01 — The big idea
Contract manufacturers are not paid for equipment. They are paid for decisions made correctly, thousands of times a week — which job runs next, which machine takes it, how much stock stands behind it.
Most of those decisions are still made on spreadsheets and experience. Simulation lets a plant test a decision before it commits to it.
02 — Executive summary
Nine decision engines, one method
This paper describes nine simulation-based planning tools built by Open Netrikkan, an AI-native discrete event simulation (DES) platform for manufacturing and logistics. Each tool answers one recurring question a contract manufacturer’s planners face every week. Together they span the full loop: designing a new line, scheduling machines, controlling changeovers, running the process, and moving finished goods out the door.
The method is constant across sectors. Model the operation as a sequence of events, feed it the customer’s real constraints, run hundreds or thousands of scenarios, and hand the planner a ranked recommendation with the reasoning visible. As McKinsey’s 2026 global AI survey finds, 88% of organisations now use AI in at least one function, yet only 37% can attribute any profit impact to it1 — the gap between using AI and deciding better with it is exactly where these nine tools sit.
| # | Tool | Decision it supports | Sector | Status |
|---|---|---|---|---|
| 1 | Changeover Optimizer | Sequencing and changeover minimisation on an assembly line | High-mix assembly | Available |
| 2 | Newline | Equipment and layout for a new component line | Auto components | Available |
| 3 | Machine Bank Scheduler | Schedule for a bank of same-type machines, different specs | Any machine bank | Available |
| 4 | Tablet | Batch flow and bottleneck control on a tablet line | Pharma | Available |
| 5 | Warehouse | Dock, slotting and labour planning, inbound and outbound | Distribution / 3PL | Available |
| 6 | Outbound | Pick, pack and dispatch configuration | Warehouse outbound | Available |
| 7 | FabSim | Yield, capacity and cycle-time levers in a fab | Semiconductor | Available |
| 8 | OilSim | Probability crude reaches the refinery on time | Oil & gas | Available |
| 9 | Replenish | Freezer restocking plan and route priority | FMCG distribution | Available |
The platform sits alongside existing ERP, MES and WMS systems; it does not replace them.
03 — The contract manufacturer’s problem
Volatility is the business model
A contract manufacturer sells capacity, not a product. Its customers change orders, volumes and specifications on their own schedule, and the manufacturer absorbs the volatility. Three pressures recur across every sector this paper covers.
High mix, constant changeover. Every switch between products, moulds, dies or recipes consumes time in which no revenue is produced. The order in which jobs run can change total changeover time by a wide margin, yet most plants still sequence by experience and spreadsheet.
Quotes precede the line. Winning a new programme means committing to cycle time, headcount and equipment before anything is built. An optimistic quote erodes margin for the life of the contract; a cautious one loses the bid.
The plant is only as good as what surrounds it. Late material, a congested dock, an understocked freezer or a delayed crude cargo idles a well-run line as surely as a machine breakdown.
Spreadsheets and static capacity calculators cannot hold these pressures because they ignore variability and interaction. A machine that averages 85% utilisation on paper still queues jobs when arrivals bunch up.
“Technologies alone will not take us ahead. It’s how we integrate them into our strategy that matters.”— Founder, Indian industrial manufacturing enterprise, quoted in PwC India, Rewriting the Rules: The Next Chapter of Indian Industrial Manufacturing, 2026
04 — The Indian manufacturing scenario
A capability window, not a cost story
India’s manufacturers are more confident than the global average and more exposed to the technology gap at the same time. PwC’s 2026 CEO Survey found 77% of India CEOs expect stronger domestic economic growth, against 55% globally, and industrial manufacturing was the second most common sector into which Indian companies diversified over the past five years.2 That confidence is not yet matched by AI-scale deployment.
The competitiveness gap is structural, not just technological. BCG’s global “factory of the future” research finds AI-enabled transformation can lift manufacturing productivity by up to 60%, and estimates that without modernisation, over a trillion dollars of manufacturing value in high-cost geographies is exposed to relocation risk3 — a pressure India’s contract manufacturers can turn into an opening only if their own plants modernise as fast as the customers awarding them work.
