OPER3215 – Lean Concepts and Continuous Improvement Statistics Assignment – Winter 2019 Case 1 -Fleetwood Auto

OPER3215 – Lean Concepts and Continuous Improvement

Statistics Assignment – Winter 2019

Case 1  –Fleetwood Auto

Fleetwood Auto manufactures various parts for automobile transmissions.  One part, the clutch plate, resembles a flat round plate with four (4) keyholes stamped into it (see Figure 1 below).  During the stamping process there is a small mark placed above one of the keyholes.  This mark labels keyhole 1.  Clockwise around the part, the other keyholes are designated 2, 3, and 4.  Recently, the customer brought to Fleetwood’s attention the fact that not all of the keyholes are being cut to the correct diameter.  Fleetwood’s quality team asked the operator to measure each keyhole in five (5) parts every 15 minutes and record the measurements.  The measurements are found in Table 1.

Figure 1 – Clutch Plate Diagram

Specification 0.390 cm  +/- 0.0045

 

 

Table 1 – Clutch Plate Data

Sample	Keyhole 1	Keyhole 2	Keyhole 3	Keyhole 4
1	0.3801	0.3904	0.3905	0.4014
2	0.3822	0.3898	0.3911	0.3992
3	0.3854	0.3911	0.3901	0.3980
4	0.3831	0.3901	0.3898	0.3974
5	0.3842	0.3908	0.3904	0.4022
6	0.3851	0.3897	0.3911	0.4008
7	0.3819	0.3901	0.3910	0.3981
8	0.3877	0.3893	0.3907	0.3997
9	0.3843	0.3900	0.3904	0.3972
10	0.3841	0.3902	0.3891	0.4006
11	0.3831	0.3900	0.3900	0.3980
12	0.3852	0.3905	0.3911	0.3977
13	0.3867	0.3898	0.3901	0.3964
14	0.3862	0.3908	0.3902	0.3973
15	0.3821	0.3897	0.3904	0.3961
16	0.3809	0.3904	0.3909	0.4014
17	0.3823	0.3894	0.3911	0.3993
18	0.3850	0.3904	0.3901	0.3988
19	0.3837	0.3905	0.3898	0.3977
20	0.3846	0.3903	0.3901	0.4021
21	0.3852	0.3894	0.3911	0.4005
22	0.3811	0.3909	0.3914	0.3983
23	0.3870	0.3892	0.3907	0.3997
24	0.3851	0.3904	0.3903	0.3962
25	0.3846	0.3903	0.3897	0.4004
26	0.3837	0.3907	0.3904	0.3981
27	0.3851	0.3905	0.3912	0.3974
28	0.3863	0.3899	0.3917	0.3964
29	0.3866	0.3907	0.3904	0.3971
30	0.3824	0.3893	0.3905	0.3962

 

1. Based on the sample data, calculate the summary statistics for each keyhole: Mean, Median, Mode, Minimum, Maximum, Range and Standard deviation.  Use functions for this – not the Statistics Package.
2. Construct a histogram, for each keyhole, from the sample data – describe the shape, location (center) and spread of each histogram.

Shape – the shape shows how the variation is distributed (is the data symmetrical or skewed)

http://asq.org/learn-about-quality/data-collection-analysis-tools/overview/histogram2.html

Location – The location is the expected (center / midpoint) value of the output being measured.

Spread – The spread is the expected amount of variation associated with the output. This illustrates the possible values that we would expect to see.  Is there any outliers?

 

3. Based on the statistical data and specification limits what is the estimate of the % of Keyholes that will be less than the specification? Estimate for each Keyhole separately.
4. Based on the statistical data and specification limits what is the estimate of the % of Keyholes that will be greater than the specification? Estimate for each Keyhole separately.

 

Case 2  –Grand River Hospital

Grand River Hospital is trying to improve the patient experience by improving food services with tasty, inviting patient meals that are also healthful. A questionnaire accompanies each meal served, asking the patient, among other things, to specify any issues with the meals.  A 200-patient sample of the survey results over the past 7 days yielded the following data:

 

		Reason for Issue
Day	# of Patients with Issues	Unclean Utensils	Incorrect Temperature of Meal	Dietary Restriction Meal – Missed	Food Not Fresh or Unhealthy
1	34	4	14	12	4
2	66	12	35	15	4
3	22	2	8	10	2
4	32	2	22	6	2
5	12	2	4	5	1
6	19	1	13	4	1
7	28	2	19	6	1

In an effort to identify the root cause of the issues, the improvement team collected additional data from the same seven day period to analyze and determine patterns.

Additional Data

Day	Total # of Meals Served	# of Meals with Dietary Restrictions	# of Kitchen Staff on Duty
1	735	120	32
2	765	182	27
3	710	152	34
4	754	185	33
5	578	145	28
6	704	200	35
7	687	183	32

 

 

 

 

1. Regression Analysis:  What could be the possible cause of the issues? With the additional data collected, develop three (3) sets of analysis to determine whether there is a relationship between the additional data collected and the total # of issues reported. (I.E. is there a relationship between Total # of Meals Served and # of Patients with Issues).  For each analysis you must include a scatter diagram (with trend line), a regression analysis (using Excel Data Analysis tool) and a conclusion on whether or not there is a relationship.  Does the scatter diagram and regression analysis depict a relationship between the variables?  Why or why not?
2. Regression Analysis: Perform one additional regression analysis between the # of Patients with Issues and a calculation, using the additional data, which determines the amount of work for kitchen staff per day.  The analysis must include a scatter diagram (with trend line), a regression analysis (using Excel Data Analysis tool) and a conclusion on whether or not there is a relationship.  Does you scatter diagram and regression analysis depict a relationship between the variables?  Why or why not?
3. Pareto:Develop a Pareto chart for the type of issues that have occurred.
4. Fishbone:For the issue with the highest frequency (from Q3), develop anIshikawa (fishbone) diagram to generate ideas as to why this issue may occur.  Your diagram must contain at least two (2) ideas per cause category.
5. Recommendation:Based on your analysis developed from Questions 1, 2, 3 and4, develop two (2) specific recommendations for the improvement team.

 

 

Assignment Deliverables

1. All work, including your analysis and recommendations must be developed in Excel and must be contained in the same file. Use a separate worksheet for each question.
2. The assignment will be graded on accuracy of calculations and formulas, use of correct data for charts, use of charting best practices (Headings, labels, etc.), accuracy and specificity of observations (Q1) and clear recommendations that are derived from your analysis.

The assignment is to be completed individually.  All assignments must be submitted to the E-Conestoga drop-box prior to the start of class.  Any late assignments will receive a grade of zero (0).  This assignment is worth 15% of your final grade in the course.

You need to submit a printed copy of your assignment.

The assignment is due the beginning of class Friday February 8, 2019.