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portfolio.exe sourcing.xls query_met.sql
Efficiency models!

MBA · Operations Transformation · Supply Chain · Digital Strategy

Fix the Ops. Scale the Business.

Aligning technical operations with growth strategy — Engineer with experience as a business account manager, leveraging advanced analytics (Excel/Power BI/SQL) to develop data-driven recommendations. MBA candidate at Broad College of Business with a specialization in Marketing and Supply Chain Management.

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Operational Overhead Reduced
Enterprise Performance Project
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Reporting time reduction
Risk Assessment Project
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Assets Produced
Dashboards Building Project
0%
Time-to-Market
New Product Strategy Project
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Media engagement growth
Targeted Headcount Project

Design Thinking.

My focus is on discovering oportunities to strategically design scalable business systems. Enhancing workflows through cross-functional teamwork and technology, and meticulously tracking client experiences for improvement metrics.

II. Case Studies

Real Problems.
Real Solutions

Focused on Business Process Re-engineering, Supply Chain Analytics, and Strategic Sourcing. Each card flips to reveal the full Problem–Action–Result breakdown.

Disclaimer: The case studies presented above are for academic and demonstrative purposes. Data and insights were synthesized from various public domain sources, industry reports, and hypothetical business scenarios to illustrate strategic framework application. All trademarks and brand assets belong to their respective owners.

III. Operations Simulators

Data in Action.
Interactive Models

Strategic growth relies on mastering the trade-offs between operational efficiency and financial performance.

Two interactive models built from real-world operations problems. Inventory Lab uses EOQ and safety stock logic applied at Ward Tires. Queue & Staffing Lab models the staffing trade-off from Six Flags — finding the right number of servers without over-spending on idle capacity.

Hypothetical Scenario
Ward Tires — Calgary Distribution Centre. You manage a mid-size tyre distributor with 12,000 units of your top-selling all-season SKU moving annually. Your supplier in Ontario ships in batches, taking 21 days on average (but sometimes 14, sometimes 28). Your warehouse costs 25% of unit value per year to operate, and each purchase order costs ~$150 in admin and receiving labour. At what order quantity do you minimise total inventory cost — and how much safety stock do you need to maintain a 95% fill rate through demand spikes?
Default inputs are pre-loaded from this scenario. Run the model to see the EOQ and cost curve.
01 Set demand & cost parameters
02 Define service & lead time targets
03 Run model — EOQ, safety stock & cost curve
A Demand ParametersHow much you sell and how variable that demand is
units/yr

Total units sold or consumed per year. At Ward Tires this was SKU-level tyre demand across trade channels.

σ units/day

How much daily demand fluctuates. Higher variability requires more safety stock to protect service level.

B Cost ParametersThe two competing costs that determine your optimal order quantity
$/order

Fixed cost each time you place an order — admin, freight set-up, receiving labour.

$/unit

Landed cost per unit. Used to calculate the holding cost on capital tied up in inventory.

% / yr

Cost to store one unit for one year as a % of its value. Typically 20–35% including capital, warehouse, insurance, and obsolescence.

C Supply & Service TargetsLead time and the service level you're committing to customers
days

Average days from purchase order to stock arriving. Longer lead time = more stock needed to bridge the gap.

σ days

How unpredictable your supplier's delivery is. High sigma = wide safety stock buffer needed.

%

The % of demand cycles you want to fulfil without a stockout. 95% = 1 stockout in 20 order cycles.

Model not yet run

Fill in your parameters on the left and click Run Model to generate your inventory optimisation analysis.

Hypothetical Scenario
Six Flags Great Lakes — Peak Saturday Operations. It's a summer Saturday and the park's flagship coaster is running at full capacity. On average, a new guest joins the queue every 2.5 minutes. Each boarding cycle takes about 5 minutes per customer. Management has deployed 3 ride operators to handle loading, safety checks, and dispatch. By 11am the queue is visibly growing. Do you add a 4th operator, or is the system still within tolerance?
Default inputs are pre-loaded from this scenario. Run the simulation to see real-time queue dynamics.
01Set arrival rate & service time
02Set number of servers & run time
03Run simulation — see queue & utilisation
AArrival & DemandHow often customers / jobs arrive
min / arrival

One new customer every X minutes on average.

minutes

Total operating window to simulate. 240 = a typical 4-hour peak period.

