BUSS 2012 MEC Research Methods Data Analytics and Project Planning Essay

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Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP IN SEMESTER INDIVIDUAL ASSIGNMENT Module Code: BUSS B 2012 Module Name: Research Methods, Data Analytics and Project Planning Level: 2 Max. Marks: 100 Instructions to Student    Answer all questions. Deadline of submission: 08/07/2020 23:59 The marks received on the assignment will be scaled down to the actual weightage of the assignment which is marks Formative feedback on the complete assignment draft will be provided if the draft is submitted at least 10 days before the final submission date. Feedback after final evaluation will be provided by 25/07/2020   Module Learning Outcomes 1. Design a project plan for business improvement. 2. Execute reliable research on business processes and other phenomenon. 3. Distinguish the different distributions in statistics. 4. Present research findings and opportunities for improvement. 5. Be able to summarize, explain and apply basic principles of good project management. Assignment Objective After finishing this assignment, student should be able to present research findings and opportunities for improvement. Further students also should be able to summarize, explain and apply basic principles of good project management. Assignment Tasks This assignment is an individual assignment. The assignment has 4 different parts and each part should be answered.  Part 1: Literature Review / Report Structure  Part 2: Descriptive Statistics and Data Analysis  Part 3: Order Profile and Walking Distances in a Warehouse Project  Part 4: Correlation, Regressions and Probability Distributions In the report it is important to show both the results and the way the results were achieved, just a couple of answers will not suffice. Where possible results should be supported by graphs. MEC_AMO_TEM_035_01 Page 1 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP Please do not print out entire Excel files as an appendix. All graphs and tables should have a correct and appropriate layout. The report must have the following structure          Introduction about the module. Application of module learning outcome in industries. Categories of Research / Research Methodology. One each literature review on the following five topics a. Qualitative Research or Quantitative Research b. Excel Data Analysis c. Project Management / Project Planning d. Logistics and Supply Chain e. Warehousing or Transportation Summarizing MOOC Lecture. (View all video lectures of Week 1 from the following link and summarize the learning https://nptel.ac.in/courses/121/106/121106007/ ) Descriptive Statistics and Data Analysis Order Profiles and Walking Distances in a Warehouse Correlations, Regressions and Probability Distributions Conclusion References (including MOOC, OMREN-Masader etc.) The weightage of this assignment constitutes 30% of the final marks. Context of the assignment Use the file that goes with this assignment an article file of a logistic company. The given file is for the spare parts of Mercedes, Toyota and Audi warehouses. In total there are data on 160 SKUs. In this assignment we will help a logistic company to make a number of strategic, tactical, and operational choices using statistical calculations. The various problems can be solved using the supplied data file. To be able to make the assignments you have the sales files. over a period of one month and you have the article file. In this assignment we will help the logistic company make a number of strategic, tactical, and operational choices using statistical calculations. The various problems can be solved by use of the supplied data files. Learning goals: Part 1: Goal of this assignment is to learn explore the research with the help of various literatures, research structure. Part 2: Goal of this assignment is to learn to deal with the cleaning of data sets based on the correct criteria and to analyse these data sets using tables, standards, and visualizations. Part 3: This question is meant to apply knowledge achieved during the statistics lectures to a number of daily logistical problems. MEC_AMO_TEM_035_01 Page 2 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP Part 4: The goal of this assignment is to apply statistical knowledge, acquired during the Statistics course, to problems faced during daily logistics operations. Part 1: Literature Review / Report Structure (Total 25 Marks) Create a report with the following information. Introduction about the module. Application of module learning outcome in industries.      