IB MET L7: How to control and manage factory operations

What decisions are made at different levels of the manufacturing hierarchy?

Factory level — Complete an order

• Overall production and supply chain management

• Ensure materials/resources are available

• Deliver customer orders on time

Production line level — Make a product

• Coordinate product flow and output

• Decide production timing and quantity

Cell level — Make a part

• Coordinate machines and robots

• Manage local workflow and part movement

Machine level — Perform a task

• Direct process control

• Control motion, temperature, positioning, filling, etc.

👉 Key idea:
Manufacturing control is hierarchical, with higher levels managing planning and lower levels controlling detailed operations

What is the basic control loop problem in manufacturing?

• A control system manages an operation by continuously comparing actual performance to a required target.

  Main elements:

  • Objective / Reference input → desired target or setpoint

  • Operation → process being controlled

  • Sensing → measure current performance

  • Decision → compare actual vs target

  • Action → adjust system to reduce error

  👉 Key idea:
  Control systems use feedback to keep operations close to the required objective.

What are hierarchical or nested control loops?

Manufacturing systems contain multiple control loops operating at different levels.

Higher-level loops:

• Coordinate overall tasks and objectives

Lower-level loops:

• Control detailed physical actions

Example:

• Factory controls production targets
  → Production line coordinates machines
  → Robot controller coordinates joints
  → Motor controller controls individual motor position

👉 Key idea:
Complex manufacturing systems are controlled using nested feedback loops at multiple hierarchy levels.

How does decision-making change across the manufacturing hierarchy?

Key principles:

• Decision range and time horizon increase higher up the hierarchy

• Action from Level i + 1 becomes the objective for Level i

• Decisions may be automated or human-controlled

• Number of decision-making units increases lower down the hierarchy

Hierarchy levels:

• Level 5: 1 Order

• Level 4: 1 × n Products

• Level 3: 1 × n × m Parts

• Level 2: 1 × n × m × p Tasks

• Level 1: 1 × n × m × p × r Steps

Higher levels:

• Fewer decisions

• Longer-term planning

• Broader coordination

Lower levels:

• Many rapid detailed decisions

• Direct machine/process control

👉 Key idea:
Manufacturing control systems break large production goals into increasingly detailed actions through nested hierarchical decision loops

How are control loops applied to different manufacturing processes?

• Different manufacturing operations use different types of control depending on the process being managed.

  Examples:

  • Additive manufacturing (3D printing)
    → Position control
    → Controls nozzle/tool movement and trajectory

  • Processing manufacture (mixing tanks)
    → Flow or temperature control
    → Maintains correct fluid flow and process temperature

  • Food filling systems
    → Level control
    → Maintains correct liquid/product fill level

  • Cutting/machining operations
    → Position control
    → Controls cutting tool location and movement accuracy

  Control loop elements:

  • Reference input/objective

  • Sensors measuring actual output

  • Controller making decisions

  • Actuators adjusting the process

  Disturbances:

  • External signals acting on the system

  • Affect process output and accuracy

  Examples:

  • Vibration

  • Temperature variation

  • Material variation

  • Wear

  👉 Key idea:
  Manufacturing systems use feedback control loops to maintain desired operation despite disturbances affecting the process output

Example: Dynamic deflection analysis

Control requirements in production processes

• Position

• Force/Power

• Temperature

• Concentration

How is AI used in machine control for manufacturing?

AI can be used to improve control of manufacturing process quality, especially in additive manufacturing.

AI systems can adjust:

• Flow rate

• Printing speed

• Tool/nozzle offset

• Heat input

Example:

• A Residual Attention Convolution Neural Network can learn the best operating conditions from process data and sensor feedback.

Benefits:

• Improved quality and consistency

• Reduced defects

• Adaptive process control despite variation or disturbances

👉 Key idea:
AI enables smarter closed-loop machine control by learning optimal operating conditions and automatically adjusting process parameters

What is the role of cells in factory automation and control?

Manufacturing cells combine machines and operations to produce families of parts efficiently.

Cells:

• Perform linked manufacturing operations

• Deliver completed parts to other cells or production stages

• Often include automated machines, robots, and material handling systems

Factory layout affects:

• How parts flow between cells

• Production efficiency

• Material handling complexity

If machines are arranged randomly:

• Product flow becomes inefficient

• Transport distances increase

• Scheduling and control become more difficult

Cellular layouts improve:

• Simpler product flow

• Easier automation and control

• Reduced handling and delays

👉 Key idea:
Manufacturing cells organise related operations together to improve automation, simplify part flow, and increase factory efficiency

What are cell-level operations and control in manufacturing?

