Supply Chain Simulation & Optimization Studio Pro

Exhaustive Data Tables, Column Definitions & Methodology Manual | PDF Edition

1. Executive Overview & Integrated Workflow Methodology

Supply Chain Studio Pro integrates analytical optimization with discrete-event simulation to address strategic, tactical, and operational supply chain challenges across multi-echelon networks.

      End-to-End Methodological Triad:
      
      Greenfield Analysis (GFA) → Network Optimization (NO) → Dynamic Simulation (SIM)
      
      1. GFA determines unconstrained, ideal geographic Center of Gravity (CoG) facility locations.

      2. NO solves Mixed-Integer Master Planning (MILP) subject to capacities, costs, and multi-echelon flow bounds.

      3. SIM validates operational execution under stochastic demand, inventory control policies, lead time jitter, and risk disruptions.

2. Deep-Dive Methodological Reference (GFA, NO, SIM Engines)

2.1 Greenfield Analysis (GFA) Engine

Purpose & Managerial Use: Greenfield Analysis evaluates pure spatial geometry and demand-weight distributions. It answers the fundamental strategic question: "If we could build N new distribution centers anywhere in the world without existing facility constraints, where should they be located to minimize transportation distance?"

Mathematical Formulation: Solves the continuous location-allocation Weber problem using Weiszfeld iteration and K-Means spatial clustering:
min ∑ w_i · d((x, y), (lat_i, lng_i)) where w_i is demand weight and d is distance.
Variant 1: Fixed Number of DCs (N): Finds optimal centroids for a user-defined candidate count N = 1 &dots; 10. Use this when capital expenditure limits you to building a specific number of warehouses.
Variant 2: Fixed Maximum Service Distance (km/mi): Automatically computes the minimum number of DCs required so that no customer exceeds a maximum service radius R_max (e.g. 350 km). Use this to guarantee strict lead time SLAs (e.g. same-day or next-day delivery).
Distance Metrics: Offers spherical Great Circle (Haversine formula) and Manhattan / Road Network Factor (1.25x) to model realistic highway detours.

2.2 Network Optimization (NO) Engine

Purpose & Managerial Use: Network Optimization applies Mixed-Integer Linear Programming (MILP) to model complex multi-echelon trade-offs between fixed facility lease costs, variable handling, freight tariffs, and production limits. It answers: "Which facilities should be open, closed, or expanded to maximize net profit while fulfilling all customer orders?"

Objective Function: Maximize Net Profit across the planning horizon:
Net Profit = Revenue - (Fixed Facility Lease + Transportation Tariffs + Inbound Handling + Production Costs + Late Penalties)
Decision Variables: Binary open/close status y_j ∈ {0, 1} for candidate DCs, and continuous multi-echelon flow volumes x_ijk ≥ 0 from suppliers to DCs to customers.
Constraints Enforced:
- Customer Demand Satisfaction: ∑ Flow = Customer Demand
- Facility Throughput Capacity: ∑ Outbound Flow ≤ Max Capacity · y_j
- Supplier Production Limit: ∑ Production ≤ Daily Capacity
Output Flow Matrix: Provides the optimal multi-echelon routing matrix indicating exact unit volumes and transport costs assigned to each path.

2.3 Dynamic Discrete-Event Simulation (SIM) Engine

Purpose & Managerial Use: Dynamic Simulation tests the operational reality of the supply chain day by day over time. While optimization models assume static averages, SIM tests stochastic variance—daily customer demand spikes, lead time delays, inventory stockouts, and unexpected risk disruptions.

Stochastic Demand & Lead Time Modeling: Daily demand is generated using Gaussian Normal distributions N(μ, σ) or Uniform distributions. Lead times incorporate transport speed and distance jitter.
Reproducibility via Random Seed (seed): Initializing the Pseudo-Random Number Generator (PRNG) with a specific seed (e.g. seed = 42) guarantees that identical daily random sequences are produced across different experiment runs for fair comparison.
Inventory Replenishment Policies Evaluated:
- (s, S) Min-Max Policy: Trigger replenishment when stock position I ≤ s up to target maximum S.
- (R, s, S) Periodic Policy: Every R days, review stock and reorder if I ≤ s up to S.
- (s, q) Fixed Order Policy: Reorder a fixed batch q when stock hits s.
Disruption & Risk Resilience Injection: Simulates real-world crises (Complete Facility Shutdown, 75% Capacity Drop, 300% Lead Time Surge) to evaluate Time-To-Recover (TTR) and financial loss exposure.

