Process Evaluation
Process evaluation is a crucial part of project management which basically emphases on the project’s implementation process and tries to evaluate how successfully the project followed the designed strategy which is planned in the logic model. The main steps involved in the process evaluation are-Description of the intervention, defining the acceptable features of delivery, formulation of related questions, determination of project methodology, planning of the required resources and finalisation of the project management plan..It can be performed by different methods like using the lean concept that provides the suitable ways to eliminate the waste and helps in the improvement of the process or by the use of SPC or Six Sigma method to decrease the defects or variances generated in the process.
Executive summary:
In this competency, I am taking the example of a drug manufacturing company which is producing the drugs against bacterial infections. Here, I am discussing the process strategy of his company and perform its process evaluation by Six Sigma method to reduce defects or variances in the process. Six sigma methods are selected here because it improves the organization’s performance by its statistical, data-driven and problem-solving method.
Let’s begin with the process strategy of the pharmaceutical company- The Company X is producing antibiotics using the same raw material and machines for 30 days but the quantity of drugs produced each day vary from each other as shown in the table below, which is turn affects the company’s productivity and lowers the yield.
Table 1: Drug production by Company X before optimization by six sigma method
Days
Number of drugs produced
1
117
2
123
3
119
4
94
5
125
6
120
7
121
8
127
9
146
10
120
11
113
12
121
13
143
14
118
15
121
16
120
17
92
18
128
19
114
20
95
21
120
22
150
23
121
24
112
25
149
26
144
27
120
28
142
29
110
30
139
From this table, this is clear that even after utilizing the same material there is significant difference in the number of drugs produced per day i.e., there are faults in the process strategy due to which the production and yield of drugs varies.
To improve the per day productivity and reduction of wastage of raw material, we uses here the six sigma methodology . The Six Sigma method uses a defined sequence of steps – DMAIC steps which are Define, Measure, Analyse, Improve, and Control and improves the process strategy. This method statistically analyse the existing process steps and determines exactly which process elements need the improvement. Later, new alternatives were developed for improvement, select and implemented as shown below:
· In the first step define- the problem of six sigma method here , the problem was defined as the non-uniform production of drugs each day.
· In the second step measure- by performing the statistical analysis the variation among each days and average productivity was assessed as shown below with the help of statistical calculation and control chart.
Figure1: Control chart for drug production before applying the six sigma method
The curve here is very i
egular which means there is a significant difference in drug production at each day while using the same raw material and machines. And by statistical evaluation we find that the average or mean production is 122.8 drugs per day with the lower limit of 107.67, upper limit of XXXXXXXXXXand very high standard deviation of XXXXXXXXXXThis shows that lots of raw material and manpower is wasting on those days when less number of drugs are produced.
· In the third step analyse- the different parameters used for the drug production each day like machine temperature, rotation, mixing characters, input of raw materials, process holding time and operational characteristics were assessed and check for related variations each day.
· In the fourth step improve - when the parameters that are causing the variations or the operational e
ors identified then different strategies to improve them were designed like using same operational parameters and material inputs each day etc.
· In the fifth step – These new parameters were implemented and the faults were co
ected to achieve the uniform production.
The drug production data of Company X by implementing the Six sigma methodology is as follows:
Table 2: Drug production by Company X after optimization by six sigma method
Days
Data
1
135
2
136
3
135
4
134
5
135
6
136
7
134
8
136
9
134
10
135
11
135
12
135
13
134
14
134
15
133
16
135
17
134
18
135
19
134
20
134
21
135
22
135
23
134
24
135
25
134
26
135
27
134
28
135
29
135
30
134
From this table, this is clear that after optimization of six sigma method there is uniformity in the number of drugs produced per day and the wastage is reduced. By statistical evaluation we find that the average or mean production is XXXXXXXXXXdrugs per day with the lower limit of 133.9, upper limit of 139.5 and very low standard deviation of 0.7.
