

SIPOC

Descriptive statistics

Thought process map

Affinity diagram

Sigma Quality Level (SQL)

Description:

Validate your measurement system and collect baseline data.

Key Concepts:

Mapping a process/value-stream, forms of waste, measurement error, reproducibility, repeatability

Project:

Identify potential inputs, develop operational definitions, develop data measurement/collection plan, validate measurement system, collect baseline data, calculate SQL.

Tools:

Operational definitions

Kappa

Process map (detailed)

Data measurement plan

Data stratification tree

Histogram

Trend/ line chart

Pareto chart

Fishbone (cause/effect) diagram

DMAIC

Week 1

Week 2

LM:MBC638

Define

Measure

Key Concepts:

Inferential statistics, common distributions, developing a hypothesis, determining the likelihood some event happens based on a sample (calculating probabilities), Using the normal distribution as the “go to” distribution.

Project:

Write a null and alternative hypothesis statement.

Tools:

Hypothesis testing

Chi-square test for independence

Week 3 & 4

Week 5

Week 6 &7

Key Concepts:

Collecting sample data, how confidence intervals and sample size are related.

Project:

Utilize the sample size formula.

Tools:

Confidence intervals.

Key Concepts:

Determining input’s (x) impact on the output (y).

Project:

Use regression to identify relationships between the output (y) and inputs (x’s).

Tools:

Correlation

Simple linear regression

Multiple regression

Scatterplot

Trend/ line chart

Pareto chart

Fishbone (cause/effect) diagram

Description:

Analyze, describe, and present the data to discover the root cause(s), identify/prioritize critical inputs (x’s), determine the inputs impact on the output.

LM:MBC638

Analyze

Description:

Develop potential solutions, select best solution, pilot solutions, measure results, document new process.

Key Concepts:

Discover y= f(x)

Project:

Implement a solution, run a pilot, evaluate the results, complete a hypothesis test.

Tools:

Affinity diagram

Fishbone cause/effect diagram

Pareto

Control charts

Hypothesis testing

Process map

Solution selection matrix

Description:

Implement process changes and controls. Verify expected performance was achieved, monitor performance to sustain new levels.

Key Concepts:

Xbar/R and ImR control charts, Different control charts applicable to different processes, time series forecasting methods predict future performance.

Project:

Utilize an appropriate control chart and /or time series forecasting method

Tools:

Control charts

Time series analysis

Operational definitions

Process map

Sigma Quality Level (SQL)

Week 9

Week 8

LM:MBC638

Improve

Control

Process_Improvement_Project_Requirements_122017.ppt

Process Improvement Project

Project Selection Criteria:

Select an issue or opportunity that can be written as a problem statement.
Must be within your sphere of influence.
Is not an attempt to solve world hunger.
Uses data that is accessible to you or can be collected in a reasonable amount of effort/time.
You have the ability to measure the current and future state. You have access to baseline data or can collect it.
Preferably uses more continuous data (rather than all discrete data).
Fixing this problem will provide value. You should develop a business case to support working this issue (consider your time and others when calculating ROI.)

Examples:

Improve product quality

Reduce expenses

Improve the output of your organization

Decrease wait time

Page 1

1) Executive Summary :: Storyboard (should be presented in 1 PowerPoint slide)

Follow the DMAIC steps

Include the problem statement and baseline

Utilize at least 5 different tools/techniques (present relevant key tools to best tell your story).

Be readable; summarize and condense exhibits where necessary

Use arrows, call out boxes, and balloons to highlight questions and key learnings

Display data/charts supporting your findings and conclusions

Show results or expected results

Process Improvement Project

-Requirements-

Page 2

The final submission should be 1 file, in slide format, created in PowerPoint.

It should include 2 parts:

Executive summary slide – 1 slide Storyboard (specific requirements below).

2) Back-up slides – additional 5-15 slides (specific requirements pgs. 3-4). This is not a repeat/copy of your storyboard. The back-up slides should detail and support the content of your storyboard.

*************************************************************************************************************

2) Back-up slides - following the Storyboard include 5-15 slides containing the answers to the following questions.

DEFINE

What is your goal? How will you know if you’ve been successful?
Have clear operational definitions been established for your inputs and outputs?
What is the process you’re trying to improve? What are the current steps of the process?

