Manufacturing and Management

Lecture 1 - Where do inventions come from?: Invention vs innovation

Invention = a new idea, technology, device, process or concept. Innovation = the invention successfully adopted, used and valued in the market.

Lecture 1 - Where do inventions come from?: Technology push

The inventor has a technical possibility and must find an application/market. Examples: accident, analogy, structured search, mapping, new materials.

Lecture 1 - Where do inventions come from?: Market pull

A clear market/user need pulls development. Examples: experience in use, lead users, fashion, legislation, luxury features becoming expectations.

Lecture 1 - Where do inventions come from?: Accident

Unexpected discovery creates a new possibility. Examples: Teflon, penicillin.

Lecture 1 - Where do inventions come from?: Analogy/transfer

Borrow a principle from another domain or nature. Examples: Velcro from burrs; flying-machine ideas from birds/bats.

Lecture 1 - Where do inventions come from?: Structured search

Systematic search for solutions. Examples: TRIZ; Panasonic bread maker engineers studying expert kneading.

Lecture 1 - Where do inventions come from?: Mapping

Compare domains to reveal a missing equivalent. Example: inerter from mapping electrical and mechanical systems.

Lecture 1 - Where do inventions come from?: New materials

New properties open design space. Example: carbon fibre composites enabling lightweight structures.

Lecture 1 - Where do inventions come from?: Experience/lead users

Real use reveals problems; advanced users solve needs earlier. Examples: winglets; mountain bikes; Lamborghini.

Lecture 2 - Is there a market?: Design mix

A product is more than the object: core benefit, actual product/features, product-service system, and meta product such as brand/ecosystem/business model.

Lecture 2 - Is there a market?: Differentiation

A product can differ by function, cost, usability, aesthetics, service, brand/status, sustainability or business model. It need not be best at everything; it must be best for a target segment.

Lecture 2 - Is there a market?: Utilities

Functional utility = what it does; emotional utility = how it feels to use; social utility = what it signals about the user.

Lecture 2 - Is there a market?: Market definition

Competitors include direct substitutes and any alternative solving the same customer problem. Defining the market changes the competitor set.

Lecture 2 - Is there a market?: Segmentation

Divide a market into named groups with similar needs. Good segments are homogeneous within and heterogeneous between.

Lecture 2 - Is there a market?: Four segmentation bases

Product attributes/price; product usage; user characteristics; benefits delivered.

Lecture 2 - Is there a market?: Perceptual map

Choose two customer-relevant axes, plot competitors/segments, name clusters, identify gaps, and justify target position.

Lecture 2 - Is there a market?: Value proposition

For [target customer] who [need], the [product] is a [category] that [benefit]. Unlike [alternative], it [differentiation].

Lecture 2 - Is there a market?: Example

iPod: not just an MP3 player; device + iTunes + Apple brand/ecosystem + legal music route created a stronger design mix.

Lecture 3 - Designing the right solution: needs to prototypes: Stakeholders

Anyone affected by or able to influence design, manufacture, purchase, distribution, use or maintenance.

Lecture 3 - Designing the right solution: needs to prototypes: Internal stakeholders

Design, manufacturing, assembly, quality, service, sales, marketing, finance, logistics, management.

Lecture 3 - Designing the right solution: needs to prototypes: External stakeholders

Users, customers, buyers, regulators, distributors, installers, maintainers, suppliers, insurers, society/environment.

Lecture 3 - Designing the right solution: needs to prototypes: Buying roles

Initiator, influencer, decider, buyer, user. In medical/B2B markets these are often different people.

Lecture 3 - Designing the right solution: needs to prototypes: Persona

Fictional but realistic archetype of a user/stakeholder. Include name, background, context, capabilities, pain points, goals and design implications.

Lecture 3 - Designing the right solution: needs to prototypes: Kano model

Basic features: absence dissatisfies, presence expected. Performance features: more/better increases satisfaction. Delighters: unexpected features that delight; may become basic over time.

Lecture 3 - Designing the right solution: needs to prototypes: Prototypes

Representations used to learn, test and communicate before committing to final design/production.

Lecture 3 - Designing the right solution: needs to prototypes: Prototype types

Sketch, block model, visual/appearance model, CAD/simulation, functional prototype, production/pre-production prototype.