3. Complete Supply Chain Data Tables & Column Dictionary (All 10 Tables)

3.1 Customers & Demand Profile Table (`customers`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`id`	ID	Text Code	Unique alphanumeric identifier code for the customer location (e.g. `CUST_BER`).
`name`	CUSTOMER NAME	String	Descriptive name of the retail outlet, customer city, or regional market.
`lat`	LATITUDE (°)	Degrees	Geocoded latitude coordinate in decimal degrees (-90.0000 to +90.0000). Drag marker on GIS map to update.
`lng`	LONGITUDE (°)	Degrees	Geocoded longitude coordinate in decimal degrees (-180.0000 to +180.0000). Drag marker on GIS map to update.
`product`	PRODUCT SKU	SKU Code	SKU code of product demanded by this customer location (references `products.id`).
`demandType`	DISTRIBUTION TYPE	Enum	Stochastic probability distribution for daily order generation: `Normal` (Gaussian) or `Uniform`.
`demandMean`	DEMAND RATE	units/day, units/wk, units/mo, units/hr, tons/day	Average demand rate with user-editable time unit selector dropdown.
`demandStd`	DEMAND STD	units	Standard deviation of daily demand fluctuation (stochastic volatility).
`penaltyPerUnit`	PENALTY COST RATE	$/unit·day, $/unit·wk, $/unit·mo, $/unit·hr, €/unit·day	Financial penalty fee charged per unfulfilled backordered unit per delay time unit (user-editable time & currency).

3.2 Facilities & Distribution Centers Table (`facilities`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`id`	FACILITY ID	Text Code	Unique distribution center or warehouse identifier (e.g. `DC_FRA`).
`name`	FACILITY NAME	String	Name of warehouse facility or logistics depot.
`lat` / `lng`	LATITUDE (°) / LONGITUDE (°)	Degrees	Geocoded geographical position of facility. Draggable on interactive Leaflet maps.
`type`	CLASSIFICATION	Enum	Role of facility: `Warehouse`, `Central DC`, or `Regional Depot`.
`status`	STATUS CONSTRAINTS	Enum	Optimization constraint: `Existing` (must stay open) or `Candidate` (MILP solver decides to open/close).
`maxCapacity`	MAX CAPACITY	units	Maximum throughput storage & order handling capacity limit per period.
`fixedCost`	FIXED COST RATE	$/yr, $/mo, €/yr, ₹/yr	Annual fixed lease, utility, labor, and maintenance cost with user-editable time & currency selector.
`handlingCostPerUnit`	HANDLING COST	$/unit, €/unit, £/unit, ₹/unit	Variable cost incurred for receiving, cross-docking, picking, and packing per unit.
`initStock`	INITIAL STOCK	units	Opening inventory balance stored at facility on Day 0 of simulation.

3.3 Suppliers & Manufacturing Plants Table (`suppliers`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`id`	SUPPLIER ID	Text Code	Unique factory or manufacturing plant identifier (e.g. `SUP_STU`).
`name`	FACTORY NAME	String	Factory name or production facility description.
`lat` / `lng`	LATITUDE (°) / LONGITUDE (°)	Degrees	Geographical coordinates of manufacturing plant location.
`product`	PRODUCT SKU	SKU Code	Product SKU manufactured at this factory node.
`dailyCapacity`	PRODUCTION CAPACITY RATE	units/day, units/wk, units/mo, units/hr	Maximum daily production throughput output limit with user-editable time unit.
`unitProductionCost`	PRODUCTION COST	$/unit, €/unit	Direct manufacturing production cost per unit produced.

3.4 Products & Categories Table (`products`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`id`	PRODUCT ID	SKU Code	Unique product SKU identifier (e.g. `PROD_01`).
`name`	SKU NAME	String	Product description or item title.
`category`	CATEGORY	String	Category grouping (e.g. Electronics, FMCG, Apparel).
`unitVolume`	UNIT VOLUME	m³	Cubic volume occupied per unit item for fleet capacity verification.
`unitWeight`	UNIT WEIGHT	tons, kg, lbs	Physical weight per unit item with user-editable inline weight selector.
`costPrice`	UNIT COST PRICE	$/unit, €/unit	Wholesale purchasing cost price from supplier.
`sellingPrice`	SELLING PRICE	$/unit, €/unit	Outbound selling revenue price charged to customers per unit.
`holdingCostRatePerDay`	HOLDING COST RATE	$/unit·day, $/unit·wk, $/unit·mo, $/unit·hr, €/unit·day	Daily warehouse carrying cost per unit stored per day with user-editable time & currency selector.

3.5 Vehicle Fleet Types Table (`vehicles`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`id`	FLEET ID	Text Code	Fleet vehicle type code (e.g. `VEH_TRUCK_STD`).
`name`	VEHICLE DESCRIPTION	String	Vehicle description (40ft Truck, Express Van, Cargo Ship).
`capacityVol`	VOL CAPACITY	m³	Maximum volumetric payload capacity limit per vehicle.
`capacityWeight`	WEIGHT CAPACITY	tons, kg, lbs	Maximum payload weight capacity limit per vehicle with user-editable weight selector.
`speedKmH`	SPEED	km/h, mi/h	Average transit speed for calculating shipment travel time with user-editable speed selector.
`fixedCostPerTrip`	FIXED TRIP COST	$/trip, €/trip	Fixed dispatch handling fee incurred per vehicle trip.
`costPerKm`	FREIGHT RATE	$/km, $/mi, €/km	Variable freight cost per distance driven with inline tariff dropdown.
`co2PerKm`	CO2 EMISSIONS	kg CO₂/km, mi	Carbon emissions intensity factor per distance driven.

