Managing information systems

1. PURPOSE

The purpose of this report is to study and implement the real use of statistical data in an organization and provide judgment of some of the main issues that take place during work.

The utilization of Minitab software for interpreting the raw data within the organization and accomplishment of the improvements and decision making strategies was helpful in minimizing the occurrence of such issues.

2. Introduction

The report is based on the data from a telecommunication organization who was working for a turnkey project for a telecommunication operator. The organization is medium sized and having resources less than 200. The organization scope was to carry out site acquisition, site construction, site power energization and installation and commissioning of the telecom equipment.

The top management was concerned about the delays in site construction of this project. Due to the delays, the organization had to suffer huge losses in the form of penalties per site as declared in the contract with customer. The customer was also dissatisfied on the immense delays in the project. The data was collected and a strategy was adopted to trace the root causes and identify the areas of improvements. The statistical analysis was done to support the decisions to be made for changing departmental and organizational policies and procedures (D & J 2008).

3. Software used

The Minitab15 Software has been used to prepare this report data and graphs. Minitab is a powerful statistical software package that provides a wide range of basic and advanced data analysis capabilities.

4. DEFINITIONS

The following definitions are given below that have been used in the report and analyzed through Minitab:

  • Mean - it is also known as average. It is obtained by dividing the sum of observed values by the number of observations.
  • Median - it Is the middle value of a set of data containing an odd number of values
  • Mode - it is the value which occurs most frequently in a data set
  • Standard deviation - it gives an idea of how close the entire set of data is to average value
  • Linear regression - it is the approach of modeling the relationship between one or more variables denotedyand one or morevariables denoted as x
  • Time Series Plot - it is a sequence of data points, measured typically at successive times spaced at uniform time intervals (Taylor & Cihon 2004)
  • Trend Analysis - it refers to the concept of collecting information and attempting to spot a pattern
  • Pareto Chart - it is a type of chart which contains both bars and a line graph that displays the values in descending order as bars and the cumulative totals of each category, left to right, as a line graph
  • Scatter Plot - it is a type of mathematical diagram using cartesian coordinates to display values for two variables for a set of data (Degroot & Schervish 2002)
  • Histogram - it is a graphical display of tabulated frequencies, shown as bars
  • Line Plot - it shows data on a number line withxor other marks to show frequency
  • Bar Chart - it is a chart with rectangular bars with lengths proportional to the values that they represent
  • Pie Chart - it is a circular chart divided into sectors, illustrating percents
  • Probability Distribution Plot - it is a graphical technique for assessing whether or not a data set is approximately normally distributed

5. DATA COLLECTION

A sample of 100 sites constructed in 2009 has been taken for analysis on random sampling basis.

Following are the sitewise deficiencies recorded by customer in SDR (site deficiency report) and the site delay time is also given (Longnecker 2008).

Other items may be considered as noise as they either have negligible effect on the output or are inherent to the process and are not feasible to remove. Based on the repetition of deficiency groups' time taken by each, the total time taken by each deficiency group is calculated in the following manner:

Deficiency

Repeated/100 sites

Time required for rectification (days)

Total time taken (days)

Cable labeling

11

2

22

Documentation

29

3

87

A/C (Air conditioning) system

6

4

24

Commercial power availability

25

10

250

Fence installation

15

9

135

Fire extinguisher

4

2

8

Site folder

12

1

12

Table 1.1

6. HISTOGRAM & PIE CHART

The Histogram chart for the above table 1.1 is as follows:

Explanation: The histogram chart & Pie chart shows that highest frequency of occurrence of site delays are documentation, commercial power and Fence installation. These deficiencies are most occurring on sites and causes customer dissatisfaction. However this does not show the highest time delays contribution, for which Pareto analysis will be used to trace the highest delay contributors in the next section.

7. Pareto chart & BAR Charts

The Pareto chart for the above table 1.1 is as follows:

Explanation: The Pareto chart shows that 87.7% site delays are due to non availability of Commercial power, Fence installation and Documentation issues; hence these deficiencies are selected for onward analysis, so that they can be minimized during site construction process.

Explanation: The above bar chart shows two different variables selected for frequency of defect occurrence and delays in rectification of issues separately. Commercial power, Fence installation and Documentation issues are most frequent defects and Commercial power, Fence installation are highest time consuming factors for rectification of issues (Ovedovitz 2001).