MEASURE

Include your Data Measurement Plan or Data Stratification Tree (examples on pgs. 5-6).
What type of data did you collect (cost, cycle time, changeover time, yield, machine utilization, scrap, rework, defects, inventory)?
Was that data continuous or discrete?
Did you collect your own data or did you use existing data?
How much data did you collect and why? What is your ideal sample size using the sample size formula? What is the risk if you collected fewer samples?
How was your data collected? Describe the methods you used to collect it.
Where could you have measurement error? How much measurement error do you have? What could you do to minimize your measurement error?

Process Improvement Project

-Requirements-

Page 3

2) Back-up slides continued:

ANALYZE

What tools did you use to analyze the data? (Utilize at least 5 different tools/techniques and show evidence and detail of the tool/technique).
What is the data telling you? What did you discover?
What is the SQL for the old and new process?

IMPROVE

What solutions did you propose and/or implement? Did you successfully improve your process? What did you learn about your process?

CONTROL

How will you use this information to “hold the gains” of your improvement or make the next round of improvements in your process?

Process Improvement Project

-Requirements-

Page 4

Data Stratification Tree

Questions About Process

Stratification factors

X Variables

Measurements

New Orders

Time of year (mo.)

Training

Skill level

Wait time

(Output Y)

Does the Sales Rep have the right skills to improve selling more orders?

Are orders impacted by the sales rep skill-levels (systems, product, pricing, listening, ability to follow the process)?

Pricing Issue

Customer attitude

No.of backorders

Do new orders vary by month ?

% of orders per Sales Rep by skill level type
average & range of Sales Rep skill levels

What % of the calls are order related?

Do new orders change by the receptiveness of the customer?

Are orders impacted by call wait time?

Are orders impacted by pricing issues?

Are orders impacted by whether or not the Sales Rep follows the written process?

Do new orders vary by the availability of the product (not on backorder)?

Type of call

Written process

% type of call

wait time for each call

customer attitude rating by order type

% of calls transferred to OB due to pricing issues

mystery call /silent monitoring results (points per call)

% of orders resulting in backorders

total orders placed by month
% new orders are of total orders
% new order revenue of total revenue by month

no. of hours of training per month

Are orders impacted by call duration?

Call duration

Average call duration for xyz order vs. other orders

Do the current targets impact orders?

Target settings (calls, orders, revenue)

calls, orders, total rev, rev per mo. per Sales Rep

Page 5

Data Measurement Plan

Performance Measure

Data Source and Location

Target

Sample

Size

Who Will Collect

Data

When Will Data Be Collected

How Will Data Be Collected

% type of call

No. of inbound calls per day

order revenue per Sales Rep per month

Total revenue per month

Revenue per month by product type

Manual data collection

Susie

Develop rating scale & assess performance

John’s training spreadsheet

Use data collection form

Manual data collection

Manual data collection

Manual data collection

IB performance reports

Susie

Use data collection form

Use data collection form

Use data collection form

Use data collection form

John

5/11-6/2

5/20

5/12

1000 calls

500 orders

Pull from report

Leanne

By 6/3

28 mo

5/11 - 6/2

tbd

5/11 - 6/2

Manual data collection

Manual data collection

tbd

Monthly mystery call results

Leanne

By 6/3

Compile Pamela’s data

500 orders

12 mo

28 mo

28mo

28 mo

Leanne

ytd

Obtain from other team

Aspect reports

Leanne

% of orders per Sales Rep by skill level type
average & range of Sales Rep skill levels

wait time for each call

customer attitude rating by order type

% of calls transferred to OB due to pricing issues

mystery call /silent monitoring results (points per call)

% of orders resulting in backorders

total orders placed by month
% new orders are of total orders
% order revenue of total revenue by month