Lecture 3 - Designing the right solution: needs to prototypes: Why prototype?

Reduce uncertainty and market/technical risk; test usability, ergonomics, performance, aesthetics, manufacturability and customer reaction.

Lecture 3 - Designing the right solution: needs to prototypes: Fidelity trade-off

Low-fidelity = cheap/fast early learning; high-fidelity = expensive but closer to final function/appearance/process.

Lecture 4 - Selecting production processes: Manufacturing definition

Machines + material + energy transform material into required geometry and properties. Process choice affects cost, quality, defects and performance.

Lecture 4 - Selecting production processes: Subtractive

Remove material: machining, drilling, milling, grinding. Precise and flexible; can be slow and wasteful.

Lecture 4 - Selecting production processes: Additive

Build material layer by layer: 3D printing. Complex/custom/low tooling; slow and high unit cost for mass production.

Lecture 4 - Selecting production processes: Moulding

Shape material in a mould/cavity: injection moulding, casting, compression/blow moulding. High tooling cost but low unit cost at high volume.

Lecture 4 - Selecting production processes: Deformation

Reshape material by force: forging, rolling, extrusion, stamping, bending. Efficient/strong/high-volume; limited by material ductility and tooling.

Lecture 4 - Selecting production processes: Process selection

Ask: material, precision/tolerance/surface finish, number/volume, total cost, geometry, production rate, sustainability.

Lecture 4 - Selecting production processes: Cost factors

Tooling, capital equipment, labour, material, waste, energy, yield, inspection, rework, maintenance, cycle time.

Lecture 4 - Selecting production processes: Injection moulding

Pellets -> heated screw -> molten plastic -> nozzle/sprue/runners -> mould -> cool/eject. Control temperature, pressure, shot size, clamping force, cooling time.

Lecture 4 - Selecting production processes: Common defects

Flash, short shot, warpage, sink marks, internal stress. Poor process parameters cause defects.

Lecture 4 - Selecting production processes: AI contribution

Sensor data for process monitoring, defect prediction, parameter optimisation, predictive maintenance and automated inspection.

Lecture 5 - Scaling up new technologies: Scale-up

Turning a prototype into repeatable, safe, economical, controlled production. Prototype success does not prove manufacturability.

Lecture 5 - Scaling up new technologies: Four journeys

Technology scale-up, process/production scale-up, business scale-up, value-chain/supply-chain scale-up.

Lecture 5 - Scaling up new technologies: Why hard

Process changes can alter product function; variability, cost, yield, regulation, quality evidence and suppliers become major constraints.

Lecture 5 - Scaling up new technologies: Medical device challenge

Must show safety, clinical/medical utility, quality, sterility where relevant, regulation and consistent manufacture.

Lecture 5 - Scaling up new technologies: Risk classes

Risk-based classification: Class I low risk through IIa/IIb to Class III high risk. Higher risk means more evidence, testing and external approval.

Lecture 5 - Scaling up new technologies: Material selection

Balance mechanical properties, chemical stability, biological response, manufacturability, sterilisation compatibility, cost, regulation and sustainability.

Lecture 5 - Scaling up new technologies: Biomaterials

Polymers, metals, ceramics, composites. Biocompatibility = appropriate host response in a specific application.

Lecture 5 - Scaling up new technologies: Quality control

Proves every batch/unit meets specification: dimensions, material, surface, function, sterility, traceability, records.

Lecture 5 - Scaling up new technologies: Post-processing

Cleaning, finishing, coating, heat treatment, sterilisation, packaging, labelling and inspection can be essential to final product function.

Lecture 5 - Scaling up new technologies: Sterilisation

Sterility is not cleanliness. Methods include steam/autoclave and radiation; choice depends on material, packaging, geometry, residues, cost and standards.

Lecture 5 - Scaling up new technologies: Lifecycle/regulation

Concept -> prototype -> preclinical/clinical evidence -> approval -> launch -> post-market surveillance/adverse-event monitoring.

Lecture 6 - How does a factory work?: Factory purpose

Transforms orders/materials into finished products using people, machines, information, energy and processes.

