| Course | MGT 646 Project Management Organizational Framework |
|---|---|
| Module | Module 3 |
| Paper type | MBA scope and WBS paper |
| Length | About 1,027 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Master of Business Administration |
| Updated | October 2026 |
Free sample paper for MGT 646 Module 3
What SmartSync Will and Will Not Do: A Scope Statement and Work Breakdown Structure With Room for Machine Learning
Student Name
Master of Business Administration, Aspen University
MGT 646: Project Management Organizational Framework
Instructor Name
Month Day, Year
What SmartSync Will and Will Not Do: A Scope Statement and Work Breakdown Structure With Room for Machine Learning
The SmartSync charter promises a privacy-first assistant that controls lights, locks and thermostats. That leaves a great deal open. Does it play music? Does it speak Spanish? Does it work with every lock on the market? Without firm answers, each conversation with a retailer, partner or engineer can add work no one budgeted. This paper writes the scope statement, sets its boundaries and builds the work breakdown structure. Details beyond the course case are assumptions.
The Scope Statement
SmartSync at launch is a countertop device with four far-field microphones, a speaker, a light ring and a privacy switch that cuts power to the microphones. It recognizes about 300 English commands on the device, controls lights, locks and thermostats from twelve named partner brands over Wi-Fi and a common smart home standard, and receives firmware and model updates over the air. A companion app handles setup and settings. Acceptance criteria include 95% command accuracy in the test lab's kitchen and living room scenarios, setup in under five minutes for 90% of beta households, no audio transmitted with default settings as verified by an outside researcher, and wireless certification granted.
What Is Out of Scope
Exclusions matter as much as inclusions. Music streaming, cameras, shopping by voice, a display, Spanish and support for brands beyond the twelve partners are out of scope for launch. Spanish and a second wave of partners are listed for a later release, so stakeholders who ask for them can be told when, not only no. Each exclusion was agreed with the sponsor and recorded so that later requests start from the same page.
The 100% Rule and Deliverables
PMI's practice standard (Project Management Institute, 2019) describes the WBS as the team's whole body of work, split level by level into smaller and smaller deliverables. Its central principle is the 100% rule: the WBS includes all the work defined by the scope, including project management, and nothing outside it, and the pieces under any element sum exactly to that element. The standard recommends decomposing by deliverables or outcomes and stopping at work packages small enough to estimate, assign and track.
Choosing a Structure
Globerson (1994) showed that the structure of a WBS is not neutral. The same project can be broken down by physical product, by function or department, or by life cycle phase, and each choice highlights some relationships and hides others. A structure by department makes each group's work clear but obscures deliverables that cross departments, which is where integration problems arise. Because SmartSync's hardest problems sit at the boundaries between hardware, firmware and models, the team chose a deliverable-based structure and assigned an owner to each branch who must coordinate across departments.
Machine Learning as Real Work
Amershi et al. (2019) studied software teams at Microsoft building machine learning applications and described a workflow running from model requirements through data collection, cleaning and labeling, feature engineering, training, evaluation, deployment and monitoring. They found that machine learning differs from other software in three ways: discovering, managing and versioning data is harder; model customization and reuse need different skills; and components are entangled, so changing one model can affect others. For project planning, this means data work cannot be hidden inside a software task. SmartSync needs recordings of commands in many accents and rooms, labeling, training, compression to fit the chip and testing against noise.
The Work Breakdown Structure
| Branch | Main work packages | Owner |
|---|---|---|
| 1 Project management | Planning, reporting, risk, change control | Project manager |
| 2 Hardware | Industrial design, electronics, microphones, enclosure tooling | Hardware lead |
| 3 Firmware | Device operating system, wake word, update client | Firmware lead |
| 4 Speech data and models | Data collection, labeling, training, compression, evaluation | Machine learning lead |
| 5 Companion app | Setup, settings, privacy controls | App lead |
| 6 Cloud update service | Signing, delivery, rollback | Firmware lead |
| 7 Partner integration | Twelve brand integrations, test devices | Partnerships manager |
| 8 Validation and certification | Three validation builds, wireless certification, safety listing | Quality engineer |
| 9 Manufacturing readiness | Line setup, test fixtures, pilot run | Hardware lead |
| 10 Launch | Packaging, retail kits, support scripts | Sales director |
The WBS Dictionary
Each work package has a dictionary entry so that the owner, the estimate and the meaning of done are written down. Package 4.2, labeling, reads: label 40,000 recorded command clips by intent and noise condition; owner, data operations analyst; acceptance, a second labeler agrees on a random 5% sample at least 97% of the time; estimate, ten weeks for two analysts and a contract labeling firm. Package 2.4, enclosure tooling, names the tooling vendor, the number of mold revisions allowed in the price and the sign-off by the industrial designer. Entries like these turn the WBS from a picture into a planning tool, because estimates, budgets and the schedule in later modules are built from them.