no. of hours of training per month

Average call duration for new order vs. other

calls, orders, total rev, per Sales Rep per month

N/A

IB performance reports

Pull from report

Susie

By 6/3

28 mo

IB performance reports

Pull from report

IB performance reports

Pull from report

IB performance reports

Pull from report

SN report

Pull from report

By 6/3

Susie

Susie

Susie

By 6/3

Page 6

DEFINE

MEASURE

Name of your project

Team

Launch

Define

Measure

Analyze

Control

Improve

Key Dates --->

ANALYZE

IMPROVE

Process owner: or your Name

Storyboard

template

CONTROL

Page 7

Process Improvement Project

-Rubric-

Page 8

Content Requirements	Possible Points
A) An executive summary is provided in the storyboard format including: Is the storyboard presented in 1 PowerPoint slide? Follows DMAIC? Are tools/graphs/charts used and clearly visible? Do they support findings and conclusions Are arrows, call-out boxes, etc. used to summarize, highlight questions and key learnings? Are expected results clear? And next steps noted?	5.0
B)Is it a cohesive presentation opening with the business process and problem statement? The back-up slides (5-15) detail and support the storyboard content.	2.0
C) Was the success measure clearly identified, operationally defined and baseline identified? (Was the data identified as continuous or discrete, includes SQL?)	3.0
D) Was the data measurement plan or data stratification tree included?	1.0
E) Was the data collection method identified?	1.0
F) Was there rationale for the sample size taken? Use of the formula? Is there any reference to measurement error and how to minimize?	1.0
G) Are at least 5 different tools and techniques clearly identified? Are the tools linked/ pertinent to the data analysis?	5.0
H) Does the data analysis clearly tie to the problem conclusion? Is the “discovery” clear to the reader?	2.0
Total	20

03GoodEXAMPLEs_storyboard_students.pdf

DEFINE MEASURE

Process Improvement Project – Graphing Time Reduction Mike – MBC 638

Team Launch

5/11/08

Define

5/19/08

Measure

5/26/08

Analyze

6/6/08

Control

On-Going

Improve

7/4/08Key Dates --->

ANALYZE IMPROVE

BUSINESS CASE: $18,943 Annual Cost Reduction if Implemented in Engineering Department

Extensive graphing is

required for good data

analysis of lab qualification

testing

350 Engineer hours in the

department are spent on

repetitive graphing

procedures within Excel.

This equates to

$52,471/year

 A 30% reduction in

graphing time could result in

a $15,741 annual savings.

Questions about Process Output Stratification Factors Measurements

X Is y af f ected by the number of

data points recorded in a test?

X1 = Total Data points

collected

Number of data points

collected

Is y af f ected by the total number

of columns graphed?

X2 = Variables included in

graph

Number of dif f erent

variables measured

Is y af f ected by the person

creating the graphs? X3 = Operator Name of engineer

Is y af f ected by the quality of the

graph required?

X4 = Graph quality Title, labels,

presentation ready

Is y af f ected by additional graphs

required f or comparison? X5 = Number of graphs Number of graphs

Is y af f ected by the total number

of columns of data available?

X6 = Total number of

available variables

Total number of

available variables

Data Stratification Tree

Y =

Graphing

Time =

f (X)

Y = f(X1,X2,X3,X4,X5,X6)

Day 1/Operator 1 - Xbar Chart

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

0 1 2 3 4 5 6

Run #

X b

a r

Day 1/Operator 1 - R Chart

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 1 2 3 4 5 6

Run #

Pareto Diagram of Number of Variables Graphed

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 to 4 5 to 7 8 to 10 11 to 13 >13