Lecture 6 - How does a factory work?: Volume-variety

Low volume often allows high variety and flexible processes. High volume requires standardisation, flow, dedicated equipment and lower unit cost.

Lecture 6 - How does a factory work?: Project layout

Product stays fixed; resources move to it. Example: ship, aircraft, construction. High variety/low volume.

Lecture 6 - How does a factory work?: Functional/job shop

Similar machines grouped. Flexible for high variety; complex routing, scheduling and WIP.

Lecture 6 - How does a factory work?: Cellular layout

Machines grouped for product families. Better flow than job shop while retaining some flexibility.

Lecture 6 - How does a factory work?: Line/flow layout

Operations arranged in sequence. Efficient for high volume/low variety; inflexible and vulnerable to bottlenecks.

Lecture 6 - How does a factory work?: Continuous process

Very high volume, continuous flow, often process industries such as chemicals, oil, paper, food.

Lecture 6 - How does a factory work?: Order decomposition

Customer order -> product -> subassemblies -> parts -> operations/tasks. Factories break complexity into manageable tasks.

Lecture 6 - How does a factory work?: Key equipment

Machine tools, robots, conveyors, fixtures/jigs, sensors, inspection systems, storage, material handling, packaging equipment.

Lecture 6 - How does a factory work?: Make strategies

Make-to-stock = fast delivery but inventory risk. Make-to-order = lower inventory/customisation but longer lead time. Engineer-to-order = design starts after order.

Lecture 7 - Controlling and managing factory operations: Hierarchy

Factory -> production line -> cell -> machine -> task. Each level has different objectives and decisions.

Lecture 7 - Controlling and managing factory operations: Control loop

Objective -> sensing -> decision -> action -> operation -> feedback. Generalises control theory to manufacturing operations.

Lecture 7 - Controlling and managing factory operations: Nested loops

Higher-level action becomes lower-level objective: e.g. factory schedule sets line targets, line targets set cell/machine tasks.

Lecture 7 - Controlling and managing factory operations: Machine control

Temperature, speed, position, force, fill level, pressure, robot path, inspection decisions.

Lecture 7 - Controlling and managing factory operations: Operations management

Planning, scheduling, dispatching, monitoring, corrective action, capacity management, inventory/material coordination.

Lecture 7 - Controlling and managing factory operations: Automation rationale

Improve productivity, repeatability, quality, speed, safety, data capture; reduce labour and variability.

Lecture 7 - Controlling and managing factory operations: Automation drawbacks

High capital cost, inflexibility, integration difficulty, maintenance, cyber/security risks, skills requirements, downtime risk.

Lecture 7 - Controlling and managing factory operations: MRP

Material Requirements Planning: uses demand/orders, bill of materials, inventory and lead times to decide what to make/buy and when.

Lecture 7 - Controlling and managing factory operations: Industrial IT examples

PLC for machine logic, SCADA for supervision, MES for execution, ERP for business planning; Industry 4.0/IIoT connects machines and data.

Lecture 7 - Controlling and managing factory operations: Emerging tech

Sensors, IoT, digital twins, robotics, AI, analytics and cloud systems can improve visibility and decisions, but depend on data quality and integration.

Lecture 8 - Supply chains and industrial logistics: Supply chain

Network of organisations, people, resources, information and processes moving materials/products/services/money from source to customer.

Lecture 8 - Supply chains and industrial logistics: Supply network

More realistic than a linear chain: multiple suppliers, tiers, customers and flows. Firms compete as value chains, not isolated companies.

Lecture 8 - Supply chains and industrial logistics: Logistics

Transport, warehousing, storage, handling, distribution, routing, packaging and movement of goods.

Lecture 8 - Supply chains and industrial logistics: Objectives

Cost, quality, delivery, flexibility, resilience, security, sustainability, coordination and customer service.

Lecture 8 - Supply chains and industrial logistics: OEM and tiers

OEM sells/assembles final product. Tier 1 supplies OEM; Tier 2 supplies Tier 1; lower tiers provide subcomponents/materials.

Lecture 8 - Supply chains and industrial logistics: Lean vs agile

Lean prioritises efficiency, low waste and low inventory. Agile prioritises responsiveness to uncertain demand.