Auto components illustrate the opportunity. McKinsey projects India’s domestic auto component market to grow 7–8% annually through FY2030, outpacing the broader auto sector by 1.4–1.6x, with exports growing over 20% a year as electrification and premiumisation raise the value of every component sourced.4 Capturing that growth means quoting and running new lines faster and more accurately than the sourcing decision allows time for.
“Tech enablement and automation will surge across the sector, yet the most meaningful performance differentiation will come from how coherently those technologies, including AI and automation, work together.”— Ryan Hawk, Global Industrials & Services Leader, PwC US, on PwC’s 2026 industrial manufacturing outlook
05 — Why now
88% of organisations use AI somewhere in the business. Only 37% can show it moved profit.
McKinsey calls this the implementation gap: benefits appear at the task level and stall before they reach the balance sheet. Closing it on the shop floor means putting AI where the decision is made — the schedule, the sequence, the quote — not beside it.
Source: McKinsey & Company, The State of AI in 2026: On the Road to ROI, August 2026.
06 — How simulation works
From event model to recommendation
Discrete event simulation (DES) models a plant as things that happen at moments in time: an order arrives, a machine starts a job, a changeover finishes, a truck docks. The model advances event by event, tracking queues, utilisation and delay, and because it can draw random variation from real data, it shows the range of outcomes a plan may produce — not a single average.
The Open Netrikkan platform adds an AI decision layer on top of a conventional simulation engine.
Scenarios, not forecasts. Planners ask what happens if a customer adds 20% volume, a machine goes down for two shifts, or a mould is delayed. Each becomes a scenario, compared side by side.
Additive to existing systems. The platform reads from and complements ERP, MES and WMS systems already on the floor. It gives planners a decision layer those systems do not provide.
Fast to configure. Data arrives as the spreadsheets, PDFs and drawings a plant already holds; document extraction converts them into simulation inputs, so a first model does not wait on an integration project.
Data stays put. Language models run locally, so operational data need not leave the customer’s environment.
07 — The nine tools, at a glance
One question each, answered in minutes
08 — Assembly, new lines and machine banks
Changeover Optimizer
“In what order should this week’s work orders run so the line spends the most time producing and the least time changing over?”
Any high-mix assembly line — consumer electronics, appliances, precision instruments — faces the same problem: every model and variant needs its own fixtures, components and operator skill, and the possible sequences for a week’s orders run into the millions. The Changeover Optimizer models the line, its work orders and the setup relationship between every pair of products, simulates candidate sequences and searches for the one that cuts changeover time while still meeting due dates.
Result: A recommended sequence with the changeover hours it saves shown against the current plan — unbilled capacity recovered without capital spend.
Newline
“For a component we have not yet made, which machines, stations and layout hit the required volume and quality — and what will it cost?”
Automotive programmes are quoted against tight targets and long commitments, often within days of a drawing. Newline takes the component’s specification and a plain-English process description, parses it into ordered stages, scores candidate machines transparently against each stage, and simulates the resulting line for throughput, utilisation, work-in-progress and headcount.
Result: A scored equipment shortlist, a process-flow diagram and a line plan — evidence in place of optimism at the moment the quote is written.
Machine Bank Scheduler
“Which machine in the bank should take this job, on which shift, so every order ships on time with the fewest changeovers and idle hours?”
Any plant running a bank of same-type machines with different specifications — injection moulding presses of different tonnage, CNC centres with different tooling — faces the same scheduling problem. The module models machine capacity by shift, job-to-machine spec compatibility, due dates and changeover effort, then generates and simulates a schedule. It includes the Customer Commitment Analyser, which checks the real floor before Sales promises a date.
Result: One verified view of machine capacity: every order gets a proposed delivery date, backed by the schedule, machine loading, labour and inventory.
09 — Pharma, warehouse and outbound
Tablet
“How should a tablet line be configured and scheduled so batches clear each stage without queueing or breaching a hold-time limit?”
Contract pharma manufacturing is a chain of stages — blending, granulation, compression, coating, packing — sharing equipment across products under mandatory cleaning and quality holds. Tablet simulates batch flow through every stage, shows where batches wait and which stage limits output, and quantifies how a change in batch size or campaign order shifts throughput and lead time. Every scenario is a reproducible run, suited to regulated environments where a decision must be explained.
Warehouse
“With this inbound schedule, dock count and labour roster, where will the warehouse jam — and which change clears it?”