Cellular manufacturing

A manufacturing cell is:

• A group of one or more machines

• Producing families of similar parts/products

• Often with integrated workpiece and tool handling

A single operator may supervise much of the work within the cell.

Cell-level control problem

Goal:

• Complete the sequence of operations needed to manufacture a part or sub-assembly

The control system must:

• Coordinate multiple machine operations

• Ensure operations occur in the correct sequence

• Maintain safe operation

• Handle multiple parts simultaneously if required

This is called:

• Machine/cell coordination

Automation needs

The system:

• Receives event signals from machines/sensors

• Performs logical decisions

• Sends control/event signals back to machines

👉 Key idea:
Cell automation coordinates multiple machines and operations so parts flow through the correct manufacturing sequence safely and efficiently

How are PLCs used in manufacturing cell automation?

A PLC controls and coordinates manufacturing cell operations.

It:

• Receives signals from sensors and machines

• Makes logical decisions based on programmed conditions

• Sends commands to machines, robots, and conveyors

The PLC ensures operations occur:

• In the correct sequence

• Safely and efficiently

👉 Key idea:
PLCs automate manufacturing cells through logical decision-making and coordinated control of machine operations

How does factory-level production control manage orders?

A factory aggregates multiple levels of decisions to fulfil customer orders:

Order(s) → Product(s) → Part(s) → Tasks

Factory/production control must:

• Coordinate machining and assembly operations

• Assign operations to machines, cells, or lines

• Schedule start and finish times

• Track production progress

This is called:

• Production scheduling and execution

Automation systems:

• Prepare schedules

• Receive “operation complete” signals

• Send “operation start” signals

• Monitor product flow through production

👉 Key idea:
Factory-level control coordinates products, parts, machines, and tasks so orders are completed correctly and on time

What is production scheduling in factory control?

Production scheduling:

• Allocates machines, labour, and resources at specific times

• Ensures production meets constraints such as:

  • Capacity

  • Deadlines

  • Material availability

  • Machine availability

Factories are dynamic systems, so:

• The initially optimal schedule may no longer remain optimal

Reasons:

• Machine breakdowns

• Delays

• Rush orders

• Material shortages

Example:

• A rush schedule may prioritise a specific process or product ahead of others.

👉 Key idea:
Production scheduling continuously adapts resource allocation and operation timing to changing factory conditions and priorities

What are common objectives in production scheduling?

Scheduling objectives may include:

• Minimising average completion/flow time

• Minimising maximum lateness

• Minimising number of late jobs

• Minimising average lateness/tardiness

• Minimising makespan

  • (time between first job starting and last job finishing)

Methods used:

• Algorithm → gives the optimal solution

• Heuristic → gives a good but not necessarily optimal solution

👉 Key idea:
Production scheduling aims to optimise time, delivery performance, and resource usage using algorithms or heuristics depending on problem complexity

Scheduling heuristic 1: How can average completion time be minimised in scheduling?

To minimise average completion time:

• Schedule jobs with the shortest processing times first

• This is called the Shortest Processing Time (SPT) rule

Notation:

• Job_(completion time)

• Example: A_11​ means job A finishes at time 11

Example:

Alphabetical schedule:

• A → B → C → D

• Completion times: 11, 14, 18, 20

• Average completion time:

11+14+18+204=15.75\frac{11+14+18+20}{4}=15.75411+14+18+20​=15.75

SPT schedule:

• D → B → C → A

• Completion times: 2, 5, 9, 20

• Average completion time:

2+5+9+204=9\frac{2+5+9+20}{4}=942+5+9+20​=9

👉 Key idea:
Scheduling shorter jobs earlier reduces the average time jobs spend in the system

Scheduling heuristic 2: How does Earliest Due Date (EDD) scheduling minimise maximum lateness?

To minimise maximum lateness:

• Schedule jobs in order of earliest due date first

• This is called the Earliest Due Date (EDD) rule

Notation:

• Job_(completion time) ​

Lateness:

Lateness=Completion Time−Due

👉 Key idea:
EDD scheduling reduces the worst-case lateness by prioritising jobs with the earliest deadlines

What is an agent-based AI manufacturing control system?

Agentic AI manufacturing control uses software “agents” to represent:

• Machines

• Robots

• Cells

• Orders

Examples:

• CNC agent

• Robot agent

• Cell agent

• Order agent

Each agent can:

• Make decisions (“reason”)

• Communicate with other agents

• Help determine production scheduling and execution

The system answers questions such as:

• “Who performs the task?”

