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BUSSB 2012 Research Methods, Data Analytics and Project Planning  Middle East College
Learning Outcome 1. Design a project plan for business improvement.
Learning Outcome 2. Distinguish the different distributions in statistics.
Learning Outcome 3. Present research findings and opportunities for improvement.
Introduction
The report contains the statistics analysis of a logistics inc. company. To do the analysis the dataset of the company is cleaned using suitable criteria then, various insights are developed using the cleaned data set of the company. The frequency table of different brands that the company sells is made and a bar chart is plotted using the frequency table of the brand. The report also shows summary statistics of number of pieces per year and classified frequency table for weight per article. The report demonstrates the analysis of the dataset with help of box plots and bar chart.ABC analysis of the inventory is also carried out to gain insights into different articles that which are in high demand and which do not have ant demand. Then, a regression model is developed to determine the emergency orders which have three variables as order picking, packaging and shipping. To recognize the relationship between the various variables scatter plots are also plotted
Literature Review
Kim, J. (2012) says regression is the statistical technique of predicting value of dependent variable from two or more independent variables. There can be two types of regression linear and multiple regression. When there is one dependent and one independent variable then it is called linear regression and when there are more than one independent variable predicting a dependent variable then it is known as multiple regression. Meissner, G. (2015) gave a statement on correlation that correlation tells the degree of relationship between two variables. How much two variables are dependent on each other is indicated by correlation coefficient. If the value of correlation coefficient is near to 1 then, it indicates high degree of correlation between two variables. Runnenburg, J. (1978). Gives his view point on the descriptive statistics saying to summarise quantitative data we can use various measures such as mean , median , mode, standard deviation , range, quartiles and many more. Mean give the average value of the quantitative data. Median gives the central value of the quantitative data and mode gives the value that occurs maximum time in the given data. Together these measures are known as descriptive statistics. Brereton, R. (2014). says normal distribution is a distribution which shows symmetric distribution of a variable and in normal distribution all the three measures mean, median and mode are equal. Scott, D. (2009) tells histogram as a bar chart that shows frequency distribution of a variable in graphical form.
Project Plan
We have made the Gantt chart in excel by scheduling various tasks of project with their start date and the duration. The yaxis of the chart shows the set of tasks needed to complete the project and the xaxis show the dates on which tasks are started and completed.
Gantt Chart Showing the Project Plan
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Part 1: Data analysis and cleaning of data sets
a) Clean the data
b) Make a frequency table of the brand
c) Make a bar chart for the absolute frequency of the brand.
d) Make a classified frequency table of the weight per article.
e) Make a histogram of the absolute frequency of the number of weight per article.
f) Determine mode, median, and average of the number of sold pieces per year.
g) Determine the range of the number of pieces per pallet.
h) Make a box plot of the number of sold pieces per year and the weight per article (use the unfiltered data set here). Determine the extremes.
i) Check whether the number of replenishment lines per day is normally distribute
Task 1
This part of the task requires analysis of the dataset of the logistic inc. which is collected from different sources. Dataset is contaminated as it contains a lot of irrelevant values. First , we need to clean the dataset using a definite criteria. We have filtered the data on the basis of following criteria:
a) The weight per piece can not be zero but dataset contains a lot of data entries in which weight per piece is zero so, we have filtered those entries from the dataset
