| Course | DBA-8060 Empirical Entrepreneurship Data, Technology, and Business Intelligence |
|---|---|
| Module | Module 2 |
| Paper type | Module 2 written assignment: data, statistical modeling and visualization |
| Length | About 546 words, 4 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Doctor of Business Administration |
| Updated | October 2026 |
Free sample paper for DBA 8060 Module 2
Variability, Not Length: What 214 Purchase Orders Say About DAKOKOO's Stockouts
Student Name
Doctor of Business Administration, Thomas Edison State University
DBA-8060: Empirical Entrepreneurship: Data, Technology, and Business Intelligence
Mentor Name
Month Day, Year
Variability, Not Length: What 214 Purchase Orders Say About DAKOKOO's Stockouts
Introduction
DAKOKOO's stockouts are often blamed on long supplier lead times. Before building any system, I examined the evidence in the records we already have. Of 520 import purchase orders placed in the past year, only 214 have both an order date and a receipt date; the rest show receipt only in emails. Hazen et al. (2014) argue that poor data quality, including incomplete and inaccurate records, is a basic barrier to analytics in supply chains, and these missing dates are a direct example. This paper uses those 214 orders to ask whether stockouts follow from long lead times or from unpredictable ones.
The Nature of the Data
Data types determine which statistical methods are valid. Sharda et al. (2018) distinguish categorical data, which are nominal or ordinal, from numeric data measured on interval or ratio scales, and structured data in tables from unstructured text.
Lead times ran from 31 to 96 days, with a mean of 54 and a standard deviation of 14. Across the 38 suppliers, the standard deviation of each supplier's own lead times ranged from 3 to 22 days.
| Field | Type | Structure | Use |
|---|---|---|---|
| Supplier name | Nominal | Structured | Grouping |
| Shipping mode (ocean, air) | Nominal | Structured | Control variable |
| Priority class (A, B, C) | Ordinal | Structured | Grouping |
| Order and receipt dates | Interval | Partly structured | Lead time in days |
| Lead time in days | Ratio | Derived | Main measure |
| Supplier delay emails | Text | Unstructured | Missing receipt dates |
A Simple Model
I fitted a linear regression of each supplier's stockout rate, the share of its items out of stock at a distribution center in a month, on two predictors: its mean lead time and the standard deviation of its lead times. With 38 suppliers the model is small, but its pattern is clear. The standard deviation was a significant predictor, with each additional day of variability associated with about 0.3 percentage points more stockouts, while mean lead time was not significant once variability was included. The model explained about 46 percent of the variation in stockout rates.
The result fits supply chain theory: safety stock is set for an expected lead time, and unpredictable deliveries defeat it. It does not prove cause. Suppliers with variable lead times may also differ in product type, and 306 orders without dates are missing from the analysis, perhaps not at random.
Showing the Result
Cleveland and McGill (1984) ran experiments showing that readers compare numbers best when the marks share one axis and worst when they must judge angles, areas or volumes. Our monthly report shows stockouts by supplier in a three-dimensional pie chart, the hardest form to read. I replaced it with a dot plot of each supplier's stockout rate, sorted from highest to lowest, and a scatterplot of stockout rate against lead-time standard deviation with the regression line drawn. Both place values on common scales, and the scatterplot shows the relationship itself rather than asking managers to infer it.
Conclusion
The records show that DAKOKOO's stockouts follow unpredictable lead times more than long ones. The analysis is limited by a small sample and missing dates, which is itself the strongest argument for capturing receipt dates automatically. Better charts make the finding visible to the managers who must act on it.
References
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.1080/01621459.1984.10478080
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://doi.org/10.1016/j.ijpe.2014.04.018
Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (4th ed.). Pearson.
What the DBA 8060 Module 2 instructions ask for
Module 2 of DBA-8060 covers the nature of data, statistical modeling and visualization, and one of the course's five written assignments, worth 35 percent together, fits it; the syllabus leaves the module placement of each assignment to the course site. Expect to classify an organization's data by type, describe a key measure, apply a statistical model suited to the question and present results in charts a manager can read. Use the textbook's distinctions among nominal, ordinal, interval and ratio data and between structured and unstructured data. A small real analysis, even with limits, shows more than a description of methods, provided you state the sample, the result and what the model cannot show.
