Production/Manufacturing

APS (Advanced Planning & Scheduling)

Simulates and optimizes production and material requirement plans under constrained resources and delivery conditions.

SCOR (Supply Chain Reference Model)

Are you facing these challenges?

1. You are still building production plans in spreadsheets

You consolidate order status and enter production schedules into Excel, manually verify equipment utilization and worker availability, then revise the spreadsheet again. When a rush order comes in, the entire schedule must be rebuilt from scratch, and orders get lost in the process.

2. You lack confidence in delivery date commitments

When a customer asks, "When can you deliver this quantity?" the sales team calls the production team, who manually check inventory and equipment status before responding. This process takes hours to days, and there is little certainty that the committed delivery date will actually be met.

3. You rely on intuition to identify bottleneck processes

There is experiential knowledge on the shop floor -- "that machine is always backed up" -- but bottlenecks are not identified based on actual utilization rates and queue time data. When it comes to investment decisions such as adding equipment or adjusting shifts, objective evidence is lacking.

4. Running MRP does not produce executable plans

When MRP is executed in the ERP system, material requirements are calculated, but without considering equipment capacity constraints, the resulting plans are infeasible. Production planners end up manually adjusting schedules, and trust in MRP results erodes.

5. S&OP meetings are conducted as a formality

At monthly S&OP meetings, sales brings their numbers and production brings theirs, but the two never align. Without the ability to simulate financial impacts, decisions rely on gut feeling, and meeting outcomes rarely translate into action.

Here is how we solve it

From demand forecasting to detailed scheduling -- connected in a single chain

S&OP -> MRP -> Finite Capacity Scheduling flows seamlessly end to end.

Most APS solutions have demand planning and detailed scheduling as separate modules, causing data gaps and delays during handoffs. In this system, 9 workflows automatically link data from demand forecasting -> S&OP decision-making -> MRP explosion -> finite capacity scheduling.

Demand forecasting: 5 forecasting methods (moving average, exponential smoothing, regression analysis, AI prediction, manual input)
Hierarchical forecasting: Product family -> SKU-level top-down disaggregation
Real-time demand sensing: Early detection of deviations between forecast and actual demand
Automatic MAPE (Mean Absolute Percentage Error) tracking for forecast accuracy management

S&OP meetings become a venue for data-driven decision-making

The APICS standard 5-step S&OP process is implemented as a workflow.

The 5 steps -- Data Collection -> Demand Review -> Supply Review -> Pre-S&OP -> Executive S&OP -- proceed in sequence, with a structure where the CFO and COO review financial and operational scenarios in parallel. Key KPIs including MAPE, Bias, Fill Rate, OTD (On-Time Delivery), and Schedule Adherence are automatically aggregated for immediate reference during meetings.

5-step process: Data Collection -> Demand Review -> Supply Review -> Pre-S&OP -> Executive S&OP
Financial/operational parallel scenario review (parallelGateway)
Automatic KPI aggregation: MAPE, Bias, Fill Rate, OTD, Schedule Adherence
Executive decision history management

MRP results become executable plans

Finite capacity scheduling integrated with MRP reflects equipment capacity constraints.

After multi-level BOM explosion, phantom BOM handling, and Net Change MRP execution, results flow into the finite capacity scheduler. The scheduler considers available capacity by work center, shift calendars, and mold/tooling constraints to produce feasible schedules.

Multi-level BOM explosion + Pegging support
Net requirements calculation + 10 lot-sizing methods (L4L, EOQ, POQ, etc.)
Automatic tracking of Plan Nervousness Index
Plan stability management across 3 zones: Frozen/Slushy/Free

Confirm and respond to customer delivery dates in real time

3-step ATP/CTP verification improves delivery date commitment accuracy.

When a customer delivery inquiry comes in, the system automatically checks: Step 1 -- current inventory (ATP), Step 2 -- production feasibility (CTP simulation), Step 3 -- inventory and production capacity at other plants (multi-site substitution), then returns the feasible delivery date. Sales representatives can get answers directly from the system without calling the production team.

ATP: 3 methods -- cumulative, discrete, backward
CTP: Delivery date calculation based on production simulation
Multi-site substitution: Leveraging inventory and production capacity at other plants
Customer delivery response process (WF7) automation

Identify and improve bottleneck processes with data

Equipment utilization, queue times, and bottleneck status are automatically analyzed.

