Business Computing 

Contents

Introduction..

Analysis & Discussion - Descriptive Analysis of The Data.

Analysis & Discussion - Predictive Analysis of The Data.

Analysis & Discussion - Prescriptive Analysis of The Data.

Conclusion..

Recommendations..

Introduction to Business Computing

This report comprises of descriptive, predictive and prescriptive analysis of sales data for the online store owned by Alex and Ash Sparks.

The average gross profit, sales trend & variance gross profit etc. gives the overview of the data.

Different manufacturers and ranges of taps were compared to gain better control of the sales data

Prescriptive analysis is used to make recommendation to make strategic decisions

Analysis & Discussion - Descriptive Analysis of The Data

The below chart shows the sales trend (quarterly basis) for the online store Taps To Go

The below table gives the comparison between the average gross profit (year on year) for varying delivery charges (based upon the state to which goods are getting delivered) and fixed delivery charges

Row Labels

Average of Gross Profit

Average of Gross Profit - Fixed delivery

2017

39.1558023

38.20202769

2018

39.2684982

38.31761972

2019

39.08891934

38.12639114

Grand Total

39.17183309

38.21615715

The below table gives the comparison between the variance of gross profit (year on year) for varying delivery charges (based upon the state to which goods are getting delivered) and fixed delivery charges

Row Labels

Var of Gross Profit

Var of Gross Profit - Fixed delivery

2017

409.8037717

400.0880571

2018

416.9291757

405.9415031

2019

406.2704525

395.7329483

Grand Total

411.046197

400.6319065

The below table gives the comparison between the average gross profit for different ranges,

varying delivery charges (based upon the state to which goods are getting delivered) and fixed delivery charges

Row Labels

Average of Gross Profit

Average of Gross Profit - Fixed delivery

Average of $ discount

Basics

19.08729185

18.09989837

11.34984755

Luxury

98.04809626

97.0845897

37.09299532

Superior

44.50874289

43.54008375

25.14458755

Value

34.84594001

33.90410232

25.94070459

Grand Total

39.17183309

38.21615715

24.32603904

The below table gives the values for gross profit (year on year) for different manufacturers

Row Labels

Average of Gross Profit

2017

 

Vitra

98.00783049

Frederic

45.3990172

Villeroy and Boch

44.18566499

Dorf

38.36157827

Novelli

35.43735205

Caroma

27.52765266

Estilo Wels

19.12277693

2018

 

Vitra

98.06943266

Frederic

45.40715328

Villeroy and Boch

44.26101431

Dorf

38.38842245

Novelli

35.37458178

Caroma

27.47536056

Estilo Wels

19.0700651

2019

 

Vitra

98.06694392

Frederic

45.41213759

Villeroy and Boch

44.2198496

Dorf

38.36522723

Novelli

35.43079347

Caroma

27.55967491

Estilo Wels

19.06911539

Grand Total

39.17183309

Analysis & Discussion - Predictive Analysis of The Data

Multiple regression analysis is used here to develop regression model with Dependent Variable Gross Profit (y) and Independent Variables Recommended Retails Price (RRP), $ discount & Delivery

Regression Statistics

             

Multiple R

0.999554773

             

R Square

0.999109744

             

Adjusted R Square

0.999109726

             

Standard Error

0.604932733

             

Observations

153675

             

ANOVA

               

 

df

SS

MS

F

Significance F

     

Regression

3

63110878.35

21036959

57486888

0

     

Residual

153671

56234.92079

0.365944

         

Total

153674

63167113.28

 

         

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.063374636

0.007318266

8.659788

4.77E-18

0.049030985

0.077718287

0.049030985

0.077718287

RRP

0.107709836

1.28189E-05

8402.452

0

0.107684711

0.10773496

0.107684711

0.10773496

$ discount

0.198204997

0.000260963

759.5144

0

0.197693515

0.198716479

0.197693515

0.198716479

Delivery

1.000003076

0.000477778

2093.031

0

0.999066642

1.00093951

0.999066642

1.00093951

The regression model is given by:

Gross Profit (Y)= 0.063375 + 0.10771xRRP + 0.198205x$ discount + 1.000003xDelivery

β0 = 0.063375, β1 = 0.10771 β2 = 0.198205 & β3 = 1.000003

Analysis & Discussion - Prescriptive Analysis of The Data

Prescriptive analysis is used to know the right levers to push and pull to increase sales, profitability and customer satisfaction

In synchronization with the Descriptive and predictive analysis the objective

Gross Profit (Y)= 0.063375 + 0.10771xRRP + 0.198205x$ discount + 1.000003xDelivery

We need to maximize the objective function Z=Gross Profit (Y)

Subject to the constraints

Discount =10.2

Conclusion to Business Computing

The above sales trend shows that the sales are fluctuating over the three years, the lowest sales count was 12536 in Quarter 4 (2017) and the maximum sales count was 13344 in Quarter 3 (2018).