Introduction about the module. Application of module learning outcome in industries. Categories of Research / Research Methodology. Literature Review Summarizing MOOC Lecture (View all video lectures of Week 1 from the following link and summarize the learning https://nptel.ac.in/courses/121/106/121106007/ ) Conclusion, References. Apart from the above information, the report also must include the answers of Part 2, Part3 and Part 4 of the assignments. Part 2: Descriptive Statistics and Data Analysis (25 Marks) Note : Use the “Data Analysis” Worksheets for solving Part 2. a) Clean the data using the below steps:    Check the data set for contamination. Filter any bad data from the data set. Show what data have been filtered based on what criteria and state which percentage has been filtered out. For the rest of the assignment work with the cleaned data set (unless stated differently). b) Make a frequency table of the brand showing:  Frequency  Relative frequency  Cumulative frequency  Cumulative relative frequency c) Make a bar chart for the absolute frequency of the brand. d) Make a histogram of the number of weight per article and no. of replenishment lines per day. e) Determine mode, median, and average of the number of sold pieces present year. f) Check is there any significant difference between no of pieces sold present year and No of pieces sold previous year. Assume Unequal variances. Find out which year sale performance is better. MEC_AMO_TEM_035_01 Page 3 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP Part 3: Order profiles and walking distances in a warehouse (25 Marks) The company wants to increase the efficiency of their warehouse. The warehouse manager is considering relocating stock in the warehouse. The following groups are given: AA-articles (super fast movers): A- articles (fast movers): B- articles (medium movers): C- articles (slow movers): 4% of the articles 10% of the articles 23% of the articles 63% of the articles Note : Use the “Sales File” and “Article File” and “Warehouse Layouts” Worksheets for solving Part 3. You can also refer the below link to get more knowledge about warehouse layouts https://www.interlakemecalux.com/warehouse-manual/warehousedesign/warehouse-material-flowchart https://www.interlakemecalux.com/warehouse-manual/warehousedesign/warehouse-layout a) Make a graph showing the ABC-analysis (see book: “Introduction into Logistics”) for the articles of the selected logistic company. In the graph the y-axis shows the cumulative percentage of orderliness and the x-axis shows the cumulative percentage of SKUs. (3 Marks) (Tips: - An order line is the demand of one customer for one or more pieces of one SKU. An order consists of one or more order lines. - First determine the number of order lines per SKU. To do so you can use the function “countif” in Excel. (Use all SKUs given in the “article file”, also those that did not have any sales). Sequentially sort the SKUs in order of decreasing number of order lines.) You can also refer the below link b) Per group (AA, A, B, C) determine the number and percentage of SKUs, the number, and percentage of order lines and the number and percentage of used storage locations. Show this in a table. (3 Marks) (Tip: - To determine the number of used locations you can use the number of pallets on stock per SKU (see: “article file”). Don’t forget, half a pallet also uses 1 location.) c) What is the average number of order lines per order? (3 Marks) On the next page two lay-outs are shown. These lay-outs are also given in the data file. To simplify the problem all stock is placed on ground level locations. A pallet location has a width of 0.9m and a depth of 1.2m. Between two pallet locations there is a space of 0.2m. The lines given in the lay-outs to separate the areas (e.g. AA and A) are indicative. MEC_AMO_TEM_035_01 Page 4 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP Note : Pallet distance from x is 8m Warehouse lay-out A (layout dimensions are decisive. Separations between groups indicative) x = start route Warehouse lay-out B (layout dimensions are decisive. Separations between groups indicative) x = start route d) Based on the number of used locations per group (AA, A, B, and C) determine the exact allocation of groups within the two warehouse lay-outs. (12 Marks) (TIP: - You can do this by colouring the locations of the different groups in the lay-outs. - It is possible that the lines are not exactly straight.) MEC_AMO_TEM_035_01 Page 5 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP Answer the following questions for both warehouse lay-outs: e) What is the expected walking distance for an AA, A, B, and C article for both lay-outs? (2 Marks) (TIPS: - To determine the average walking distance, you can follow two strategies: 1. Determine per location the walking distance and then take the average per group. 