8. Regression analysis

Regression analysis of the Total delay time vs delay time due to Commercial power, Fence installation and Documentation is as follows:

Regression Analysis: Delay versus Comm power, Fence, Docs

The regression equation is

Delay = 2.63 + 0.738 Comm power + 0.708 Fence + 0.125 Docs

99 cases used, 269 cases contain missing values

Predictor Coef SE Coef T P

Constant 2.6250 0.2291 11.46 0.000

Comm power 0.73750 0.03542 20.82 0.000

Fence 0.70833 0.04505 15.72 0.000

Docs 0.1250 0.1107 1.13 0.262

S = 1.29574 R-Sq = 87.3% R-Sq(adj) = 86.9%

Analysis of Variance

Source DF SS MS F P

Regression 3 1094.52 364.84 217.30 0.000

Residual Error 95 159.50 1.68

Total 98 1254.02

Source DF Seq SS

Comm power 1 630.07

Fence 1 462.31

Docs 1 2.14

Unusual Observations

Comm

Obs power Delay Fit SE Fit Residual St Resid

96 0.0 10.000 2.625 0.229 7.375 5.78R

101 0.0 10.000 2.625 0.229 7.375 5.78R

R denotes an observation with a large standardized residual.

Output:

R-Sq = 87.3% > 70% means right input variables selected. shows, strong positive relationship

Similarly

  • For Comm power, p-Value < α = 0.05 indicates significant effect on site delays,
  • For Fence also, p-Value < α = 0.05 indicates significant effect on site delays and
  • For Docs, p-Value > α indicates that docs has no significant effect on site delays.

So in the next process we will omit Documentation issues and will carry on with Commercial power and Fence installation issues only (Nolan 1994).

Now for Commercial power non availability, the main causes of delay are

  • Competence level
  • Shortage of material

And for fence installation

  • Competence level
  • Quality of materials

9. SCATTER PLOT

Explanation: The scatter plot chart with groups is used with variable data (three major delay factors) to study possible relationships between them. However it does not indicate the cause and effect relationship between the three chosen groups. It shows low degree of relationship between the three delay factors. This is a vague relationship.

10. Time Series Analysis

MEAN: 5.38, Upper Control limit: 9.49, Lower control limit 1.27 (in days)

Explanation: The control chart shows that the process has special causes that are exceeding the upper control limit. The seven points on the above upper control limit and one point below the lower control limit is the special causes and following is the interpretation as given below:

  • The sites where delay is exceeding the upper control limit were found having issue with sites on legal stays where work cannot be started due to neighbour / municipal authority / government clearance issues, heavy rainfall, local strike, accident at the site and non availability of access to the site (Lee 2000).
  • The special cause with lower control limit was an ideal case where site was share with other telecom operator and no additional construction was required (Mclaughlin & Wakefield 2004).

11. Probability Distribution Plot

Explanation: The above bar chart shows the normal distribution curve with mean 5.38 and standard deviation 3.57. This shows a normal distribution plot.

12. ImprovementS

The purpose of bringing improvement was to reduce variations in the process and to optimize the process parameters. The most influential factor that result in delaying the site construction process and proceed is (Bryman & Cramer 1996):

Delays due to Commercial power availability

The most important factors for this delay are:

  • Competence, with the following levels
  • Low
  • High
  • Material availability (commercial power availability delays due to material availability), having the following levels:
  • Availability within 1 days
  • Availability within 6days

So high level: 6 days

And low level: 1 day

Design of experiment with 3 replications (from Minitab software)

StdOrder

RunOrder

CenterPt

Blocks

Competence

Material availability

7

1

1

1

Low

6

11

2

1

1

Low

6

9

3

1

1

Low

1

2

4

1

1

High

1

10

5

1

1

High

1

1

6

1

1

Low

1

5

7

1

1

Low

1

8

8

1

1

High

6

4

9

1

1

High

6

12

10

1

1

High

6

3

11

1

1

Low

6

6

12

1

1

High

1

Analysis of the results:

StdOrder

RunOrder

CenterPt

Blocks

Competence

Material availability

Delay

7

1

1

1

Low

6

12.5

11

2

1

1

Low

6

12

9

3

1

1

Low

1

11.5

2

4

1

1

High

1

7.5

10

5

1

1

High

1

8

1

6

1

1

Low

1

11

5

7

1

1

Low

1

10.5

8

8

1

1

High

6

10

4

9

1

1

High

6

10

12

10

1

1

High

6

10

3

11

1

1

Low

6

11.5

6

12

1

1

High

1

8.5

From Minitab software:

Factorial Fit: Delay versus Competence, Material availability

Estimated Effects and Coefficients for Delay (coded units)

Term Effect Coef SE Coef T P

Constant 10.250 0.1250 82.00 0.000

Competence -2.500 -1.250 0.1250 -10.00 0.000

Material availability 1.500 0.750 0.1250 6.00 0.000

Competence*Material availability 0.500 0.250 0.1250 2.00 0.081

S = 0.433013 PRESS = 3.375

R-Sq = 94.59% R-Sq(pred) = 87.84% R-Sq(adj) = 92.57%

Analysis of Variance for Delay (coded units)