N u

m b

e r

V a ri

a b

le s

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

C u

m u

la ti

v e

F re

q u

e n

c y

Current Improved

Time per graph (sec) 57 36

Time spent

graphing/year (hr) 350 224

Annual Cost Reduction 52,471$ 33,529$

% Percent Annual Cost

Reduction

Cost Reduction

36%

Population Mean = 56.7

Std Deviation = 14.7

X2 (Upper Spec Limit) 60

Z2 = 0.223

P(X>60) = 0.412

P(X is out of spec) = 0.412

DPM = 411655

SQL = 1.72

SQL Baseline

Population Mean = 36.2

Std Deviation = 13.5

X2 (Upper Spec Limit) 60

Z2 = 1.762

P(X>60) = 0.039

P(X is out of spec) = 0.039

DPM = 39052

SQL = 3.26

SQL Improved Process

H0: mu >= 40

H1: mu < 40

Acceptable Level of Risk= 10%

alpha = 0.10

n = 31

New Process Mean = 36.2

New Process Variance = 181.8

Zo = -1.55

P = 2* Z() 0.12

Confidence = 87.9%

Sigma Quality Level

Increased from 1.72

to 3.26

$18,943 annual cost

reduction! $

Eliminated wasteful, repetitive

steps that can be automated

with Excel Macros

Pareto showed that 80% of

graphs consisted of 8

variables or less

n= 31

x bar (sec) = 56.7

s = 14.7

1- alpha = 0.95

alpha = 0.05

alpha/2 = 0.025

U= 61.89

L= 51.56

95% Confidence Interval for the true average graphing time

51.56 <= Population Mean <= 61.89

57 +/- 5.16 seconds 95% confidence interval that

true graphing time is 57 +/-

5.16 seconds

Hypothesis test indicates 88%

confidence that new process

meets the goal of at least a 30%

improvement in graphing time

Identified Critical Inputs and

Outputs to Measure

Measurement Systems Analysis

using X-bar, R Charts and

Precision-To-Total Ratio

R Charts in control.

Xbar Charts show ability to

measure differences

PTR = 0.22

Control

 Maintain Revision Control on

original spreadsheet macros

 Provide to Engineering Department

 Survey engineers for usage in 3

months

http://www.convergingsystems.com/img/stopwatch.gif

DEFINE MEASURE

Process Improvement Project – Cooking Optimization

Define

Feb

Measure

March Analyze

March

Control

May Improve

April Key Dates --->

ANALYZE IMPROVE

Process owner: Neil

CONTROL

• Optimize the governing

factors of cooking, with

considerations being the

taste and cost.

• 2 types of dishes- A & B

• A & B have a fixed amount

of chicken, carrots,

tomatoes, capsicum, onion.

• Chilies and Garlic also, but

varying quantity.

Taste of Dish A

When Y = cost, all ingredients are

significant; but when Y = Taste above

inputs are significant

S- Money (Myself) & Wegmans (Ingredients)

I- Ingredients, Utensils, Electric Stove

P-Cooking Process

O- Dish A & B

C- Volunteers

Taste of Dish B

DISH A

DISH B

Dish B>Dish A, avg. means

(even with less ingredients)

Keeping the ingredients which

have significance to cost and taste

in mind, with preferences to taste; I

run the test again with less quantity

of salt, garlic, siracha, soy sauce,

sugar and vinegar.

We can see from the new t-test

that the mean cost of the dish A

& B now have lower avg. means

and only chili & garlic are in

excess (acceptable).

DISH A

& B

average

savings

$15.2

BUSINESS IMPACT

To save money while

still maintaining taste

of the dishes

DEFINE MEASURE

Team Launch: Define: Measure: Analyze: Control:Improve: Key Dates

ANALYZE IMPROVE

Process owner: Landon

CONTROL

September 10 September 17 September 24 October 29 November 19

Landon, Engineering Project Managers, Finance Office, Stakeholders

September 6

Problem Statement

Cycle time for a signature

sheet averaged 11.25 days

with each project manger

spending about 11.25 hours

for each purchase over

$100,000.

Business Impact

The average wage a project

manager earns is $35/hr,

therefore it costs $393.75 per

project in just gathering

signatures! At 144 projects a

year this process costs

$56,700 annually.

- Finance Office will not accept old

version of signature sheet.

- Purchases tracked in SharePoint

- Appraisal rated on compliance

1 3 5 7 9 11 13 15 17

F r e q

u e n

c y

Cycle Time (Days)

1 3 5 7 9 11 13 15 17

F r e q

u e n

c y

Cycle Time (Days)

New Old S

ig n

a tu

re S

h e

e t

C y

cl e

T im

e (m

a n

-h o

u rs

)

Past man-hours

4 11

6 11

7 11

9 12

9 13

10 13

10 14

11 14

11 17

11 28

Mean= 11.25

Std dev= 4.57

Median= 11.0

High man-

hours High cycle

time

Project manager signs form

Obtain engineering supervisor’s signature

Submit to Finance

Obtain production supervisor’s signature

Obtain engineering director’s signature

Obtain group director’s signature

Submit form to Finance Department

Wait for Thursday

Obtain finance supervisor’s signature

Is it

Thursday?

Is project over

$100,000?

Too many steps!

Need to take

PM out of all

these steps • Cycle time reduced to 2.88 days!

• Man-hours reduced to 0.20 hours!

• Cost savings $55,692 annually!

• SQL raised from 2.3 to 3.6 and rising!

