MBA673 Business Analytics Life Cycle Report 2 Sample

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MBA673 Business Analytics Life Cycle Report 2 Sample

Assessment Description

The Garment Industry is one of the key examples of the industrial globalisation of this modern era. It is a highly labor-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories.

Assessment Instructions

In class: Your group of 3 to 4 team members will be given a dataset at the commencement of week 10 class from a garment factory. As a group analyse the dataset using Tableau and exploratory.io. for 120 mins

Your nominated group spokesperson will provide a 6 minute verbal presentation of your work in the final 60 minutes of class time. Please note the data set will be posted at the beginning of your class time in Week 10.

After Class: As an individual, you will take notes in class and then write a 500-word report which summarises the analysis, as well as providing suggestions for further analysis. This component of the assessment is to be submitted via Turnitin in week 11.

The dataset can be used for regression purposes by predicting the productivity range (0-1) In so doing, the dataset can also be used to build a forecasting model by analysing the productivity of the factory over time.

The data attributes are:

01 date : Date in MM-DD-YYYY

02 day : Day of the Week

03 quarter : A portion of the month. A month was divided into four quarters

04 department : Associated department with the instance

05 team_no : Associated team number with the instance

06 no_of_workers : Number of workers in each team 0

07 no_of_style_change : Number of changes in the style of a particular product

08 targeted_productivity : Targeted productivity set by the Authority for each team for each day.

09 smv : Standard Minute Value, it is the allocated time for a task

10 wip : Work in progress. Includes the number of unfinished items for products

11 over_time : Represents the amount of overtime by each team in minutes

12 incentive : Represents the amount of financial incentive (in BDT) that enables or motivates a particular course of action.

13 idle_time : The amount of time when the production was interrupted due to several reasons

14 idle_men : The number of workers who were idle due to production interruption

15 actual_productivity : The actual % of productivity that was delivered by the workers. It ranges from 0-1.

• Individual Task (20 marks)Write notes explaining your data visualisations and insights derived therefrom (200 words, 8 marks)

• Write notes explaining your forecasting model (100 words, 4 marks)

• Recommend how your dataset, visualisations and forecasts could be improved (200 words, 8 marks).

Solution

Introduction

The report discusses the effect of globalisation on the garment industry. The report explains the data visualisations and insights that are derived from productivity comparisons. It also explains the forecasting model that is used to implement the regression model. In the end, some recommendations are given on the dataset, visualisations and forecasts that are made.

Explanation of Data Visualisations

The productivity performance of the working team can be observed through the data visualisations (Ahmad et al., 2020). Data visualization is regarded as the representation of data and information. It uses the graph chart visual tools and maps in order to present the data and ensure relevant understanding of the content to the user. It is quite convenient for the data professional to quickly explain the trend pattern and outliners in the data set. Data visualization is considered to be an important step in the data processing and conveying of data. University assignment help, The data should be presented in our tabular format or graphical format (Evergreen, 2019). Data visualization improves organizational effectiveness and manages productivity. The effective use of data visualization helps to understand the potential of the organization and manage the performance in a constructive way. Data visualization has revealed that effective approaches towards organizational productivity manage to overcome the resources in a relevant way. Individual performance is represented graphically and different numbers could depict different forms of performance within the organizational environment. It is more evidence that team members have a strong capability and they have depicted excellent service quality in the organization environment.

Explanation of the Forecasting Model

The forecasting model is used to compare the departments. According to the comparison between the day and give targeted and actual productivity it can be identified that all weekdays finished with actual productivity higher than the targeted productivity. However, on the sunday the productivity was less due to less active performance.

As per the last data visualisations, it can be assumed that almost every team has performed with the same outcomes of actual productivity in comparison to the actual time they got. Based on the data visualisations it can be determined that a forecasting model is used to compare the productivity of departments on a daily basis (Golnaraghiet al., 2019). The model compares the targeted productivity and day to determine the actually achieved productivity by each team.

Recommendations

It is required to make sure the dataset includes pertinent productivity-related factors including workforce distribution, incentives, target productivity, job completion times, and individual and team performance measures.

In order to further enhance the quality of analysis, it is important to use the data visualisations technique to identify important performance metrics in an understandable way. Data consolidated technique to be used to track the performance attributes of each team instead of analysing the teams’ productivity as a single entity.

It is also important to forecast models by including extra productivity-affecting variables such as seasonal changes, market demand, or external variables that have an impact on the workflow. In order to create projections that are more reliable and accurate, it is worth utilising cutting-edge methods like machine learning algorithms.

Conclusion

The report has discussed the data visualisations that have been done to compare the productivity performance of teams. It has compared the actual performance of the teams and their targeted productivity.  

References

Ahmad, S., Miskon, S., Alabdan, R. and Tlili, I., 2020. Towards sustainable textile and apparel industry: Exploring the role of business intelligence systems in the era of industry 4.0. Sustainability, 12(7), p.2632.

Evergreen, S. D., 2019. Effective data visualization: The right chart for the right data. SAGE publications.

Golnaraghi, S., Zangenehmadar, Z., Moselhi, O. and Alkass, S., 2019. Application of artificial neural network (s) in predicting formwork labour productivity. Advances in Civil Engineering, 2019.

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