BService ParametersHow long it takes to serve each customer
min / customer

Average time one server spends with one customer.

servers

Parallel servers working simultaneously. Increase this to see the effect on wait time.

$/hr

Hourly labour cost per server. Used to calculate total staffing cost vs. wait-time trade-off.

Simulation not yet run

Configure arrival rate, service time and staff count. Click Run Simulation to see queue dynamics.

Hypothetical Scenario
TechSmith Corporation — Acquisition Valuation. During the Broad Consulting Club engagement, you're tasked with building a preliminary DCF for a mid-market B2B software target. The company is generating $5M in revenue with strong early-stage growth (~18% per year). EBITDA margins are at 22%. The acquirer's WACC is 10%, and comparable SaaS transactions are clearing at 8× EBITDA. Does the current growth trajectory support the acquisition price?
Default inputs are pre-loaded from this scenario. Run the model to see NPV, IRR and the valuation football field.
01Enter revenue & cost projections
02Set discount rate & market assumptions
03Run model — NPV, IRR & valuation range
ARevenue ProjectionsBase year revenue and growth assumptions
$K

Current / Year 0 annual revenue in thousands.

% / yr

High-growth phase reflecting market penetration or new product revenue.

% / yr

Maturity phase — business stabilises toward a steady-state growth rate.

BCost StructureMargins and capital requirements
%

Earnings before interest, tax, D&A as % of revenue.

%

Non-cash charge added back to get to free cash flow.

%

Capital expenditure required to sustain growth.

%

Incremental working capital needed per dollar of revenue growth.

CMarket AssumptionsDiscount rate, terminal value and tax
%

Weighted average cost of capital. Typically 8–14% for mid-market companies.

%

Perpetuity growth rate beyond Year 5. Should be at or below long-run GDP growth.

%

Effective corporate tax rate. US federal + state blended: ~26–28%.

×

Market comparable exit multiple for terminal value cross-check.

Model not yet run

Fill in revenue projections, cost structure, and market assumptions. Click Run DCF Model to generate NPV, IRR and valuation range.

III. Skills & Tools

Core Competencies.

Drag the cursor across the pit — the balls scatter. Highlighted balls are your core competencies.

Let's Connect

Struggling with Execution?
Let's Connect!

A. My foundation in Civil Engineering allows me to understand the complex infrastructure and capital-intensive nature of the Utilities sector, while my MBA and past professional experience in the automotive sector provides the framework to translate those technical realities into C-suite value. I specialize in taking "bottom-up" data and turning it into operational strategies that drive measurable margin growth.

A. It is about creative autonomy and systemic understanding. Since 2020, I have operated at the intersection of design and digital commerce—building custom landing pages for retail stores and launching Shopify ecosystems for entrepreneurs. Assisting with targeted ads, SEO Google Analytics, and customer journey mapping.

By coding this portfolio from first principles, I am able to "freewill" the design to match my strategic brand while demonstrating a fundamental truth: I understand the technical architecture behind the articles and simulation models I create. In a world of templates, I choose to build the system.

A. A calculated risk I took was at Ward Tires, where I drove the shift toward a Just-In-Time (JIT) delivery model. In the Canadian tire market, where seasonal demand is highly volatile and winter weather constantly threatens supply lines, stripping away safety stock is a massive commercial risk. To mitigate this, I didn't just arbitrarily cut inventory—I completely restructured our vendor development strategy. By collaborating directly with upstream supplier R&D teams and running rigorous cost models, we executed the shift successfully. The result was a 10–15% reduction in stockouts, improving shelf availability during our peak seasons.

A. I am a Canadian Citizen eligible for TN (Trade NAFTA) status under the USMCA. This allows for immediate work authorization upon a job offer in categories like Management Consultant or Engineer. This process is completed directly at the port of entry, bypassing the H-1B lottery and traditional sponsorship costs, making me available to join US-based teams with minimal lead time.

Punch Kokkalemada with Sparty at Michigan State University
Current Base East Lansing, MI
System Status: Active Open to Full-Time · May 2026