SQL = 2.3 Hypothesis Test Ho: mu ≥ 11.25 hr

Ha: mu < 11.25 hr

7.240 24/23.0

5.112.0

/ 

 

x t

 P-value ≈ 0

Electronic Signature Sheet

SQL = 3.6

R = 0.78

between no. of

steps and time

EV 7.21

AV 3.65

R&R 10.86

PV 89.14

Percent of Total

Variance Ratios

DEFINE MEASURE

Process Improvement Project - for Widget Assembly

Team

Launch

Define

2/02/2017

Measure

2/16/2017

Analyze

03/23/2017

Control

04/20/2017

Improve

04/13/2017 Key Dates --->

ANALYZE IMPROVE

Nelson

CONTROL

Problem statement: Due to high cycle

times we cannot get the desired amount of

throughput for the Widget assembly. The

average cycle time for each part is less than

44 seconds. Defects are inherent because of

improper line balancing and bottleneck in

the system.

SQL = 2.752

Ho :Mean cycle time for part >= 44 seconds

Ha :Mean cycle time for part < 44 seconds

Sample size 36

Sample mean 45.036

Standard Deviation 2.681

Hypothesized mean 44

Test Statistic (Z) 2.318537859

P-value 0.989789946

From the above result P-value is greater

than 0.05 thus we do not reject the Null

hypothesis and thus the Mean cycle time is

greater than 44 seconds. Hence we need to

analyze what’s wrong

1 2 2 1.5 1

Easy to implement Resources Cost Process impact Complexity Total Rank

Solution 1 5 3 3 1 5 23.5 3

Solution 2 1 1 1 5 1 13.5 4

Solution 3 3 3 5 3 3 26.5 2

Solution 4 3 3 5 5 3 29.5 1

Solution 1

Solution 2

Solution 3

Solution 4 To implemet Solution 1 and Solution 3 together

To reduce number of activites at station 5 and distribute them equally

Increase one more station and equally dstirubte the activites at station 5

Reduce one station form the process line and combine two stations

SQL = 3.545

Mean 47.94 45.30

Known Variance 10.37 3.57

Observations 36.00 44.00

Hypothesized Mean Difference 0.00

z 4.36

P(Z<=z) one-tail 0.00

z Critical one-tail 1.64

P(Z<=z) two-tail 0.00

z Critical two-tail 1.96

Here the p-value = 0.00 < 0.05. Hence we reject the null hypothesis and we

can say the Cycle time before is greater then the cycle time after

improvement. Hence we have improved our process considerably.

We have finalized

fourth proposed

solution from the

matrix and thus

we need to

improve the

process based

upon the selected

solution.

Here we have selected the R chart and Xbar chart for the

control of the process because the sub group size is

only two for my process. Now as we can see the R chart seems to be under

control. Only some of the points on Xbar chart are out of control for the process. Thus we need to double

check what wrong at those points and figure out a way

to get the process under control.

Also from the Cp and Cpk values we can see that our

process is capable to be undertaken but there is still scope of improvement to

make it more capable.

Cp = 1.16

Cpk = 1.08

Why is SQL less?

What can be done better?

What factors shall we

measure, analyze and

improve?

R&R =10.86% < 30 %,

Measurement plan is okay

Because the Station 5 has the highest

number of activities being done and thus it

takes the highest time. So we need to

equally divide the number of activities

hence to reduce cycle time

> 44 seconds

DEFINE MEASURE

Process Improvement Project – Cycle Time Reduction

Team Launch

8/23

Define

9/08

Measure

10/16

Analyze

10/24

Control

On-Going

Improve

10/31 Key Dates --->

ANALYZE IMPROVE

Process owner: Dan

CONTROL

PROJECT TEAM: Dan  Mary  Karen  Linda  Peter

BUSINESS CASE: $54,000 in annual processing costs

It takes 43 days to process a

grant application. Only 8% of

applications are being

processed within 30 days of

receipt. The time to process

the application has lead to

unhappy applicants and staff

who are finding more and more

of their daily work time being

devoted to “grant

administration.” The funding

levels available to applicants

and the number of applications

are expected to increase in the

near future, which has the

potential to compound the

problem.

Number of Complete Applications

5 7

8 8 8 7

9 11 11 12

13 13 14

Ju l, 20

0 6

S ep

t N o v

Ja n

M ar

M ay

Ju l, 20

0 7

Date

N u

m b

e r

Series1

Linear (Series1)

The Number of applications

received is increasing.