Row Labels

Average of Gross Profit

Average of Gross Profit - Fixed delivery

2017

39.1558023

38.20202769

2018

39.2684982

38.31761972

2019

39.08891934

38.12639114

Grand Total

39.17183309

38.21615715

The average gross profit for varying delivery charges (based upon the state to which goods are getting delivered) is greater than the average gross profit for fixed delivery charges for all three years

Hence keeping the standard delivery charges is not good for gross profit.

Row Labels

Var of Gross Profit

Var of Gross Profit - Fixed delivery

2017

409.8037717

400.0880571

2018

416.9291757

405.9415031

2019

406.2704525

395.7329483

Grand Total

411.046197

400.6319065

The variance of gross profit (year on year) for varying delivery charges (based upon the state to which goods are getting delivered) is greater than the variance of gross profit for fixed delivery charges. This implies standard delivery charges will reduce the high variation in gross profit

The comparison between the average gross profit for different ranges,

varying delivery charges (based upon the state to which goods are getting delivered) and fixed delivery charges

Row Labels

Average of Gross Profit

Average of Gross Profit - Fixed delivery

Average of $ discount

Basics

19.08729185

18.09989837

11.34984755

Luxury

98.04809626

97.0845897

37.09299532

Superior

44.50874289

43.54008375

25.14458755

Value

34.84594001

33.90410232

25.94070459

Grand Total

39.17183309

38.21615715

24.32603904

The below table gives the values for gross profit (year on year) for different manufacturers

Row Labels

Average of Gross Profit

2017

 

Vitra

98.00783049

Frederic

45.3990172

Villeroy and Boch

44.18566499

Dorf

38.36157827

Novelli

35.43735205

Caroma

27.52765266

Estilo Wels

19.12277693

2018

 

Vitra

98.06943266

Frederic

45.40715328

Villeroy and Boch

44.26101431

Dorf

38.38842245

Novelli

35.37458178

Caroma

27.47536056

Estilo Wels

19.0700651

2019

 

Vitra

98.06694392

Frederic

45.41213759

Villeroy and Boch

44.2198496

Dorf

38.36522723

Novelli

35.43079347

Caroma

27.55967491

Estilo Wels

19.06911539

Grand Total

39.17183309

This shows the average gross profit for manufacturer Vitra is the maximum and for the manufacturer Estilo Wels is the lowest

From the regression table below R2 = 0.999109744, this means that 99.91% of the variation in Y is explained by the regressors RRP, $ discount & Delivery charges

Regression Statistics

Multiple R

0.999554773

R Square

0.999109744

Adjusted R Square

0.999109726

Standard Error

0.604932733

Observations

153675

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0.063374636

0.007318266

8.659788

4.77E-18

0.049030985

0.077718287

0.049030985

0.077718287

RRP

0.107709836

1.28189E-05

8402.452

0

0.107684711

0.10773496

0.107684711

0.10773496

$ discount

0.198204997

0.000260963

759.5144

0

0.197693515

0.198716479

0.197693515

0.198716479

Delivery

1.000003076

0.000477778

2093.031

0

0.999066642

1.00093951

0.999066642

1.00093951

The regression model is given by:

Gross Profit (Y)= 0.063375 + 0.10771xRRP + 0.198205x$ discount + 1.000003xDelivery

β0 = 0.063375, β1 = 0.10771 β2 = 0.198205 & β3 = 1.000003

The above can be used for predicting future gross profit, given the regressors’ values

Recommendations for Business Computing

Considering the Regression model

Gross Profit (Y)= 0.063375 + 0.10771xRRP + 0.198205x$ discount + 1.000003xDelivery

  • From β3 It is visible that increasing/decreasing delivery charges will have the maximum impact on gross profit (Y)
  • Increasing Discount percentage can reduce gross profit
  • Buying goods from manufacturers like Vitra & Dorf will be more profitable

 Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Management Assignment Help

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