• “When should it happen?”

Compared with traditional hierarchical control:

• Control is more distributed

• Devices cooperate collectively

• Scheduling becomes more flexible and adaptive

👉 Key idea:
Agent-based manufacturing control uses intelligent software agents to coordinate factory operations through distributed decision-making rather than strict hierarchy

What is Material Requirements Planning (MRP)?

MRP is a system used to determine:

• When components should be manufactured

• When raw materials or parts should be ordered

MRP helps plan production in advance based on:

• Product demand

• Production schedules

• Component requirements

Challenges:

• Demand may be seasonal or irregular (“lumpy”)

• Different products may share common components, creating variable component demand even if final product demand is constant

👉 Key idea:
MRP provides a systematic way to plan production and material ordering so the correct parts and materials are available at the right time

Making factory level decisions

How does a BOM support Material Requirements Planning (MRP)?

A Bill of Materials (BOM) shows the component breakdown of a product into:

• Sub-assemblies

• Parts

• Raw materials

Example:

• A top handle assembly may contain:

  • Top handle

  • Nails

  • Bracket assembly

  • Coupling

Using:

• The production schedule

• The BOM

…the factory can perform requirements explosion:

• Work backwards through the BOM

• Calculate quantities of every component and raw material required

Example:

• If Product B requires 2 Part D
  → producing 100 products requires 200 Part D

👉 Key idea:
MRP uses the BOM to determine exactly what parts and materials are needed, in what quantities, and when they must be available for production

How do communication systems operate across manufacturing hierarchy levels?

Low-level systems

At machine/task level:

• Sensors, switches, robots, and CNC machines generate real-time data

• Connected through:

  • I/O systems

  • PLCs

  • Robot/CNC controllers

Cell-level PLCs coordinate:

• Machines

• Robots

• Sensors

• Material handling

Factory-level systems

Higher-level PCs/cloud servers manage:

• Orders

• Products

• BOMs

• Materials and inventory

• Scheduling and production tracking

These systems process:

• Large data volumes

• Complex non-time-critical information

What is the difference between real-time and non-real-time communication in manufacturing systems?

Real-time communication

Software protocols and communication hardware that provide real-time guarantees to support time-critical operations.

Used mainly from:

• Cell level downward

• PLCs, robots, CNC machines, sensors

Characteristics:

• Deterministic

• Low latency

• Uninterruptible operations

• Time-dependent decisions/actions

• Small/simple data volumes

Examples:

• Robot motion control

• Sensor feedback

• Machine safety systems

Non-real-time communication

Software protocols and communication hardware that do not require real-time guarantees, where communication efficiency is more important than timing performance.

Used mainly at:

• Factory/order/product level

• PCs and cloud servers

Characteristics:

• Non-deterministic

• Batched communications

• No strict time dependency

• Large/complex data volumes

Examples:

• BOM management

• Scheduling

• Inventory/material tracking

• Production reporting

👉 Key idea:
Machine-level control requires deterministic real-time communication, while factory-level management uses non-real-time communication for larger-scale information processing

What are the main challenges in integrating factory operations?

Factories are difficult to integrate because they are:

• Spatially large

• Highly complex

• Made of many interconnected systems and decision levels

• Operated by many people, often without a full system overview

Problems can occur such as:

• Machine breakdowns

• Quality defects

• Raw material shortages

Examples:

• Failure of canned food heating process

• Speckled paint defects

• Shortage of computer chips

These problems create knock-on effects:

• Production delays

• Scheduling disruption

• Material shortages elsewhere

• Reduced product quality

In highly automated factories:

• Failures can rapidly propagate through the system

• Recovery and coordination become more difficult

👉 Key idea:
Factory integration is challenging because failures in one part of a complex interconnected system can affect many other operations across production

What technologies are associated with Industry 4.0 manufacturing?

Industry 4.0 is the fourth industrial revolution, driven largely by:

• Internet connectivity

• Digital integration

• Smart automation

Manufacturing decisions occur at different timescales:

• Planning → days/months

• Scheduling → hours/days

• Execution → minutes/hours

• Process control → seconds/minutes

Emerging technologies include:

• Machine learning (ML)

• Internet of Things (IoT)

• Cloud systems

• 3D printing (3DP)

Notes:

• Many technologies are not yet widely adopted in all companies

• A technology alone is not a complete manufacturing solution

👉 Key idea:
Industry 4.0 integrates digital, connected, and intelligent technologies across all levels of factory planning, scheduling, execution, and control