Utilization rates and queue times for each work center are monitored in real time, and bottleneck status (is_bottleneck) is automatically detected. For processes identified as bottlenecks, improvement measures such as overtime deployment and SMED setup optimization can be simulated.

SMED 4-stage analysis: Internal/external task separation, improvement action tracking
Setup groups: Similar item sequencing based on GT (Group Technology) codes
Setup sequencing: Optimal sequencing via TSP/Greedy algorithms
What-If scenarios: Investment ROI simulation for CNC additions, shift changes, etc.

6 dispatching rules and multiple optimization algorithms

Select the scheduling rules that match your shop floor conditions.

Choose from 6 dispatching rules -- SPT (Shortest Processing Time), LPT (Longest Processing Time), EDD (Earliest Due Date), FIFO (First In First Out), CR (Critical Ratio), and Weighted -- and apply Forward, Backward, or Bidirectional scheduling. A dual optimization engine featuring NSGA-II (multi-objective genetic algorithm) and CP-SAT (constraint programming) is built in, with Bayesian parameter tuning for automatic algorithm performance adjustment.

Plans are automatically readjusted even in emergencies

Automatic response to 4 types of events: equipment breakdown, rush orders, material delays, and quality rejections.

When an unexpected situation arises, the event-driven rescheduling workflow (WF9) is automatically triggered. Plans within the Frozen Zone (confirmed horizon) are protected while only affected orders are selectively rescheduled. Automatic alerts (WF8) and escalation are triggered upon constraint violations.

Global standards this solution follows

This section introduces the core theoretical frameworks you should know in the APS (Advanced Planning & Scheduling) domain, and explains what each standard means for your production planning operations. APS is designed on the basis of multiple production management theories, and implementing these theories in a system enables the transition from "plans based on intuition" to "plans based on data."

APICS/ASCM Theory -- The International Common Framework for Production Planning

Why does this standard matter?

APICS (now ASCM, Association for Supply Chain Management) is the organization that defines global standard theories for production and supply chain management. Concepts such as MRP, MPS, CRP, S&OP, and ATP were all systematized by APICS. Using an APS that follows these theories means your production planning processes operate on the same theoretical foundation as global manufacturers. The concepts that production managers study when pursuing CPIM (Certified in Planning and Inventory Management) certification are implemented directly in the system.

How is this applied in VEXPLOR?

APICS Standard Theory	What it means for your operations	VEXPLOR Implementation
MRP/MRP II (Material Requirements Planning)	Calculate when and how much material to procure by exploding the BOM	aps_material_requirements, mrp_runs, mrp_results -- Multi-level BOM explosion, net requirements, pegging
MPS (Master Production Schedule)	Determine weekly production quantities based on demand	aps_production_plan (plan_type='MPS') -- Master production schedule based on demand orders
CRP (Capacity Requirements Planning)	Verify whether planned production is feasible within actual equipment capacity	aps_capacity_constraints, aps_bottleneck_analysis -- Load analysis by work center, automatic bottleneck identification
S&OP (Sales & Operations Planning)	Cross-functional demand-supply balancing decisions across sales, production, and finance	sop_meetings, sop_decisions + WF6 -- APICS 5-step S&OP process implementation
ATP/CTP (Available/Capable-to-Promise)	Respond to customer delivery inquiries with accurate feasible dates	aps_atp_records, WF7 -- 3-step verification: Inventory -> CTP Simulation -> Multi-site
Finite Capacity Scheduling	Create executable schedules reflecting equipment constraints	aps_scheduling_results, aps_scheduling_rules -- 6 dispatching rules, Forward/Backward/Bidirectional
DRP (Distribution Requirements Planning)	Optimize inventory transfers across multiple plants and warehouses	aps_multi_plant_transfers, aps_network_optimization -- Multi-plant production allocation, transportation cost matrix

TOC (Theory of Constraints) -- A Systematic Approach to Bottleneck Management

Why does this standard matter?

The Theory of Constraints, created by Dr. Eliyahu Goldratt, starts from the principle that "a factory's throughput is determined by its slowest process (the bottleneck)." The 5-step process for finding and improving bottlenecks (Identify -> Exploit -> Subordinate -> Elevate -> Repeat) is recognized as one of the most practical improvement methodologies in manufacturing. It enables investment decisions such as equipment additions or shift adjustments to be made based on data rather than intuition.

How is this applied in VEXPLOR?