2. Determine how far you have to walk on average in horizontal direction and sequentially how far you have to walk on average in vertical direction.) a) Based on the calculations, which warehouse design would you choose?. And why ? Part 4: Correlation, Regression and Probability Distributions. (25 Marks) Note : Use the “Emergency Orders” Worksheets for solving Part 4. Using the emergency order data provided, discuss the following with necessary tables, graphs and trend lines. a) Draw the relationship (Correlation) between each variable (Order Picking, Packaging and Shipping) with separate tables and scatterplots. Discuss each result. (9 Marks) 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. (9 Marks) c) Predict any 6 set of data using the generated regression equations. (3 Marks) d) Apply paired sample t-test for the relevant data and discuss the result. (4 Marks) Rules & Regulations:      All resources should be cited using CU Harvard style. The final assignment must have a Title page, Table of Contents, References/ bibliography using CU Harvard Style and page numbers. Title Page must have Assignment Name, Module name, Session, your name, ID, and the name of the faculty. Softcopy in word format is to be submitted through Turnitin link on Moodle. Viva will be conducted after the assignment submission as per the dates informed earlier. Guidelines: The assignment report may consists of approximately 1200-1500 words and not exceeding 1500 words (Excluding Tables and Data), which comprises of the answers to the above questions. Assignment must be computer typed.  Font - Times New Roman MEC_AMO_TEM_035_01 Page 6 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP  Font – Style - Regular  Font - Size - 12  Heading should be with Font Size 14, Bold, Capital and Underline.    Explain with suitable diagrams wherever required. Diagrams must be drawn using suitable software or by pencil. Each student has to do the assignment individually / Students have to do the assignment collaboratively and each student should write a brief reflection on their contribution and learnings from group work. You can refer books in eLibrary or use internet resource. But you should not cut and paste material from internet nor provide photocopied material from books. The assignment answers should be in your own words after understanding the matter from the above resources. Important Policies to be followed 1. Student Academic Integrity Policy*: MEC upholds the spirit of academic integrity in all forms of academic work and any form of violation of academic integrity shall invite severe penalty. Any benefit obtained by indulging in the act of violation of academic integrity shall be cancelled. All cases of violation of academic integrity on the part of the student shall fall under any of the below mentioned categories: 1. Plagiarism 2. Malpractice 3. Ghost Writing 4. Collusion 5. Other cases If the student fails a module and has a proven case of academic integrity violation in this module, the student is required to re-register the module. This is applicable to first and second offenders of plagiarism. 1. Plagiarism A. First offence of plagiarism I. If a student is caught first time in an act of plagiarism during his/her course of study in any assignment other than project work, the student will be allowed to re-submit the assignment once, within a maximum period of one week. However, a penalty of deduction of 25% of the marks obtained for the resubmitted work will be imposed. II. Period of re-submission: The student will have to re-submit the work one week from the date he or she is advised to re-submit. III. If the re-submitted work is also found to be plagiarized, then that assessment will be awarded a zero mark. Re-submission of the work beyond the maximum period of one week will not be accepted and the assessment will be awarded a zero mark. B. Second offence of plagiarism MEC_AMO_TEM_035_01 Page 7 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP If any student is caught second time in an act of plagiarism during his/her course of study (in a subsequent semester), the student will directly be awarded zero for the work in which plagiarism is detected. In such cases, the student will not be allowed to resubmit the work. A warning of suspension shall be issued, and student has to sign an undertaking and undergo counselling session in such cases. 2. Malpractice/Ghostwriting/Collusion A. First offence of Malpractice/Ghostwriting/Collusion If a student is caught in an act of Malpractice/Ghostwriting/Collusion for an assessment component irrespective of coursework or end semester, the student shall fail the module and shall be required to re-register the module B. Second Offence of Malpractice/Ghostwriting/Collusion If a student is caught a second time in an act of Malpractice/Ghostwriting/Collusion for an assessment component irrespective of coursework or end semester, the student shall fail the module. A warning of suspension shall be issued, and student has to sign an undertaking and undergo counselling session in such cases. 3. Third Offence of Academic Integrity Violation If a student is caught a third time in an act of Academic Integrity Violation for an assessment component irrespective of coursework or end semester (in a subsequent semester), the student shall fail the module and also shall be suspended for one semester from the College, as recommended by institutional level academic committee, Chaired by the Associate Dean, Academic Affairs. 4. Fourth Offence of Academic Integrity Violation: If a student is caught a fourth time in an act of Academic Integrity Violation for an assessment component irrespective of coursework or end semester (in a subsequent semester), the student shall fail the module and also shall be expelled from the College, as recommended by institutional level academic committee, Chaired by the Associate Dean, Academic Affairs. 