Source DF Seq SS Adj SS Adj MS F P

Main Effects 2 25.5000 25.5000 12.7500 68.00 0.000

2-Way Interactions 1 0.7500 0.7500 0.7500 4.00 0.081

Residual Error 8 1.5000 1.5000 0.1875

Pure Error 8 1.5000 1.5000 0.1875

Total 11 27.7500

Estimated Coefficients for Delay using data in uncoded units

Term Coef

Constant 9.20000

Competence -1.60000

Material availability 0.300000

Competence*Material availability 0.100000

Explanation: The control chart shows that the revised and improved processes where the mean delay time on sites was reduced and the data showed that the process was brought in control by taking the following actions:

  • The overall quality policy was revised and awareness sessions were conducted to make the resources realize that the delays and customer dissatisfactions must be reduced and efficiency and effectiveness must be given special focus (Berenson, Levine, & Krehbiel 2002).
  • The process flaws and open ended processes were redefined and a strategy was made for reduction in delays on sites.
  • Subcontractor efficiency and delay factor was reported to the top management on weekly basis and management reviews were done more often.

13 CONCLUSIONS

Achievements and Financial Benefits

  • The time delay in site construction reduced from mean delay of 5.38 to 2.47days.
  • The financial impact on cash flow improved and early payment release for commissioning of sites was calculated to be 800,000 USD each month.
  • The annual saving is estimated to be 9.6million USD. The other non-financial benefits that helped the company was improved processes and motivation among the resources.
  • These results were deployed on international projects as well and the regional team appreciated the analysis and has started implementing this in their regional projects spread across three different countries.
  • The subcontractor also benefited from the results and their cash flow also improved. The subcontractor teams were more effective when the exact issues were addressed and the efficiency was significant.
  • Customer satisfaction survey showed that the customer satisfaction index which is from a range of 1 to 10 improved. The initial CSS showed dissatisfaction at 3 and after six months of implementation of changes and new strategy this CSS result was 7. This was a major milestone in getting more business and improving the standing in the telecom market.

Decision Making Strategy

The organization used this data and had few management reviews to bring some significant changes and improvements. The site construction delay factor was a major hindrance in cash flow and re-works on sites. The policy of the organization was focused on resource involvement and training needs were given prime importance. A committee was setup to drive the delay reduction strategy in the site construction which was having sufficient powers and influence on different departments. The decisions were based on the statistical results and the delays per site were archived and analyzed on regular basis.

The country manager of the organization declared a new policy on the incentives on site construction with balanced quality, time and scope. The human resource department was involved to support in the resource development area and more incentives out of schedule were approved from the board of directors and regional head of the organizations.

The strategy towards the subcontractor was also modified and the competence development among the resources of the organization and the subcontractor were improved. Budget allocation on individual development and team building exercises were given focus and regular checks were developed.

The project director gave a further decomposed strategy to the team members and the stakeholders. This was a documented memo with areas of focus for short term and long term targets set for each stake holders and quarterly review of the short term and long term targets was scheduled as well. This implied that the stakeholders would be in constant communication with the management and also started focussing on the root causes rather than taking corrective actions.

14. REFERENCES

  • Berenson, M., Levine, D., & Krehbiel, T. (2002).Basic Business Statistics. Englewood Cliffs: Prentice Hall.
  • Bryman, A., & Cramer, D. (1996).Quantitative Data Analysis with Minitab. New York: Routledge.
  • D., L., & J., S. (2008).The Little Sas Book. City: SAS Press.
  • Degroot, M., & Schervish, M. (2002).Probability and Statistics. Boston: Addison-Wesley.
  • Lee, J., (2000).Business and Financial Statistics Using Minitab 12 and Microsoft Excel 97. City: World Scientific Publishing Company.
  • Longnecker, M., (2008).An Introduction to Statistical Methods and Data Analysis. Pacific Grove: Duxbury Press.
  • Mclaughlin, K., & Wakefield, D. (2004).Introduction to Data Analysis Using Minitab for Windows, an. Englewood Cliffs: Prentice Hall.
  • Nolan, B., (1994).Data Analysis. Cambridge: Polity Press.
  • Ovedovitz, A., (2001).Business Statistics in Brief. Cincinnati: South-Western College Pub.
  • Taylor, J., & Cihon, C. (2004).Statistical Techniques for Data Analysis, Second Edition. Boca Raton: Chapman & Hall/CRC.

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