TOC Step	What it means for your operations	VEXPLOR Implementation
Identify (Find the Bottleneck)	Determine which equipment or process is delaying overall production	aps_bottleneck_analysis -- Automatic detection of utilization_rate, queue_time, and is_bottleneck
Exploit (Maximize Bottleneck Utilization)	Maximize utilization of the bottleneck process	Overtime deployment simulation, SMED setup optimization -- Maximizing bottleneck resource utilization
Subordinate (Align Non-Bottlenecks)	Synchronize non-bottleneck processes to bottleneck pace	Scheduling rules assign sequencing based on bottleneck, buffer management
Elevate (Expand Bottleneck Capacity)	Make investment decisions to expand bottleneck capacity	What-If scenario ROI analysis (e.g., CNC addition), Monte Carlo simulation

Lean Scheduling -- Setup Time Reduction and Flow Optimization

Why does this standard matter?

One of the core principles of Lean manufacturing is "waste elimination." One of the biggest sources of waste on the shop floor is changeover (setup) time, and SMED (Single-Minute Exchange of Die) is a methodology for systematically reducing this setup time. In an environment of increasing high-mix low-volume production, setup optimization directly impacts equipment utilization and on-time delivery rates.

How is this applied in VEXPLOR?

Lean Concept	What it means for your operations	VEXPLOR Implementation
SMED (Single-Minute Exchange of Die)	Separate internal tasks (requiring equipment stoppage) from external tasks (performable during operation) to reduce setup time	aps_smed_analysis -- 4-stage analysis (stage_1~4), internal/external task separation, improvement action tracking
Setup Groups	Sequence similar items consecutively to minimize changeover frequency	aps_setup_groups -- GT (Group Technology) code-based, differentiated intra-group and inter-group setup times
Lot Sizing	Optimize production batch sizes to match demand patterns	10 lot-sizing rules (L4L, EOQ, POQ, FOQ, FOP, etc.) balancing inventory and setup costs
Leveling (Heijunka)	Reduce production volume fluctuations to maintain stable flow	Indirect support through lot-sizing rules (explicit Heijunka board planned for future release)

How this differs from existing systems

A single chain from demand to the shop floor

Most APS solutions have Demand Planning (DP), Supply Network Planning (SNP), and Detailed Scheduling (PP/DS) as separate modules, making inter-module data synchronization a challenge. This system provides an end-to-end chain from S&OP -> MRP -> Finite Capacity Scheduling within a single solution, with 9 workflows automating data linkage.

Design on the canvas and deploy immediately

All existing APS solutions are code-based or configuration-based. This system lets you design the APS schema via drag-and-drop on a visual canvas and deploy automatically through a 6-stage pipeline. Even non-specialists can visually configure tables, relationships, and workflows, dramatically lowering the barrier to APS adoption.

Advanced simulation capabilities are included out of the box

Features classified as premium add-ons even in enterprise APS solutions are included as standard.

Monte Carlo Simulation: Risk analysis based on probability distributions, percentiles, and confidence intervals
Network Optimization: MILP (Mixed-Integer Linear Programming), multi-plant production allocation, transportation cost optimization
Plan Stability Metrics: Nervousness Index, 3-zone management with Frozen/Slushy/Free zones
Scenario Comparison: Weighted sum or Pareto comparison of What-If scenarios

Immediate integration with ERP/MES

APS solutions require accurate data from ERP (items, BOM, inventory) and MES (production actuals) to generate reliable plans. Since ERP and MES are already integrated within the same platform, data flows in real time without the need for separate interface development.

Integration Target	Key Data Exchange
APS <-> ERP	Item/BOM/inventory/demand order reference, inventory reservation <-> plan linkage, MRP results -> planned orders/purchase requests
APS <-> MES	Scheduling results -> work orders, production actuals -> schedule feedback, equipment status reference
APS <-> QMS	Quality rejection events -> rescheduling triggers
APS <-> EAM	Equipment breakdowns/maintenance schedules -> available capacity adjustments

How does it compare to global solutions?

Compared to SAP APO/IBP

SAP's planning solutions (transitioning from APO to IBP) are the flagship solutions in the global APS market. The table below summarizes the coverage by SAP module and what it means for your operations.