5. Other cases If a student commits an act of academic integrity violation as per the definition of “other cases” mentioned in the previous section or of a different nature, student’s case shall be forwarded to an institutional level academic committee, Chaired by the Associate Dean, Academic Affairs. The committee shall investigate the case by means of a viva and/or a disciplinary hearing and shall take appropriate decision. The penalty that can be granted to a proven case of academic integrity violation which falls in this category of “other cases” can be a warning/component zero/ module fail/suspension/expulsion depending on the nature and gravity of the offence. 6. Types/Variations of Cases: I. II. If plagiarism is detected in any component of one assessment, the deduction in marks will be applicable for the whole assessment, even if only the component or part submission alone needs to be resubmitted. If plagiarism is detected in a group assessment, all students of the group will be considered as having committed an act of plagiarism and the policy will then be applied to all students MEC_AMO_TEM_035_01 Page 8 of 12 III. IV. Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP If plagiarism is detected in any component of a group assessment, the deduction in marks will be applicable for the whole assessment even if only the component or part submission alone needs to be resubmitted. All students of the group would be considered as having committed an act of plagiarism and the policy will then be applied to all the students of the group. If the assessment consists of components or part submissions that could be a group assessment component (e.g. group assignment) and an individual assessment component (e.g. individual reflection), the following will be applicable: a. If plagiarism is detected in the group assessment component, all students of the group will be considered as having committed an act of plagiarism, The policy will then be applied to all students of the group. Group assessment component will be resubmitted as per the policy. b. If plagiarism is detected in the individual assessment component, the individual assessment component will be resubmitted and the policy will then be applied to that student alone. c. For both (a) and/or (b), the deduction in marks will be applicable for the whole assessment. * for further details Refer to MEC Student Academic Integrity Policy in Student Handbook. 2. Late Submission Regulations: It is the students’ responsibility to check all relevant timelines related to assessments. As per the Assessment Policy at MEC, late submissions are allowed for one week (5 working days) for all UG modules with a penalty. In such cases, a deduction of 5% of the marks obtained for the submitted work shall be imposed for each working day following the last date of submission till the date of actual submission. Assessment documents submitted beyond a period of one week (5 working days) after the last date of submission will not be accepted and will be awarded a zero for that assessment. In cases where the submission has been delayed due to extenuating circumstances, the student may be permitted to submit the work without imposing the late submission policy stated above. The extended period of submission will be one week from the original last date of submission. In such cases, the student is expected to submit the supporting certificates on or before the original last date of submission of the assessment and the decision of extension rests with faculty responsible for the assessment .The late submission policy shall be applied if the student fails to submit the work within one week of the original last date of submission. Students may contact their teachers for clarification on specific details of the submission time if required. 3. Research Ethics and Biosafety Policy To protect and respect the rights, dignity, health, safety, and privacy of research subjects involved including the welfare of animals and the integrity of environment, all student projects are expected to be undertaken as per the MEC Research Ethics and Biosafety Policy. Accordingly the following shall apply. MEC_AMO_TEM_035_01 Page 9 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP  Research and other enterprise activities shall be conducted by maintaining the high ethical standards consistent with national and international standards and conventions.  Any research at MEC that is categorized as high-risk research shall be subject to review and approval by the Research Ethics and Biosafety Committee.  Research activities involving collection of human or animal tissues and manipulation of microbial, animal or plant cells shall be subject to review and approval by the Research Ethics and Biosafety Committee.  Participants involved in research must be informed about the purpose of research and intended uses of research findings. Written consent must be obtained from people involved prior to the commencement of research.  