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BUSS-B 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 y-axis of the chart shows the set of tasks needed to complete the project and the x-axis 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

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 ABC-analysis 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?

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.

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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