SAP Module	VEXPLOR Coverage Area	Coverage	What it means for you
DP (Demand Planning)	Demand forecasting, demand sensing, demand orders	85%	Demand planning with 5 forecasting methods and hierarchical forecasting
SNP (Supply Network Planning)	Network optimization, multi-plant transfers	75%	MILP-based multi-plant production allocation available; limited for global-scale networks
PP/DS (Production Planning/Detailed Scheduling)	Scheduling results, optimization engine, Gantt	90%	Executable plans with finite capacity scheduling and multiple optimization algorithms
gATP (Global ATP)	ATP records, ATP allocation rules, WF7	85%	Accurate delivery commitments via 3-step Inventory -> CTP -> Multi-site verification
IBP S&OP	S&OP meetings, S&OP decisions, WF6	80%	APICS 5-step S&OP operated systematically through workflows
IBP Response Management	Event rescheduling (WF9), constraint violation alerts (WF8)	80%	Automatic response to 4 event types including equipment breakdowns and rush orders
IBP Inventory	Safety stock policies, ATP inventory snapshots	70%	Safety stock management available; advanced inventory optimization planned for future development

Overall coverage compared to SAP IBP: approximately 81%

What SAP has that VEXPLOR currently lacks: Global-scale supply chain network optimization, advanced inventory optimization, IBP-level strategic-operational integrated planning

What VEXPLOR has that requires separate purchase or premium add-ons in SAP: No-Code canvas design, Monte Carlo simulation included as standard, SMED 4-stage analysis, plan stability metrics (Nervousness Index)

Compared to Siemens Opcenter APS, Asprova, and PlanetTogether

A feature-by-feature comparison with 4 global APS vendors. This is organized around the practical differences you will experience when selecting an APS.

Comparison Area	SAP IBP	Opcenter APS	Asprova	PlanetTogether	VEXPLOR	What it means for you
Demand Forecasting	World-class	Basic	Strong	Basic	Strong	5 forecasting methods and demand sensing meet practical requirements
S&OP	World-class	Basic	Limited	Limited	World-class	APICS 5-step S&OP operated at the same level as SAP IBP
Finite Capacity Scheduling	Strong	World-class	World-class	World-class	World-class	6 rules + dual optimization engine on par with competing solutions
ATP/CTP	World-class	Basic	Basic	Basic	World-class	3-step ATP/CTP delivers delivery commitment accuracy at SAP level
What-If/Simulation	World-class	Strong	Strong	Strong	World-class	Monte Carlo simulation included as standard
Setup Optimization/SMED	Basic	World-class	World-class	Strong	World-class	SMED 4-stage + GT-code-based setup groups for setup time reduction
Network Optimization	World-class	Basic	Limited	Limited	Strong	MILP-based multi-plant allocation available; SAP leads at global scale
Monte Carlo Simulation	Strong	Basic	Limited	Limited	Strong	Probability distribution/percentile-based risk analysis as a standard feature
Gantt/Visualization	Strong	World-class	World-class	World-class	Strong	Gantt visualization provided; room for enhancement in Gantt interactivity compared to specialized APS tools
No-Code Canvas Design	Not available	Not available	Not available	Not available	Only in the industry	Visual design capability while all competitors are code/configuration-based

VEXPLOR APS Positioning

VEXPLOR APS offers feature scope comparable to SAP IBP while providing a scheduling engine on par with Opcenter APS and Asprova. On top of this, the unique differentiator of a No-Code canvas dramatically lowers the barrier to APS adoption. However, global-scale supply chain network optimization and interactive Gantt chart manipulation capabilities are areas that require further development.

Expected benefits after implementation

Delivery Management

Delivery commitment response time: With automated ATP/CTP verification, response time to customer delivery inquiries is reduced from hours or days to minutes.
Improved on-time delivery rate: Finite capacity scheduling produces executable plans, reducing deviations between planned and actual performance.
Rush order response: Event-driven rescheduling minimizes the impact on existing orders when rush orders are inserted.

Equipment Efficiency

Bottleneck improvement: Data-driven bottleneck identification and SMED analysis enable systematic reduction of setup times. Industry case studies from companies that have adopted SMED report setup time reductions of 40-60%.
Improved equipment utilization: Finite capacity scheduling reduces both equipment overload and idle time, improving overall utilization.

Inventory Management

Optimized inventory levels: Linked MRP and safety stock policies simultaneously reduce excess inventory and material shortages.
Multi-plant inventory utilization: Multi-site ATP leverages inventory at other plants to accelerate delivery dates.