The time to complete a process

cycle is also increasing.

Total Application Errors by Type

132

14 9

75.0%

86.9%

94.9%

100

120

140

160

Incomplete/Incorrect ApplicationsStalled Approvals In The Log-in

Phase,

Stalled Approval At The

Managerial Level

Reworks

Type

D e fe

c ts

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

Problem:

Incomplete and inaccurate

applications were identified as

the primary factor leading to

defects in the process cycle.

Solution:

New Application process

incorporating drop down menus

New Application Procedure =

Less Mistakes & Quicker Cycle Time

Base Group

62.06UCL

42.50CL

22.94LCL

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126

Units

C y

c l e

i m

e - D

a y

New "Improved" Process

UCL 44.59

CL 28.56

LCL 12.53

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Units

C y

c le

T im

The defect rate reduced from 93% to 32%

Monthly monitor and review procedure is in

place. Out of control signal = action plan.

↑ Number of Applications

+ ↑ Cycle Process Time __Tough Times Ahead

Time to Complete Proccess Cycle

Jul-06 Aug Sept Oct Nov Dec Jan Feb March April May June Jul-07

Date/Time/Period

T im

e (

D a

y s

)

Data 1

Median

Goal

43 days

29 days

SQL=3

http://www.convergingsystems.com/img/stopwatch.gif

http://www.eureka-moment-books.com/home/eurekahp.gif

http://images.google.com/imgres?imgurl=http://www.wpclipart.com/signs_symbol/safety_signs/danger_sign.png&imgrefurl=http://www.wpclipart.com/signs_symbol/safety_signs/&h=724&w=1136&sz=34&hl=en&start=186&tbnid=C6BoUHkP3qXFWM:&tbnh=96&tbnw=150&prev=/images%3Fq%3Ddanger%26start%3D180%26gbv%3D2%26ndsp%3D20%26svnum%3D10%26hl%3Den%26sa%3DN

DEFINE MEASURE

Increase Monthly Income of Check Cashing Business

Project Launch

2/2

Define

2/3

Measure

2/11

Analyze

3/16

Control

4/15 Improve

4/6 Key Dates --->

ANALYZE IMPROVE

Process owner: Yanni

CONTROL

Problem Statement

Revenue varies by month.

Weekly average of check

Cashed amount= $80k

Variability:

Cashed amount= $45-50k

Business Impact

Increase yearly cashed

amount by $60K-70K

by reducing the

variability per month

by.

That will improve

monthly average

income.

Feb Mar

20000

40000

60000

80000

100000

1 16 31 46 61 76

C a sh

e d

A m

o u n t

Date

RUN Chart

Cashed Amount

Linear (Cashed Amount)

Multiple Linear Regression

Analysis

• Advertisements

• Money Lost due to Fraud

Checks

• Monitor the Run chart weekly.

• Implement advanced check

cashing equipment and keep

track of Bounced checks.

• Increase newspaper and flyer

advertising.

Weekend

spikes

After ChangeBefore Change

1000

800

600

400

200

# o

f R

e je

c t s p

e r D

a y

Frequency Rejects Before Change & After Change

DEFINE – 5/15/11 MEASURE - 6/1/11

Finding the Skinny on Thin Film Sensor Reject Rates

Control

Improve

ANALYZE - 7/1/11 IMPROVE - 8/1/11 1) Problem Statement:

Production reject rate of thin film

sensors increases after process

change.

3) Business Impact:

Reducing/eliminating frequency

rejects will prevent reworking of

part, extra inventory and labor

from 100% testing which could

potentially save

Cause & Effect Matrix Scoring:1=low, 3 = med, 5=high,

Importance to Customer (sensors w/ correct frequency) = 1

Process

Inputs

(X)

Effect Rating Probability Score

Vendor

frequency

sorting

quality

Allow accurate

calculation of

thickness

5 High, makes adjustments

when providing thickness

data to techs

Fixture

Geometry

Even coating

thickness

5 High, location determines

the thickness of the

coating.