Inside the DBA 8060 Module 2 example
The paper begins with a question managers argue about: are stockouts caused by long lead times or unpredictable ones? A six-row table classifies the lead-time fields by type and structure. Descriptive figures give the range, mean and spread of lead times across 214 orders and 38 suppliers. A regression with two predictors shows variability, not length, tracking stockouts, and the paper states its sample, effect size and explained variance before naming two limits: confounding by product type and missing records. The visualization section uses Cleveland and McGill's ranking of graphical judgments to replace a three-dimensional pie chart with a sorted dot plot and a scatterplot. Hazen and colleagues support the data quality point.
Where the marks sit in the DBA 8060 Module 2 rubric
Mentors look for correct classification of data, appropriate statistical methods, accurate interpretation, sound visualization choices and scholarly support in APA style. Credit goes to assignments that report models honestly: sample size, effect, fit and limits. Treating missing data as a finding, rather than hiding it, shows the judgment expected at the doctoral level. Visualization choices supported by research on how people read graphs show more than personal preference. The connection between analysis and decision, in this case what managers should watch, shows that statistical work serves the organization. Concise tables and plain explanations of results meet the course's emphasis on communication.
Common DBA 8060 Module 2 mistakes, and how to avoid them
Students lose marks by describing statistical techniques in general terms without analyzing any data, or by reporting a model without its sample size and limits. Some treat ordinal codes as numbers and average them. Others recommend charts on looks alone, including pie and three-dimensional charts that research shows are hard to read. Pick a question your managers argue about, sort the fields that bear on it by type, fit one model you can defend and state its finding in plain words. Say what the data cannot show. Request a free first sample of your own DBA-8060 Module 2 written assignment, built around your data. Keep the charts in an appendix only if the paper's length limit forces it, and describe in the text what each one shows.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More DBA 8060 and Doctor of Business Administration sample papers
- DBA 8060 Module 1: Written Assignment: Big Data as an Entrepreneurial Opportunity at a Retail Distributor
- DBA 8060 Module 3: Discussion: A Star Schema and a Scorecard for DAKOKOO's Retail Contracts
- DBA 8060 Module 4: Written Assignment: Predicting Stockout Risk With a Classification Tree
- DBA 8060 Module 5: Written Assignment: Mining Supplier Emails and Shopper Posts for Lead-Time Signals
- DBA 8060 Module 6: Written Assignment: Optimizing Safety Stock and Simulating Expedite Costs
- DBA 8060 Module 7: Discussion: Batch, Stream or Managed Cloud? Choosing DAKOKOO's Big Data Tools
- DBA 8060 Module 8: Final Project: A White Paper on Implementing Big Data at DAKOKOO Distribution
- DBA 8020 Module 4: Discussion: How the Fed's Rate Path Affects a Refill Launch
- DBA 8000 Module 2: Written Assignment: The Traps Hidden in the Core Lab Decision
- HRM 8010 Module 1: Written Assignment: Global Workforce Planning for Three Sites
- DBA 8090 Module 3: Written Assignment: Diagnosis and Resistance at a Marine Contractor
DBA 8060 Module 2 questions, answered
Where can I find a free DBA 8060 Module 2 sample paper?
The paper above models DAKOKOO's supplier lead times and stockouts and redesigns its charts for Module 2.
What are the four levels of measurement?
Nominal and ordinal data are categorical, while interval and ratio data are numeric, and each level allows different statistics.
Why are pie charts hard to read?
Cleveland and McGill found people judge angle and area less accurately than position along a common scale.
What is the difference between structured and unstructured data?
Rows and columns with set fields are structured; free text like supplier emails has no fixed layout and counts as unstructured.
How many written assignments does DBA-8060 have?
Five written assignments worth 35 percent together, alongside eight discussions, two synchronous events and a final white paper.