b) The number of pieces sold should be a integer. It cannot be in decimal. So, we have filtered all the entries that contain a decimal value for the number of pieces sold.
c) Also the number of pieces cannot be a negative number. It should be a positive number. So, we have filtered all those entries which contain negative value for number of pieces sold.
d) The number of pieces sold column can not contain a entry in the form of date format. So, in the filtered dataset we have removed all the entries which contain the value in the format of date.
e) The dataset also contains one outlier in the column number of pieces sold which is much greater than other values . So, we have removed that value so that our analysis do not get ruined due to that outlier.
We had the sample size of 150 for our initial dataset but after filtering the data on the basis of above criteria we are left with only sample size of 122.
We have the following dataset after filtering. So, we have filtered 81.33% of the dataset.
Article number 
Brand 
Weight per piece (kg) 
# of sold pieces per year 
Number of replenishment lines per day 
# of pieces per pallet 
3B4839015A_V 
Ford 
15.7 
1173 
3 
30 
06A115561_V 
Fiat 
8.2 
1034 
1 
60 
6X1837013A_V 
Fiat 
15.6 
1022 
3 
30 
3B1857521_V 
Ford 
16 
410 
3 
30 
048109243A_V 
Ford 
26.7 
208 
2 
20 
3B0953235B 01C_V 
Kia 
26.7 
134 
1 
20 
2D0837249C_V 
Audi 
6.8 
92 
4 
70 
8A0807346C 01C_V 
Kia 
14.4 
132 
1 
30 
1J1959565E 01C_V 
Audi 
8.7 
178 
4 
60 
4B0121101E_V 
Audi 
12.4 
68 
3 
40 
N 10112603_V 
Kia 
4.5 
2026 
1 
110 
N 0138493_V 
Audi 
14.3 
6136 
2 
30 
191853733A_V 
Audi 
15.9 
3672 
5 
30 
357853586D_V 
Ford 
15.4 
3196 
2 
30 
N 90634901_V 
Kia 
15.1 
4653 
2 
30 
811807577C_V 
Audi 
13.8 
2044 
1 
40 
028129589B_V 
Audi 
0.8 
2833 
5 
630 
701867299 1YX_V 
Audi 
7 
5194 
4 
70 
N 90085001_V 
Ford 
9 
3309 
1 
60 
028103533_V 
Audi 
1.8 
3548 
2 
280 
1H0853586_V 
Audi 
9.7 
4519 
3 
50 
028103532A_V 
Kia 
23.6 
3332 
2 
200 
N 90074401_V 
Audi 
6 
1092 
0 
80 
161867299 01C_V 
Audi 
4.9 
1830 
1 
100 
N 0147392_V 
Kia 
14.5 
1119 
1 
30 
N 0241222_V 
Audi 
0.9 
982 
4 
560 
N 0138494_V 
Kia 
6.7 
1010 
1 
70 
893823740_V 
Kia 
15 
1509 
4 
30 
N 90335004_V 
Audi 
12 
2656 
4 
40 
N 0177512_V 
Audi 
6 
2180 
2 
80 
893919040A_V 
Kia 
12.9 
1588 
1 
40 
N 90775001_V 
Audi 
7.9 
888 
4 
60 
8D0805960_V 
Audi 
1.9 
931 
1 
260 
028010227E_V 
Fiat 
11 
1177 
2 
50 
N 90200201_V 
Kia 
12.3 
1483 
0 
40 
N 0438541_V 
Audi 
2 
3493 
2 
250 
N 0177192_V 
Fiat 
5.1 
860 
3 
100 
357837242_V 
Audi 
16.7 
406 
3 
30 
N 0177185_V 
Audi 
9.9 
2775 
1 
50 
3B0868243_V 
Ford 
12.3 
1568 
3 
40 
N 10209005_V 
Ford 
12.6 
838 
2 
40 
701853585_V 
Audi 
14.7 
1164 
4 
30 
N 10083401_V 
Audi 
1 
5550 
4 
500 
N 10261503_V 
Fiat 
23.9 
435 
2 
20 
N 90206103_V 
Ford 
4.4 
2403 
2 
110 
N 90329205_V 
Ford 
0.4 
475 
2 
1250 
D 00950025_V 
Audi 
30.7 
3788 
3 
20 
N 10209603_V 
Audi 
6.9 
1939 
5 
70 
N 01781364_V 
Audi 
23.8 
372 
2 
20 
N 90355404_V 
Audi 
27.5 
250 
2 
20 
N 0102478_V 
Audi 
14.2 
420 
2 
40 
801867299 01C_V 
Audi 
14.4 
553 
5 
30 
037121687_V 
Kia 
362 
726 
2 
0 
101000036AC_V 
Audi 
7.6 
1344 
0 
70 
N 90833801_V 
Audi 
4.2 
777 
1 
120 
N 10013401_V 
Audi 
2.2 
1271 
1 
230 
N 90348701_V 
Audi 
10.7 
384 
3 
50 
N 0177612_V 
Audi 
6.1 
1752 
4 
80 
N 10101001_V 
Ford 
7.7 
288 
1 
60 
059121119_V 
Audi 
7.1 
863 
4 
70 
N 90288901_V 
Fiat 
4.8 
1037 
2 
100 
053103663_V 
Audi 
3.3 
904 
2 
150 
N 90787802_V 
Audi 
5.6 
477 
4 
90 
034133557E_V 
Audi 
5 
565 
4 
100 
155853585_V 
Ford 
12.7 
739 
1 
40 
N 10081101_V 
Audi 
12 
436 
1 
40 
N 90592702_V 
Audi 
2 
530 
25 
250 
N 90316802_V 
Fiat 
24.1 
1913 
3 
20 
N 0177172_V 
Audi 
11.1 
2375 
1 
50 
030905423A_V 
Audi 
11.9 
918 
4 
40 
811807447_V 
Fiat 
5.2 
823 
4 
100 
N 10256401_V 
Fiat 
3.9 
641 
1 
130 
357501641B_V 
Audi 
0.9 
474 
0 
560 
443845631A_V 
Audi 
155 
1329 
2 
1 
036035255J_V 
Kia 
7.2 
1506 
2 
70 
7M0867299K_V 
Audi 
1.3 
649 
2 
380 
AKL43401925_V 
Audi 
15 
75 
2 
30 
N 90353501_V 
Audi 
14.2 
504 
1 
40 
N 0438121_V 
Audi 
23.8 
1593 
1 
20 
N 90329104_V 
Audi 
15.6 
550 
1 
30 
8A0407181_V 
Ford 
16.2 
1634 
4 
30 
077010138B_V 
Audi 
11.8 
834 
4 
40 
445827589_V 
Fiat 
15.2 
322 
1 
30 
321611939E_V 
Ford 
7.9 
690 
2 
60 
051905207_V 
Fiat 
11.8 
332 
0 
40 
1H0845125_V 
Audi 
9.9 
418 
4 