Data obtained from participants must be treated with high confidence and should be used only for the intended purpose of research. Assessment Evaluation Criteria Classification And % Range Outstanding Excellent Very Good Quality Good (Acceptable) Adequate/ Satisfactory Reflection and critical analysis. Highly competent analytical skills and reflective practice, demonstrating personal learning and growth, insight into required professional values and principles and professional development planning. Strong analytical skills and reflective practice used, demonstrating personal learning and growth, insight into required professional values, principles and competencies and professional development planning. Good use of analytical skills and reflective practice demonstrating personal learning and growth, insight into required professional values, principles and competencies and professional development planning. Acceptable use of analytical skills and reflective practice demonstrating personal learning and growth, insight into required professional values, principles and competencies and professional development planning. Adequate use of analytical skills and reflective practice demonstrating personal learning and growth, insight into required professional values, principles and competencies and professional development planning. MEC_AMO_TEM_035_01 Knowledge and Understanding/ Application of Theory Extensive knowledge and depth of understanding of principles and concepts and /or outstanding application of theory in practice. Referencing and Bibliography Presentation, Grammar and Spelling Evidence of reading an extensive range of educational literature/research and where applicable workplace strategies, policies and procedures. Accurate referencing and bibliography correctly using appropriate referencing style Excellent presentation, logically structured, using correct grammar and spelling, excellent crossreferencing and links to supporting evidence Excellent knowledge and understanding of principles and concepts and /or excellent knowledge and understanding of the application of theory in practice Evidence of reading a wide range of educational literature/research and where applicable, workplace strategies, policies and procedures. Appropriate referencing and bibliography correctly using appropriate referencing style Good presentation, competently structured, using correct grammar and spelling, clear and easy to use links to supporting evidence Good knowledge or key principles and concepts and/or good knowledge of the application of theory in practice Evidence of reading a good range of educational literature/research and where applicable workplace strategies, policies and procedures. Generally well referenced with correct use of the appropriate referencing style Reasonable presentation, completely structured, acceptable grammar and spelling, acceptable links to supporting evidence Acceptable knowledge of key principles and concepts and/or knowledge of the application of theory in practice Evidence of reading an appropriate range of educational literature/research and where applicable, relevant workplace policies and procedures Adequate referencing. Generally accurate use of appropriate referencing style Adequate presentation and structure, acceptable grammar and spelling, adequate links to supporting evidence Adequate knowledge of key principles and concepts and/or satisfactory evidence of the application of theory in practice. Evidence of minimal reading of educational literature/research and where applicable relevant workplace policies and procedures Adequate referencing. Appropriate referencing style used but may contain some inaccuracies. Weak presentation , satisfactory structure, grammar and spelling, links to supporting evidence Evidence of Reading Page 10 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP Weak /Poor (all learning outcomes not adequately met) Little use of analytical skills and reflective practice demonstrating personal learning and growth, insight into required competencies and/or professional development planning. Professional values and principles not reflected in the submission. and/or Insufficient/no use of analytical skills and reflective practice demonstrating personal learning and growth, insight into required competencies and professional development planning MEC_AMO_TEM_035_01 Little evidence of knowledge of key principles or concepts and/or little evidence of the application of theory in practice Little or no evidence of reading outside of the course textbook and/or reference to relevant work place policies and procedures and/or and/or No evidence of knowledge of key principles or concepts and/or no evidence of application of theory in practice No evidence of reading outside of the course textbook and/or reference to relevant workplace policies and procedures Little or no referencing, incorrect style, or very inaccurate use of appropriate referencing style Poor presentation, grammar and spelling, links to supporting evidence and/or Unacceptable