DBA 8060 Module 6 Optimization and Simulation Example

Course	DBA-8060 Empirical Entrepreneurship Data, Technology, and Business Intelligence
Module	Module 6
Paper type	Module 6 written assignment: prescriptive analytics, optimization and simulation
Length	About 506 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 6

Stock Where the Uncertainty Is: An Optimization and Simulation of Safety Stock at DAKOKOO

Student Name

Doctor of Business Administration, Thomas Edison State University

DBA-8060: Empirical Entrepreneurship: Data, Technology, and Business Intelligence

Mentor Name

Month Day, Year

What this page is doingThe title states the policy's logic in a phrase, holding buffer stock where supply is least predictable.

Stock Where the Uncertainty Is: An Optimization and Simulation of Safety Stock at DAKOKOO

Introduction

DAKOKOO holds four weeks of safety stock for every imported item, whatever its demand or supplier. Earlier modules showed that stockouts follow unpredictable lead times, and a model now flags items at risk. Prediction alone does not say how much stock to hold. That is the task of prescriptive analytics.

From Prediction to Prescription

Sharda et al. (2018) describe prescriptive analytics as the use of optimization and simulation to recommend actions, building on descriptive and predictive work. Bertsimas and Kallus (2020) show how predictive models and optimization can be joined, using data on the factors that drive uncertainty to make better decisions than optimizing on average forecasts alone. Their point applies directly: a safety stock policy should use what is known about each supplier's variability rather than one rule for all.

The Optimization Model

The decision is the safety stock for each of 1,200 imported items. Every item gets its own buffer, yet all 1,200 compete for one pool of shelf space and cash, so the model sets them jointly. The objective is to minimize the total of holding cost, at 22 percent of item value a year, and expected expedite cost, based on last year's average of $1,850 per expedited shipment. The constraint is an overall fill rate of at least 98 percent across retail orders, weighted by volume, with a floor of 95 percent for any single item. Silver et al. (2017) give the standard result that safety stock should grow with the variability of demand during lead time, which depends on both demand variability and lead-time variability. The model uses each item's demand history and its supplier's lead-time spread, both now available after the text mining work.

What this page is doingStating the decision, objective and constraints separately makes the model readable to managers who will never see the solver.

Testing the Policy by Simulation

An optimization solved on estimated averages can mislead when the inputs are uncertain. I therefore simulated 5,000 seasons, drawing weekly demand and each order's lead time from distributions fitted to past data, and compared the optimized policy with the current rule.

The optimized policy holds slightly more stock overall but places it differently: more for items from the eleven suppliers with the widest lead-time spread and less for stable domestic suppliers. Expedite costs fall by about half.

Measure (simulated annual mean)	Flat four-week rule	Optimized policy
Fill rate	95.1 percent	98.2 percent
Safety stock value	$14.6 million	$15.3 million
Expedited shipments	2,210	1,040
Holding plus expedite cost	$7.30 million	$5.29 million

Limits and Use

The results depend on the fitted distributions, and a disruption outside past experience, such as a port closure, would break them. Expedite cost per shipment is an average and may rise with fuel prices. I recommend applying the policy to one distribution center for a quarter and comparing actual expedites and fill rates with the simulation before extending it.

Conclusion

Prescriptive analytics turns the earlier findings into a decision: hold stock where the uncertainty is. Optimization sets the policy, simulation tests it against uncertainty, and a pilot will test it against reality.

References

Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044. https://doi.org/10.1287/mnsc.2018.3253

Sharda, R., Delen, D., & Turban, E. (2018). Business intelligence, analytics, and data science: A managerial perspective (4th ed.). Pearson.

Silver, E. A., Pyke, D. F., & Thomas, D. J. (2017). Inventory and production management in supply chains (4th ed.). CRC Press.

What the DBA 8060 Module 6 instructions ask for

Module 6 of DBA-8060 turns to prescriptive analytics through optimization and simulation, and this page sets one of the course's five written assignments against it; the syllabus leaves the order of assignments to the course calendar. Expect to explain how prescriptive analytics differs from descriptive and predictive work, formulate a model with a clear decision, objective and constraints, test it under uncertainty and interpret the results for managers. Sharda, Delen and Turban's chapter on optimization and simulation provides the methods. You do not need advanced mathematics to do this well, but you do need to state each part of the model in plain terms, give realistic figures and explain what the results depend on.

Inside the DBA 8060 Module 6 example

The paper links back to earlier modules, so the reader sees prescription as the next step after description and prediction. It defines prescriptive analytics from the textbook and draws on Bertsimas and Kallus for the case against one rule for all items. The optimization section separates the decision, objective and constraints, with costs and service targets given. Silver and colleagues supply the inventory result that safety stock should follow lead-time and demand variability. A simulation of 5,000 seasons tests the policy, and a four-row table compares it with the current rule. A limits section names the dependence on fitted distributions and proposes a one-center pilot. The pilot recommendation keeps the decision reversible until real expedite counts confirm the simulated gain.

Reading the DBA 8060 Module 6 grading rubric

DBA-8060 mentors grade this assignment on accurate concepts, a sound formulation, appropriate testing, interpretation for decision makers and scholarly support in APA format. A model whose parts are named in plain language shows understanding even without equations. Using simulation to test an optimized policy under uncertainty shows that the student knows the limits of optimizing on averages. Comparing the new policy with the current one, in the measures managers use, shows practical value. Saying which inputs drive the result, and testing it first at one site, shows doctoral caution. Reporting the number of simulated runs and the source of each input distribution makes the test reproducible.

Common DBA 8060 Module 6 mistakes, and how to avoid them

Students lose marks by describing optimization and simulation without building any model, by leaving out constraints or by presenting a single answer as certain. Some confuse prediction with prescription and stop at a forecast. Name the decision, the objective and the constraints, with figures. Test the result against uncertainty, even with a simple simulation, and compare it with what the organization does now. Explain where the extra benefit comes from, not just its size. State what could break the model and how a pilot would check it. Send your own figures and a free first optimization draft for DBA-8060 can be built from them. A short table comparing the current rule with the optimized policy, in cost and fill rate, makes the case at a glance.

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.

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DBA 8060 Module 6 questions, answered

Where can I find a free DBA 8060 Module 6 sample paper?

The paper above optimizes and simulates safety stock for DAKOKOO Distribution's imported items.

What is prescriptive analytics?

Analytics that recommends actions, usually through optimization or simulation, building on descriptive and predictive analysis.

What are the parts of an optimization model?

Decision variables, an objective to maximize or minimize and constraints that limit the feasible choices.

Why use simulation after optimization?

Simulation tests how a policy performs when demand and other inputs vary, which an optimization on averages cannot show.

What drives safety stock?

Variability in demand during the lead time, which reflects both demand variability and lead-time variability, plus the service level targeted.

DBA 8060 Module 6 Written Assignment: Optimizing Safety Stock and Simulating Expedite Costs Example