Modelled on a reference distribution centre of roughly 500,000 sq ft, 30 dock doors and 80–120 staff per shift. Warehouse takes advance shipping notices, dock and resource parameters and slotting data, then runs scenarios across throughput, slotting, dock scheduling and resource use, comparing volume bands such as a normal week against a peak week.
Outbound
“Which combination of picking method, wave release, staffing and cut-off times gets orders out on time at the lowest cost?”
An interactive simulator in which the planner moves the operational levers and every KPI recalculates live, with configurations saved and compared side by side. Where a manufacturer ships directly to retailers or brand owners, this is where contractual service levels are won or lost.
10 — Semiconductor, upstream oil and FMCG
FabSim
“Where in the fab are we losing yield, capacity and cycle time — and which lever recovers the most?”
Six simulation modules — chamber matching, defect flow, sampling optimisation, capacity planning, excursion risk and work-in-progress flow — run in-browser on realistic synthetic data, so a first evaluation needs no upload of sensitive fab data.
OilSim
“What is the probability that enough of the right crude reaches the refinery’s crude distillation unit on the day it is needed?”
A Monte Carlo engine spanning roughly 20 parameters across crude sourcing, maritime logistics, port and terminal operations and refinery configuration, run across 2,000 stochastic trials — the same discipline every manufacturer with a long, uncertain inbound chain needs, at its most demanding.
Replenish
“Which freezers should be restocked, with what, and in what order of visits, so shelves stay full without wasted trips or write-offs?”
FMCG brands and the contract manufacturers behind them depend on cabinets placed across thousands of outlets, where demand varies by outlet, weather and day of week. Replenish simulates cabinet-level demand, stock levels and delivery capacity, then proposes a prioritised restocking plan — closing the loop between sell-through at the outlet and production runs at the plant.
The pattern
Changeover hours, quote accuracy, machine utilisation, batch lead time, dock cost, service penalties, yield, stock-out risk, delivery cost.
Nine different cost lines. One method — simulate before you commit — recovers value on every one of them.
11 — Deployment approach and business case
Start with the decision that costs the most today
A contract manufacturer does not need to adopt all nine tools. The usual path starts with the one decision that costs the most today, proves it against a measured baseline, then extends.
Durations are indicative and depend on data readiness.
Aligning our interest with yours
Where a manufacturer prefers to pay for results, we offer gain-share pricing. Protections include an audited baseline, an agreed definition of utilisation, a carve-out for market cyclicality, a joint measurement committee, and a monthly floor fee. The manufacturer pays more only when the measured result improves.
Data and control
Models run on data the plant already holds. Language models run locally, so operational data need not leave the customer’s environment. The platform is additive: it is designed to sit beside SAP, Siemens Opcenter, and equivalent MES/WMS systems, never to replace them.
12 — Conclusion and next steps
See it against your own numbers
Contract manufacturers compete on reliability and cost, and both are set by planning decisions that are hard to test on a live floor. Simulation makes those decisions testable, and India’s manufacturers now have both the confidence and the competitive pressure to make that shift before their customers make it for them.
The best way to see one of these tools is to try it against a real scenario. A practical next step is a 30-minute working session in which you nominate the one planning decision that hurts most, and we run the closest tool against a representative case from your own operation.
To arrange it, write to the Open Netrikkan team at info@opennetrikkan.com
13 — Sources
- McKinsey & Company, The State of AI in 2026: On the Road to ROI, August 2026.
- PwC India, 29th CEO Survey — India results, 2026; PwC India, Rewriting the Rules: The Next Chapter of Indian Industrial Manufacturing, 2026.
- Boston Consulting Group, AI-Powered Factories Are Rewriting the Rules of Global Manufacturing, May 2026; BCG, How the Factory of the Future Is Reshaping the Economics of Manufacturing Competitiveness, 2026.
- McKinsey & Company, Shifting into High Gear: India’s Auto Component Sector, 2026.
- PwC, Industrial Manufacturing’s Race to 2030, global sector outlook, 2026.
- Boston Consulting Group, Turbocharging Automotive Operations with GenAI, 2026.
Figures illustrating FabSim and OilSim outcomes in this paper are drawn from each tool’s own demonstration environment on representative or synthetic data; real results depend on the customer’s own operating data.
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