030103533C_V 
Audi 
9.8 
534 
0 
50 
191863447_V 
Ford 
13.5 
299 
2 
40 
N 10286102_V 
Audi 
9.4 
512 
2 
50 
893407237_V 
Ford 
7.5 
736 
4 
70 
431253149A_V 
Audi 
14.7 
349 
1 
30 
113853585C_V 
Audi 
22.4 
789 
2 
20 
191867199 E91_V 
Ford 
14.7 
366 
2 
30 
052905225C_V 
Ford 
2.1 
642 
2 
240 
6X0955425B_V 
Kia 
8.5 
959 
4 
60 
191201511A_V 
Audi 
0.9 
208 
3 
560 
191881213 909_V 
Fiat 
2.9 
447 
2 
170 
101000033AA_V 
Audi 
3.8 
1967 
2 
130 
311881247_V 
Audi 
14.1 
834 
0 
40 
357867646_V 
Audi 
4.4 
278 
2 
110 
893253147F_V 
Audi 
9.4 
400 
1 
50 
3B0839723_V 
Fiat 
16.8 
1432 
4 
30 
1H0937530_V 
Audi 
14.5 
291 
5 
30 
N 0177622_V 
Audi 
9.9 
338 
1 
50 
N 0147263_V 
Audi 
6.5 
253 
0 
80 
3A0611053_V 
Ford 
20.7 
225 
2 
20 
N 10229901_V 
Kia 
17.1 
197 
2 
30 
N 90577101_V 
Kia 
16.7 
288 
2 
30 
3A0853600 EPG_V 
Fiat 
16.4 
727 
1 
30 
191853615A_V 
Audi 
12.4 
1210 
2 
40 
893947565A_V 
Audi 
15.9 
463 
2 
30 
1H0819055B 01C_V 
Fiat 
7.3 
319 
4 
70 
N 90426401_V 
Audi 
12.6 
707 
0 
40 
038010241_V 
Audi 
9.9 
328 
1 
50 
ZA 000412 ISO_V 
Fiat 
3.7 
868 
2 
140 
1H0611053A_V 
Fiat 
16.3 
335 
4 
30 
1H0837237D_V 
Ford 
11.7 
542 
2 
40 
1H0837229B_V 
Fiat 
14.9 
245 
2 
30 
N 90821401_V 
Audi 
7.8 
897 
3 
60 
N 90136802_V 
Audi 
8.4 
204 
0 
60 
8D0845237_V 
Kia 
4.8 
221 
2 
100 
N 10299501_V 
Fiat 
24 
194 
2 
20 
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To make the frequency table of brand we have used pivot for brand column of the table. We have used count function in the pivot table to make the frequency table. After calculating the frequency of each brand we have calculated cumulative frequency by adding the values contained in the frequency table .Then, we have calculated the relative frequency by dividing the respective of each brand with the total frequency.
We have got the following table:
Brand