Where Scope Is Most Likely to Grow
Two branches carry most of the risk of growth. Partner integration tends to expand because every retail buyer has a favorite lock or thermostat brand, and the list of twelve will be tested often. Speech data tends to expand because each failed test suggests collecting more recordings. The team set limits in advance: a new partner brand requires dropping one or moving it to the later release, and additional data collection beyond 40,000 clips needs the change board's approval with an estimate of the accuracy gain expected.
Controlling Scope
Scope requests arrive constantly, from engineers who see an easy addition, from retailers and from beta households. Requests are logged in one place. The product owner decides on changes within a sprint's existing work. Changes that touch hardware, certification, cost or the launch date go to a change board of the sponsor, the project manager and the relevant leads, which meets after each sprint review. Approved changes update the WBS and its dictionary.
Conclusion
PMI's 100% rule keeps the WBS complete, Globerson's finding supports organizing it around deliverables, and Amershi and colleagues explain why speech data and models need their own branch. The scope statement and exclusions give the team a shared answer to the question of what SmartSync is.
References
Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (pp. 291-300). IEEE. https://doi.org/10.1109/ICSE-SEIP.2019.00042
Globerson, S. (1994). Impact of various work-breakdown structures on project conceptualization. International Journal of Project Management, 12(3), 165-171. https://doi.org/10.1016/0263-7863(94)90032-9
Project Management Institute. (2019). Practice standard for work breakdown structures (3rd ed.). Project Management Institute.
Reading the MGT 646 Module 3 assignment instructions
In MGT 646, Module 3 commonly asks students to define the SmartSync project's scope and decompose it into a work breakdown structure. Follow the Module 3 page and case file in your classroom; this example fills gaps with stated assumptions. Write a scope statement with deliverables and acceptance criteria. List what is out of scope. Choose how the WBS will be organized and explain why. Decompose the work to a level that can be estimated and assigned. Explain how scope will be controlled. Cite sources on scope and decomposition in APA 7 form, and keep the scope consistent with the charter from Module 1. Include a sample WBS dictionary entry to show the level of detail behind each package.
How this MGT 646 Module 3 example is built
The scope statement commits to a countertop device that controls lights, locks and thermostats by voice in English, runs speech recognition on the device and receives updates over the air. Out of scope at launch are music streaming, cameras, shopping by voice and Spanish, which is planned for a later release. PMI's Practice Standard for Work Breakdown Structures sets out the 100% rule and deliverable-oriented decomposition. Globerson's International Journal of Project Management article found that structuring a WBS by product, function or phase changes how a project is understood. Amershi and colleagues' study at Microsoft describes the stages of machine learning work, from data collection to model monitoring. The WBS table has ten branches, including a separate branch for speech data and models, and a change board reviews scope requests at each sprint review.
MGT 646 Module 3 rubric: what earns full marks
Scope papers score well when the boundaries are explicit, the WBS is complete and its structure is a deliberate choice. This example states deliverables with acceptance criteria and lists exclusions so later disputes can be settled. PMI's standard guides decomposition, Globerson's finding justifies organizing by deliverable, and Amershi and colleagues explain why machine learning work deserves its own branch. The scope control process connects the WBS to the hybrid life cycle chosen in Module 1, which shows the course project developing as one piece of work.
Common MGT 646 Module 3 mistakes, and how to avoid them
Scope papers often list features without acceptance criteria or exclusions. Say what done means for each deliverable and what the project will not deliver. Another weakness is a WBS organized by department, which hides deliverables that span teams; organize by deliverable and explain the choice. Apply the 100% rule, including project management and launch work. Decompose to work packages that can be estimated and given an owner. Treat data and model work as real work with its own estimates. Finally, describe the route a scope change takes from request to decision. Predict which branches are most likely to grow and set limits for them in advance, since the project manager can resist pressure more easily with a rule agreed before the pressure arrives.
Write yours, or have the desk draft it
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More MGT 646 and Master of Business Administration sample papers
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- MGT 646 Module 5: Cost and Budget
- MGT 646 Module 6: Risk Assessment
- MGT 646 Module 7: Execution and Quality
- MGT 646 Module 8: Monitoring and Launch Readiness
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- MGT 500 Module 6: Leading and Motivating Employees
MGT 646 Module 3 questions, answered
What does MGT 646 Module 3 usually ask for?
Aspen's MGT 646 commonly asks students to bound the SmartSync product and break its work into a WBS in this module. Start with your classroom prompt.
What is the 100% rule?
PMI's WBS standard holds that a work breakdown structure includes all of the project's work, and each level adds up exactly to the level above.
Does it matter how a WBS is organized?
Globerson found that organizing a WBS by product, function or phase changes how people understand and manage the project.
Where can I find a free MGT 646 Module 3 sample paper?
The example above bounds the SmartSync AI home assistant's launch features and breaks the work into ten deliverable branches.
Why give machine learning its own WBS branch?
Amershi and colleagues found machine learning work involves data collection, labeling, model training and monitoring that ordinary software plans tend to miss.