presentation, grammar and spelling, structure is very poor, links to supporting evidence Page 11 of 12 Research Methods, Data Analytics and Project Planning (BUSS-B 2012) – Spring 20 – CW3– QP BUSS-B 2012 – Research Project Planning - Assignment Methods, Data Analytics and ID NO. __________________ NAME : ______________________________________ Deliverables Aspects 0-5 6-20 (Band : 6-15 Satisfactory, 16-20 Good) Part 1 Literature Review / Report Structure Poor discussion of Introduction, Categorization of Research/ Research Methodology, Literature Review, MOOC, Conclusion, References Deliverables Aspects 0-5 Satisfactory / Good discussion of Introduction, Categorization of Research/ Research Methodology, Literature Review, MOOC, Conclusion, References. However few discussion context needs improvement Descriptive statistics and data Analysis Poor discussion of descriptive statistics and Data Analysis. Most of analysis found wrong Deliverables Aspects 0-5 Satisfactory / Good discussion of descriptive statistics and Data Analysis. However few statistical analysis and discussion needs improvements Part 3 Order profile and walking distance in warehouse Poor analysis of order profiling and walking distances in warehouse Deliverables Aspects 0-5 Satisfactory / Good analysis of order profiling and walking distances in warehouse. However few analysis and discussion needs improvements Correlation, Regression and Probability Distribution Poor analysis of Correlation, Regression and Probability Distribution Satisfactory / Good discussion of Correlation, Regression and Probability Distribution. However few analysis and discussion needs improvements Mark Very Good / Excellent discussion of descriptive statistics and Data Analysis. Observed most of the discussions are presented with proper result analysis. 21-25 Mark (Band : 21-23 Very Good, 24-25 Excellent Very Good / Excellent analysis of order profiling and walking distances in warehouse. Observed most of the discussions are presented with proper result analysis. 6-20 (Band : 6-15 Satisfactory, 16-20 Good) Part 4 21-25 (Band : 21-23 Very Good, 24-25 Excellent 6-20 (Band : 6-15 Satisfactory, 16-20 Good) Mark Very Good / Excellent discussion Poor discussion of Introduction, Categorization of Research/ Research Methodology, Literature Review, MOOC, Conclusion, References. Most of the discussion found relevant and good key points covered. 6-20 (Band : 6-15 Satisfactory, 16-20 Good) Part 2 21-25 (Band : 21-23 Very Good, 24-25 Excellent 21-25 Mark (Band : 21-23 Very Good, 24-25 Excellent Very Good / Excellent discussion of Correlation, Regression and Probability Distribution. . Observed most of the discussion are presented with proper result analysis. Signature of the Evaluator : Total Marks Comments : Penalty Final Marks MEC_AMO_TEM_035_01 Page 12 of 12
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Research Methods, Data Analytics and Project Planning
Name
Institutional Affiliation

1. Literature review/ report structure
a) Introduction
The module focuses on research and data analysis. Research is an integral part in academic and
businesses as well. Therefore, it should be done after critical analysis of the topic to avoid
duplication of ideas and have the maximize the results. This module has showed the important
steps to undertake during a research and the importance of research in various institutions. Also,
the module shows the various data analysis techniques. This module is prepared to impacting
knowledge to students prior to industrial setting. After completion of this module the student can
showcase their knowledge and hands-on skillset in solving business problems technically.

b) Categories of research
Research is the creativity and innovativeness embraced by humans to solve the problems around
them. It involves analyzing the available aspects, criticizing the aspects, acquiring deep
knowledge on a subject and presenting results to the relevant community. Research is
categorized into two major classifications; qualitative and quantitative research.

c) Qualitative research
Most research activities are based on qualitative data rather than quantitative data. Qualitative
research involves use of information rather numerical data. Unstructured data is obtained in
various forms and analyzed to give explanations and prove certain phenomenon. Recent study
has showed that qualitative research as the back bone for societal changes(Hennink et al., 2020).
Moreover, qualitative analysis is more specific and deeply embedded on a subject.

d) Quantitative research
Quantitative approach is based on systematic analysis of a topic using the available statistics,
mathematical datasets. Researchers and analysts obtain large datasets and with the help of
mathematical models’ researchers can generalize the large dataset eliminating bias.

e) Data analysis using excel
Most people view excel as a basic spreadsheet for storing data, however, most statisticians and
data analysts today use excel in even complex datasets. Excel offers a variety of ...


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