Frequency

Commulative Frequency

Relative Frequency

Commulative Relative Frequency

Audi

68

68

56%

56%

Fiat

19

87

16%

72%

Ford

19

106

16%

87%

Kia

16

122

13%

100%

Grand Total

122




To make the classified frequency table for weight per article we have used the columns brand and the weight per article. In the pivot table we have used the sum function to calculate weight for each brand.
So, we have got the following table:
Brand

Sum of Weight per piece (kg)

Audi

815.7

Fiat

231.1

Ford

227.2

Kia

562

Now, we have used mean, median and mode functions of excel to determine the mean, median and mode of the number of pieces sold per year and we have got following results.
Summary

Number of pieces sold per year

Mean

1179.70492

Median

783

Mode

208

To determine the range of number of pieces per pallet we have subtracted the highest value of number of pieces per pallet and lowest value of number of pieces per pallet. We have got the underlying result.
To plot the box plot for number of pieces sold per year and weight per article we have found the maximum value of number of pieces sold per year and weight per article, minimum of number of pieces sold per year and weight per article and median of number of pieces sold and weight per article and first and third quartile of number of pieces sold per year and weight per article. Then , we have plotted bar chart for both the columns and after that we stacked the columns and plotted error lines to get the box plots.
Box Plot for number of sold pieces per year

Max

6136


Q1

388


Median

783


Q3

1500.25


Min

68


Box Plot for number of pieces sold per year
Weight Per article
Max

362

Q1

6.025

Median

11.05

Q3

15

Min

0.4

Box plot for weight per article
We have plotted histogram to know whether number of replenishment per day is normally distributed or not. From the shapes of the histogram we can conclude that this variable is not normally distributed
Histogram of number of replenishment lines per day
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Part 2: Order profiles and walking distances in a warehouse
a) Make a graph showing the ABCanalysis for the articles of Logistat Inc.
b) determine the number and percentage of SKUs, the number, and percentage of order lines and the number and percentage of used storage locations
c) What is the average number of order lines per order?
Task 2
We have found the number of order lines by aggregating the data of article and the sales file , then , we have calculated number the percentage of SKU as well as number and percentage of order lines. Then, we have assigned the suitable group to each article and calculated number of locations and walking distance in the warehouse. We have also plotted graphs to show relationship between order lines and the SKU.
Graph showing Commulative order lines and Commulative SKU
By classifying each article in different groups we have found the number and percentage of SKU and number and percentage of Order lines.
Group

Sum of SKU

Sum of Percentage SKU

Sum of Order lines

Sum of Percentage Order lines

A

36.7

0.139490688

203

0.203

AA

176.7

0.671607754

603

0.603

B

49.7

0.188901558

193

0.193

Grand Total

263.1

1

999

0.999

Table showing SKU AND order lines per group
Group 
Average of Locations 
A 
2 
AA 
97 
B 
1 
C 
0 
Table showing Average number of location per group
Part 3: Correlation, Regression and Probability Distributions.
a) Discuss relationship (Correlation) between each variable (Order Picking, Packaging and Shipping) with separate tables, graphs and scatterplots.
b) Generate 3 valid Regression Equation for predicting any Emergency Order Variables. Discuss the result with line fit plots, normal probability plot and key outcomes.
c) Predict any 5 set of data using the generated regression equations.
Task 3
We have found the scatter plots using 2 variables at a time and then, we have calculated 3 regression equations using the regression function of data analysis tool of excel. The scatter plots between the various plots shows that there is linear relationship between the variables taken two at a time. We have also found the correlation coefficient between the variables which comes out to be 1 for all variables. There is strong degree of correlation between the variables.

Orderpicking

Packaging

Shipping

Orderpicking

1.00



Packaging

1.00

1.00


Shipping

1.00

1.00

1.00

Table showing correlation coefficient between different variables
Graph showing correlation between the Order picking and packaging
Graph showing Correlation between shipping and order picking
Graph showing Correlation between shipping and order picking
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Regression equation
We have found three regression for emergency orders by using 2 variables at a time using regression function of XLSTAT.
First Regression Equation
Order picking=12+0*Packing+10*shipping
Figure showing Line fit plot between shipping and order picking
Figure showing line fit plot between Packaging and order picking
Figure showing normal probability plot for order picking
Second Regression Equation
Packaging=2.2+0*Shipping+0.1*order picking
Graph showing Line fit plot between shipping and packaging
Graph showing Line fit Plot between order picking and packaging
Graph showing normal probability plot for packaging
Third Regression Equation
Shipping=1.2+0*Packaging+0.1*order picking
Graph showing line fit plot between shipping and order picking
Graph showing line fit plot between shipping and packaging
Graph showing normal probability plot for shipping
Predicting value of shipping using equation Shipping=1.2+0*Packaging+0.1*order picking
Packaging (min)

Order picking (min)

Shipping

3.81

16.08

2.81

4.49

22.94

3.49

3.79

15.91

2.79

4.32

21.19

3.32

3.57

13.68

2.57

Table showing predicted values using equation
Conclusion
We have analyzed the data of the logistics inc. with the help of various statistical functions and techniques such as correlation, regression and scatter plots, box plots, bar charts , histogram and developed a regression model for the company logistics inc. The regression model can provide the value of third variable if 2 variables are known. We have used multiple regression model because we have two independent variables and one dependent variable. We have also found the correlation coefficient between the different variables which comes out to be 1 which shows there is strong correlation between all the variables of the emergency order. Scatter plot also show linear relationship between the variables taken two at a time.
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