200

400

600

800

1 /5

/2 0

1 0

1 /2

6 /2

0 1

0 2

/2 3

/2 0

1 0

3 /3

1 /2

0 1

0 4

/2 0

1 0

5 /1

0 /2

0 1

0 6

/2 /2

0 1

0 6

/2 2

/2 0

1 0

7 /1

4 /2

0 1

0 8

/9 /2

0 1

0 9

/1 6

/2 0

1 0

/1 8

/2 0

1 0

1 1

/4 /2

0 1

1 /2

9 /2

0 1

2 /1

6 /2

0 1

/1 0

/2 0

1 1

2 /1

/2 0

1 1

2 /2

0 1

1 3

/1 4

/2 0

1 1

Moving Range Chart

Jan 1, 2010-April 4, 2011

Began testing new material

June 7,2010

100806040200

% Vendor A

% R

e je

c t s

Scatterplot of % Rejects vs % Vendor A

The r2 shows that the amount of raw material used from

Vendor A explains 46.6 % of the change in reject rate.

0.00% 5.00% 10.00%

Frequency

Pattern

Contaminates

Quartz

Marks/Scratc

hes

Low Activity

Pareto Chart of Defects

Oct 4,2010 -April 4, 2011

8) Probable Cause 2 – Evaporation Fixture Geometry

7) Probable Cause 1 - Raw Material Supply

The sensors are held in a fixture positioned over a

evaporation source that coats them with metal. I

performed a test run to measure baseline

performance. The data revealed that the metallic

coating has too much variation in thickness w/ a

mean of 2235 Å, but the range should be 500 Å.

This could be caused by the position of the source,

size of mask or angle of the holding fixture.

2) Work on largest category of

defect for MAXIUM IMPACT

Before

After

4) Out-of-Control:

Process is highly variable to begin

w/ but much worse after change.

5) Change of Focus

The change did cause an increase in

variability, but the process is not

very good to start w/ a DPMO of

19,263! Finding the root cause of

the inherent process variability

should solve the new issue.

6) Identify Primary Inputs (Y)

A second run was done to test if a centered evaporation

source would decrease thickness variability (Ha). A

one-tail test was performed & the P value was high,

thus it did not significantly improve the process. This

points to the mask size & fixture as the root cause of

the variation.

Z = -1.19 P = 1-Z = 1-1.19 =0.86 =86%

Ho: Test 1 thickness variability ≤ Test 2

-Receipt of material from Vendor A

was halted. A comparison of their

measurements vs. ours found a 7.6 KHz

difference!

6058500605700060555006054000605250060510006049500

Frequency

F r e

q u

e n

c y

Mean 6053366

StDev 1569

N 200

Histogram of Frequency Normal

They recalibrated their instruments &

next shipment was markedly improved

with a mean very close to the center of

our specification range of 6.055 as

shown on this histogram.

9) Solution to Probable Cause 1

10) Solution to Probable Cause 2 I’m working with engineering to

develop a new fixture that will

improve the geometry.

Scatter plot reveals that using raw materials

from Vendor A has a strong positive

correlation of 0.7 with the reject rate.

0.00

5000.00

10000.00

1 2

2 4

3 6

4 8

5 1

0 6

1 2

7 1

4 8

1 6

9 1

9 0

2 1

1 2

3 2

2 5

3 2

7 4

2 9

5 3

1 6

3 3

7 3

5 8

3 7

9 4

0 0

4 2

1 4

4 2

4 6

3 4

8 4

Range Chart - Vendor A

Constructing a control chart of measurements taken by QC

of frequency illustrates that the vendors process is out of

control.

10) Changes to be Made:

QC technician does acceptance

testing of raw materials w/ zero

tolerance.

Vendor supplies Certificate of

Analysis w/ test statistics.

Control chart created for raw

materials.

New fixture for more uniform

thickness to prevent any frequency

rejects.