Lecture 8 - Supply chains and industrial logistics: Bullwhip effect

Small demand changes downstream cause amplified fluctuations upstream due to forecasting, batching, promotions and delays.

Lecture 8 - Supply chains and industrial logistics: Resilience

Ability to withstand disruption. Built through redundancy, dual sourcing, buffers, visibility, flexibility and risk monitoring.

Lecture 8 - Supply chains and industrial logistics: Trade-offs

Low inventory lowers cost but reduces resilience; global sourcing lowers cost but increases disruption/coordination risk; local sourcing improves speed/control but may cost more.

Lecture 8 - Supply chains and industrial logistics: Quantitative tools

Forecasting, inventory models, network design, optimisation, routing, scheduling, simulation, risk analysis.

Lecture 9 - AI and data science in manufacturing and supply chains: Data analytics

Collecting, cleaning, integrating and analysing data to find patterns and support decisions.

Lecture 9 - AI and data science in manufacturing and supply chains: Machine learning

Algorithms learn from data to predict, classify, optimise or detect patterns without explicit rule programming.

Lecture 9 - AI and data science in manufacturing and supply chains: AI

Systems performing tasks associated with human intelligence: perception, prediction, reasoning, optimisation, planning or generation.

Lecture 9 - AI and data science in manufacturing and supply chains: Descriptive analytics

What happened? Dashboards, KPIs, defect history, downtime reports.

Lecture 9 - AI and data science in manufacturing and supply chains: Predictive analytics

What will happen? Demand forecasts, failure prediction, defect prediction, lead-time estimates.

Lecture 9 - AI and data science in manufacturing and supply chains: Prescriptive analytics

What should we do? Optimised scheduling, inventory decisions, routing and process parameters.

Lecture 9 - AI and data science in manufacturing and supply chains: Product design

Generative design, customer insight mining, simulation acceleration, material/process discovery, design optimisation.

Lecture 9 - AI and data science in manufacturing and supply chains: Manufacturing

Computer vision inspection, predictive maintenance, process monitoring/control, anomaly detection, robotics, scheduling.

Lecture 9 - AI and data science in manufacturing and supply chains: Supply chain/logistics

Demand forecasting, inventory optimisation, supplier risk, route optimisation, disruption detection, agent-based simulation.

Lecture 9 - AI and data science in manufacturing and supply chains: Challenges

Fragmented/dirty data, rare events, legacy systems, integration, explainability, trust, skills, cyber risk, bias/ethics, ROI uncertainty.

Lecture 10 - Protecting assets: intellectual property: Why IP matters

Helps capture value, delay imitation, attract investors, enable licensing, support bargaining power and protect knowledge assets.

Lecture 10 - Protecting assets: intellectual property: IP is broad

Not only patents: patents, designs, trademarks, copyright, contracts, know-how, data, software, manufacturing capability and relationships.

Lecture 10 - Protecting assets: intellectual property: Patent

Protects technical inventions/products/processes if novel, inventive and industrially applicable. Public disclosure before filing can destroy novelty.

Lecture 10 - Protecting assets: intellectual property: Copyright

Protects expression such as software code, drawings, manuals, text and images; not the underlying idea.

Lecture 10 - Protecting assets: intellectual property: Design right

Protects appearance/shape/configuration, not technical function.

Lecture 10 - Protecting assets: intellectual property: Trademark

Protects brand identifiers such as names, logos and symbols.

Lecture 10 - Protecting assets: intellectual property: Trade secret/know-how

Confidential knowledge such as recipes, process parameters, supplier knowledge, data or tacit manufacturing expertise.

Lecture 10 - Protecting assets: intellectual property: Contracts/NDAs

Manage confidentiality, ownership, rights to use, rights to improvements and collaboration boundaries.

Lecture 10 - Protecting assets: intellectual property: Freedom to operate

Check whether commercialising your product infringes others' IP.

Lecture 10 - Protecting assets: intellectual property: IP strategy

Choose what to patent, keep secret, publish defensively, license, share with suppliers or protect by speed/capability.

Lecture 11 - How does the product make money? Getting investment: Business model

How the organisation structures value creation and value capture.