Decision-Making Quality

Enhanced S&OP effectiveness: Data-driven scenario reviews ensure that S&OP meeting decisions translate into action.
Investment decision evidence: What-If simulations and ROI analysis provide objective evidence for investment decisions such as equipment additions or shift adjustments.
Risk assessment: Monte Carlo simulation quantifies plan uncertainty, enabling proactive risk identification.

Key Functional Areas

Demand Forecasting

Supports 5 forecasting methods (moving average, exponential smoothing, regression analysis, AI prediction, manual input). Hierarchical forecasting from product family to SKU level enables top-down disaggregation, and real-time demand sensing captures deviations between forecast and actual demand early. Forecast accuracy is automatically tracked via MAPE.

Production Scheduling

6 dispatching rules with Forward/Backward/Bidirectional scheduling. Dual-engine optimization with NSGA-II (multi-objective genetic algorithm) and CP-SAT (constraint programming), with Bayesian tuning for automatic parameter adjustment. Gantt chart visualization for intuitive review of scheduling results.

Constraint Management

10 constraint types managed in 3 tiers: Hard (inviolable), Soft (penalty on violation), and Preference. Reflects 5 resource constraints: available capacity by work center, shift calendars, mold/tooling availability, material availability, and worker skills. Automatic resolution strategies are applied on constraint violations, with violation history and escalation management.

Scenario Analysis

Provides What-If scenarios and Monte Carlo simulation. Scenario comparisons are performed using weighted sum or Pareto methods, and parameter sensitivity analysis identifies which variables have the greatest impact on results.

Network Optimization

Supports MILP (Mixed-Integer Linear Programming) based production allocation optimization in multi-plant environments. Calculates cost-minimizing production allocation across the entire network, incorporating inter-plant transfers, transportation cost matrices, and service level constraints.

Real-World Business Scenarios

Scenario 1: Rush Order Insertion

A rush order is received from the sales team. The system checks ATP, determines that current inventory cannot meet the delivery date, and runs a CTP simulation. The list of existing orders affected by inserting the rush order into the current schedule, along with each order's delivery date change, is displayed immediately. The production planner reviews and approves the rescheduled plan where only orders outside the Frozen Zone have been rearranged. The sales team then confirms the delivery date to the customer.

Scenario 2: Equipment Breakdown Response

A CNC machine goes down for 2 days due to breakdown. Event-driven rescheduling (WF9) is automatically triggered, reassigning orders allocated to that machine to alternative equipment or adjusting schedules. If no alternative equipment is available, a list of orders with impacted delivery dates and estimated delay days is sent as an alert, enabling the sales team to proactively inform customers.

Scenario 3: Monthly S&OP Meeting

The S&OP workflow automatically initiates the data collection phase. Sales demand forecasts, production capacity plans, and finance revenue/profit simulations are prepared in parallel. At the Pre-S&OP stage, gaps between supply and demand are automatically identified, and 3 scenarios (maintain status quo, increase production, outsource) are presented in a comparison table. The final decision is made at the Executive S&OP, and the decision is automatically reflected in MRP and the scheduler.

Ideal for these companies

Make-to-Order (MTO) manufacturers where delivery management is a core competitive advantage
High-mix low-volume manufacturers where setup optimization and scheduling are critical
Companies operating multiple plants that need to optimize production allocation across facilities
Companies seeking to systematize their S&OP process to improve alignment among sales, production, and finance
Companies transitioning from spreadsheet-based production planning to system-based planning
Tier 1 automotive parts OEM suppliers: Environments with strict customer delivery requirements where high-mix low-volume production and setup optimization are essential

Technical Foundation

Item	Details
Data Model	Approximately 69 tables, 100+ relationships
Workflows	9 predefined (S&OP, MRP conversion, ATP verification, rescheduling, etc.)
Optimization Engine	NSGA-II (genetic algorithm), CP-SAT (constraint programming)
Simulation	Monte Carlo, What-If, sensitivity analysis
Network Optimization	MILP-based multi-plant production allocation
Automatic Calculations	18+ computed columns
ERP Integration	Required dependency (items/BOM/inventory/demand orders)
Deployment Method	6-stage automated deployment (DDL -> API -> UI -> Menu -> Route -> Artifact)

Try it yourself

Apply the APS (Advanced Planning & Scheduling) template on the canvas, and data models to screens are auto-generated.

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