Control – 8/8/11

Rick, Steve & Production Staff

project_ideas_withxy_examples_pdf.pdf

**Example** **Example**

Process Improvement Projects Output (y) Potential inputs (x) but there could be many more/others

Increase product sales Sales dollars

Types of product, number of sales people, time spent on calls, dollars

spent on ads, dollars of inventory on hand

Reduce cost of rework Rework dollars

Defect types, different production lines, different operators, shifts, time

spent on rework per defect type, operator training-level

Increase time with family (Reducing non-value added activities) Time for NVA in minutes

Time at work, time to bed, hours slept, meals purchased, meals made,

time per activities, homework time, dollars spent

Decrease your landfill waste Waste in pounds (or ounces)

Categories of waste, days /time waste is disposed, who generates the

waste, hours at work, hours at home, activity types

Improve sleep time/quality Sleep time in hours (or minutes)

Time to bed, hours worked, calories after 8pm, time to wake, minutes

of exercise, total oz. beverages consumed, caffeine consumed

More Project Ideas

Improve the quality of a baked good while decreasing cost

Reduce the number of defects

Increase beer production efficiency

Reduce produce (fruits and vegetable) waste

Decrease home or facility energy use

Decrease your carbon footprint

Improve Twitter utilization

Reduce headache frequency/severity

Increase billable hours

Reduce document registration errors

Decrease time on cellphone

Improve Instagram utilization

Increase furniture sales

Reduce time to assign nursing care

Decrease blood sugar levels

Increase number of loans processed

Increase revenue

Increase customer satisfaction

Reduce cycle time

Decrease the amount of time it takes to place/process an order

Decrease the time to repair

Reduce process variation between production plants

Reduce rejected material

Reduce wait time per patient

Improve revenue generated per client

Improve the effectiveness of marketing communication

Reduce the number of cosmetic defects per product

Increase net profit per product

Reduce fraudulent loss

Improve cycle time of payments

Increase revenue generated per loan application

Increase number of contacts

Increase number of website hits

Increase number of leads

Decrease time late to work

Reduce the amount of time it takes to certify a supplier

Reduce the number of customer complaints

Improve the performance rating of a piece of equipment

Reduce variation between equipment

Increase the number of customer calls

Reduce time to order advertising space

Increase customer contact time

Increase investment dollars

Decrease inspection time

Decrease grocery bill

Reduce overall food expense

Reduce commute time to/from school or work

Reduce personal expenses

Decrease time associated with dealing with "behavior issues"

Maximize quality study time

Increase attendance to a particular event

Reduce time spent managing the budget

Improve indoor air temperature

Decrease cost of living expenses

Reduce the time it takes to produce a status report

Reduce test variability between labs

Reduce the number of steps in the quoting process

Improve the ratio of dollars spent per prescription written

Reduce the time it takes to close a real estate deal

Generate incremental revenue from existing customers

Reduce backlog of claims

Increase server utilization

Reduce raw material inventory

Reduce time to mitigate/process a raw material discrepancy

Reduce the number of delays and cancellations

Optimize a truck delivery

Reduce gallons of fuel consumed

Reduce overall processing time of RFQs

Reduce process time to ship parts

Reduce overtime

Increase the number of deliveries

Increase customer service calls processed

Increase customer (client) visits

Reduce start-up costs

Reduce time to solve customer problem

Increase school enrollment and revenue

Improve advertising agency workflow

Increase reliability test performance

Reduce travel time

Increase online sales profit

Reduce average number of golf putts per round

Reduce errors found by auditors

Improve utilization of trailer space

Reduce cost overruns in IT

Reduce gap between actual billed hours vs. estimate

Improve meeting efficiency

Increase throughput providing a service

Reduce errors made per invoice

Increase number of transactions that occur online

Reduce cycle time to process grants

Increase time available in a day to exercise

Decrease clean-up costs

Reduce parking expense

Reduce food/grocery expenses

Improve consistency of each batch produced

Improve email response time

Reduce discretionary spending

Reduce multiple shipments per client

Reduce morning time (out the door) process

Reduce travel expenses

Increasing number of clients

Reduce repair time

Reduce number of open purchase orders

Reduce fuel consumption

Increase sales payouts

Improve staffing utilization

Reduce cycle time for trash removal process

Reduce room temperature variation

Increase quality leads

Increase adoption rate of sales tool

Generate more internet traffic

Reduce the gap between estimated and actual asset value

Reduce raw material costs

Reduce scrap rate

Data Measurement Plan Template.pptx

Data Measurement Plan

Performance Measure	Data Source and Location	How Will Data Be Collected	Who will Collect Data	When will Data be Collected	Target Sample Size

Data Stratification Tree Template.pptx

Data Stratification Tree

Questions About the Process		Stratification Factors	Measurements
		X Variables





	(Output Y)

0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0

0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0

0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0
0	0