3.6 Paths & Freight Rates Table (`paths`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`source`	SOURCE NODE	Node ID	Origin facility or supplier ID.
`destination`	DESTINATION NODE	Node ID	Destination warehouse or customer region ID.
`vehicleType`	ASSIGNED FLEET	Fleet ID	Assigned vehicle fleet type (references `vehicles.id`).
`leadTimeDays`	LEAD TIME DURATION	days, weeks, months, hours	Transit lead time duration with user-editable time unit selector dropdown.
`transportCostPerKmTon`	FREIGHT TARIFF	$/ton·km, $/kg·km	Unit transportation cost per weight per distance driven.

3.7 Sourcing Policies Table (`sourcing`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`customer`	TARGET CUSTOMER	ID or ALL	Customer ID or `ALL` for global sourcing rule.
`policy`	SOURCING POLICY	Enum	Warehouse assignment rule: `MinCost` (lowest cost), `Closest` (shortest distance), `SingleSource`.
`minOrderQty`	MIN ORDER QTY	units	Minimum allowed order quantity constraint.
`batchSize`	BATCH SIZE	units	Order batch quantity multiplier.

3.8 Inventory Control Policies Table (`inventory`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`facility`	WAREHOUSE FACILITY	Facility ID	Facility ID where inventory policy applies.
`product`	PRODUCT SKU	SKU Code	Product SKU managed under this policy.
`policy`	CONTROL POLICY	Enum	Replenishment logic: `(s,S) Min-Max` or `(R,s,S) Periodic`.
`minLevel_s`	REORDER POINT s	units	Safety stock reorder point $s$. Trigger order when stock ≤ s.
`maxLevel_S`	MAX LEVEL S	units	Target maximum inventory level $S$. Order quantity $Q = S - I$.
`reorderQty_q`	REORDER QTY q	units	Fixed order batch size $q$ for $(s,q)$ policies.
`reviewPeriodR`	REVIEW PERIOD R	days, weeks, months, hours	Periodic review inspection interval $R$ with user-editable time unit dropdown selector.

3.9 Costs & Tariffs Table (`costs`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`id`	TARIFF ID	Text Code	Unique tariff ID.
`name`	COST DESCRIPTION	String	Cost description (Handling, Storage, Duty).
`costPrice`	COST RATE	$/unit, €/unit	Cost multiplier rate.
`sellingPrice`	SELLING RATE	$/unit, €/unit	Revenue rate.

3.10 Disruption Risks Table (`disruptions`)

Column Field	Explicit Header Name	Editable Unit Types	Detailed Description & Managerial Usage
`facility`	TARGET FACILITY	Facility ID	Target facility affected by risk event.
`startDay`	START TIME	days, weeks, months, hours	Start time of disruption event with user-editable time unit selector.
`endDay`	END TIME	days, weeks, months, hours	End time of disruption event with user-editable time unit selector.
`type`	DISRUPTION TYPE	Enum	Risk event classification: `shutdown`, `capacity_drop` (75% drop), `leadtime_surge` (+300% delay).
`description`	RISK DESCRIPTION	String	Explanation text logged in telemetry box.

4. Step-by-Step Practical Case Study Guide

Select Case Study 1: European Distribution Network from the top dropdown.
In Tab 2 (GFA), select Fixed Maximum Service Distance (350 km) and click Run Greenfield Analysis. Inspect recommended DC locations in Frankfurt, Prague, and Amsterdam.
In Tab 3 (NO), check Include GFA Candidate DCs and click Solve Optimization Model. Review optimal multi-echelon flow assignments and cost breakdown.
In Tab 4 (SIM), set start date (2026-01-01) and end date (2026-06-30), click Run Simulation, and watch live animated shipments transport orders across the Leaflet GIS map!

1. Executive Overview & Integrated Workflow Methodology

2. Deep-Dive Methodological Reference (GFA, NO, SIM Engines)

2.1 Greenfield Analysis (GFA) Engine

2.2 Network Optimization (NO) Engine

2.3 Dynamic Discrete-Event Simulation (SIM) Engine

3. Complete Supply Chain Data Tables & Column Dictionary (All 10 Tables)

3.1 Customers & Demand Profile Table (customers)

3.2 Facilities & Distribution Centers Table (facilities)

3.3 Suppliers & Manufacturing Plants Table (suppliers)

3.4 Products & Categories Table (products)

3.5 Vehicle Fleet Types Table (vehicles)

3.6 Paths & Freight Rates Table (paths)

3.7 Sourcing Policies Table (sourcing)

3.8 Inventory Control Policies Table (inventory)

3.9 Costs & Tariffs Table (costs)

3.10 Disruption Risks Table (disruptions)