The process evaluation parameters are calculated as shown below:
1. Coefficient of variation = STD/mean = XXXXXXXXXX
2. Process capability ration, Cp = UCL-LCL/6*STD = XXXXXXXXXX = XXXXXXXXXX
3. Process capability index, CPk = Minimum of (UCL-mean)/3*STD, (mean-LCL)/3*STD) =0.718 =0.33, 0.718, 0.33
4. Minimum of (0.33, 0.33) = 0.33 here, cp is equal to Cpk means process is centered at midpoint of the data and same value of Cp and Cpk also denotes the same tolerance range and process range as shown in the control chart
Figure2: Control chart for drug production after applying the six sigma method
2. Summary of the Evaluation of Control Chart and Process metrics based on SPC
In control chart most points are near the average and few points are near the control limits or beyond, it shows the process is centred in the middle and optimized.
3. Summary of evaluation and explanation about whether the process would benefit from the use of Six Sigma methods
Results shows that use of six sigma can improved the process and
ing most of the points near the average and therefore provides the uniform production of each day, increases the yield and minimize the waste of raw materials and power consumption.
Description of the SPC project and recommendations for improvement
The statistical process control or SPC tools and its statistical procedures when applied in this process helps us in identifying the e
ors incorporated during the operational parameters and provides the solution of that which when implemented on the real time basis our production methodology was improved and we achieve high yield and uniform productivity. Therefore, six sigma methods provide the solutions for potential production and the SPC tools are highly recommendations for project improvement.
Control Chart
Data 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 26 27 28 29 30 117 123 119 94 125 120 121 127 146 120 113 121 143 118 121 120 92 128 114 95 120 150 121 112 149 144 120 142 110 139
Sheet1
Days Data
1 117
2 123 calculation:
3 119 Coefficient of variation = STD/mean XXXXXXXXXX
4 94 Process capability ration, Cp = UCL-LCL/6*STD XXXXXXXXXX XXXXXXXXXX
5 125
6 120
7 121
8 127
9 146
10 120
11 113
12 121
13 143
14 118
15 121
16 120
17 92
18 128
19 114
20 95
21 120
22 150
23 121
24 112
25 149
26 144
27 120
28 142
29 110
30 139
122.8 Average or mean (B2:B31)
XXXXXXXXXX Stdev S (B2:B31)
XXXXXXXXXX 3*STD
XXXXXXXXXX LCL
XXXXXXXXXX UCL
XXXXXXXXXX 6*STD
Data
Mean
LCL
UCL
Control Chart
Data 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 26 27 28 29 30 117 123 119 94 125 120 121 127 146 120 113 121 143 118 121 120 92 128 114 95 120 150 121 112 149 144 120 142 110 139
Sheet2
Days Data
1 135
2 136 calculation:
3 135 Coefficient of variation = STD/mean XXXXXXXXXX
4 134 Process capability ration, Cp = UCL-LCL/6*STD XXXXXXXXXX XXXXXXXXXX
5 135 Process capability index, CPk = Minimum of (UCL-mean)/3*STD, (mean-LCL)/3*STD) XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX
6 136 minimum of (0.33, 0.33) = 0.33
7 134 here cp is equal to CPk means process is centered at midpoint of the data.
8 136
9 134 same value of Cp and Cpk also denotes the same tolerance range and process range.
10 135
11 135
12 135
13 134
14 134 In control chart most points are near the average
15 133 Few points are near the contol limits or beyond
16 135
17 134
18 135 It shows process is centered in the middle
19 134
20 134 it shows that use of six sigma can imporoved the process and
ing most of the points near the average.
21 135
22 135
23 134
24 135
25 134
26 135
27 134
28 135
29 135
30 134
XXXXXXXXXX Average or mean (B2:B31)
XXXXXXXXXX Stdev S (B2:B31)
XXXXXXXXXX 3*STD
XXXXXXXXXX LCL
XXXXXXXXXX UCL
XXXXXXXXXX 6*STD
Data
Mean
LCL
UCL
Control chart Drug Production Vs. Days
Data 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 26 27 28 29 30 135 136 135 134 135 136 134 136 134 135 135 135 134 134 133 135 134 135 134 134 135 135 134 135 134 135 134 135 135 134