Tech n Ops Flashcards
4 v’s of operations
variability, variety, volume, visibility
Primary performance parameters
Cost, quality, flexibility, speed, reliability (ethics and environment)
- Strategy: choose balance of these so competitive advantage
Green impacts
Lubin and Etsy (2010): sustainability not as a second-thought but original design e.g. 3D printing - additive manufacturing - cake to homes.
Hervani et al (2005): green supply chain management - keiretsu style of relationship
packaging, supply chain compliance, revised distribution networks
Societal pressures and repetitional risk defining minimum ethical standards.
- ethical vs financial frontier -> push it out
Sustainability examples
Unilever: Sustainable living plan -> Marmite by-product to be eaten & produce methane to generate power
HP recycling facility
Product-process matrix
Hayes & Wheelwright 1979: project, jobbing, batch, line, continuous
- volume vs variety
Focused factory
Skinner 1974
- “Simplicity and repetition breed competence”
- need congruous tasks - competitive priorities
- cannot achieve high performance in all competitive dimensions simultaneously
Sandcone model
Ferrous & Meyer 1990
- cumulative capabilities to avoid tradeoffs
- quality, dependability, speed, cost
Queuing points
- Identify the bottleneck
- Baulk - don’t join
- Renege - leave
- Deal with seasonality - find products with counter-seasonal demand
- Kingsman formula: cost:service trade-off
- variability, inventory, capacity utilisation
- E.g. IKEA ‘ designing out the bottlenecks’ : design car parks, marked short-cuts, express checkouts
EOQ assumptions
The Q of items that supposedly minimise the total cost of inventory mgmt
Stable demand linear holding costs order costs known and fixed (FX impacting this - OECD 2000 - 95% are SME) no supplier delays / batch limitations unlimited capacity to store ignores stockout costs
Little (92): humans desire simple models that can be applied broadly
Points against EOQ
Taichi Ohno: always challenge the current EOQ - don’t take costs as given - SMED
Williams and Westbrook 1994: mass customisation - EOQ taking last gasps
Inventory costs
Calloni et al (2005):
- Physical - insurance / storage
- Working capital costs (opportunity cost)
- Devaluation costs (quality defects)
- Obsolescence costs
Porter et al (1999): 75% UK use MRP, useful for storage costs etc but not obsolescence as hard to measure
Typical estimate is 20-30% - probably conservative as excludes quality, depreciation and opportunity cost
Challenges with inventory
Contestability: hard to predict demand
Seasonality
Kopczak and Johnson (2003): want to get earlier demand info or impact the demand pattern to match supply and demand. Don’t take as given and try match supply.
Inventory examples
HP: computer’s quickly change specification - in 1995 cost of inventory same as overall profit margin
Ford - owned mines for iron - raw material to cash to 33hrs from 728 - Model T obsolete in 1920s.
Airbus - A320neo not available - 3 year wait time post Boeing crash - Boeing keep producing before outcome?
UPS: late packages $5-30 rev - ‘hot status board’ where haven pilots & planes to ‘rescue volume’ - saves $20m rev.
National Health Service Blood & Transport: shelf life of 5 days
Bullwhip effect
Small disturbances downstream cause increasingly large disturbances, errors and volatility as work upstream
Sterman 1989: Beer game
- ‘misperceptions of feedback’: attribute dynamics to external effects not acknoweledge were internally generated.
- amplification 700%
- now have more decision aids e.g. data on demand
Fisher’s inventory costs and solution
Fisher et al 1994:
- stockout costs:
- markdown costs
- Risk-based production sequencing: use off-peak capacity to produce predictable, free up peak for unpredictable when demand materialises
Adv inventory
- exploitation price reduction
- avoid stockout costs
- Economies of scale: decrease cost of ordering
- buffer/insurance against uncertainty
- increase in value (wine)
- Smooth production
- pipeline inventory (allow for lead times)
- Little Law’s implies minimum needed to run factory: no. in system = arrival rate x wait time
Disadvantage inventory
- obsolescence / perishability
- depreciation, storage, capital costs
- Hids problems: used to maintain delivery despite unreliable production. Can’t see operating inefficiencies (TPS)
TPS Japanese words
Keiretsu - exclusive relationships - light-touch contracts Kanban - pull-scheduling Kaizen - continuous improvement Jidoka - total employee involvement Pokayoke - preventing errors e.g. sim card or USB port - built into design. Muda - waste Andon - line stop authority Heijunka - level scheduling
Author opinions of lean / TPS
Hayes and Pisano: reduces trade-off as pushes out ppf
Krafik (1988): pure Fordism with added glue of teamwork
Bohan (1998): how do you implement a philosophy
Takeuchi et al (2008): studied Toyota 6 years. Practices necessary but not sufficient for the success. They’ve mastered ‘soft innovation’ that relates to corporate culture. Emulating Toyota is about creating culture (not cyst copy practices)
Staats et al (2011): implementation is hard. Non-manufacturing - lack of repetition.
Quality definition and TQM founders
Consistent conformance to customer’s expectations.
- cost of prevention vs that of failure
Deming, Ishikawa, Turing
TQM characteristics
continuous improvement meeting customer requirements reducing rework increased employee involvement process redesign competitive benchmarking management responsibility team-based problem solving constant measurement closer relationships with suppliers
Author opinions of TQM
Benson (1993): in 10 short years became pervasive part of business thinking as quarterly results
Slack (1995): must implement all
Powell (1995): findings suggest features generally associated with TQM..don’t produce competitive adv but certain tacit, behavioural, imperfectly imitable features such as culture, employee empowerment produce adv
Osigweh (1989): concept stretching - ambiguity and empirical vagueness as stretch to new contexts
Hackman & Wakeman (1995): what counts as success, many influence, short vs long term
Issues with hospitals
Professional rivalries
Ethic concerns e.g. care.data
Knowledge-doing gap
Pfeffer and Sutton 1999
Adler et al (2003) solution:
- ‘Performance Improvement Capability’ 5 components
- skills supported by systems, embedded structures, need strategic guidelines that fit with culture
Cultural blindspots
Weick and Sutcliffe (2003):
- culture enables sustained collective action
- blind the collective to vital issues to factors outwits the bounds of the organisational perception
- tenacious justification
- e.g. Bristol Royal Infirmary - paediatric cardiac surgery - blamed severity not themselves
- bring into TQM
Types of service variability
Frei (2006):
- arrival
- preference
- request
- effort
- capacity
Service profit chain
Heskett et al (1997):
- internal quality service-> employee satisfaction & productivity -> external service value -> customer satisfaction -> customer loyalty -> rev & profit
- frontline workers & customers at centre of strategy - profit is the outcome not the focus
- Southwest Airlines CEO Kelleher found on tarmacs/terminals interacting
- tech more important than employee interaction?
Production line approach to services
Levitt (1972): technocratic vs humanistic terms
Service blue-printing
Bitter et al (2008)
- services are fluid, dynamic and coproduced by consumers, employees and technology
- can’t over simplify
Self-service technologies
Meuter et al (2000):
- Adv: less c2c interactions, £, speed, avoid hiring/firing
- Disadvantage: tech savvy, complaints proliferate via reviews, prone to customer driven failure, lack human contact impact loyalty
- e.g. ATM, mobile banking, pay-at-pump terminals, file for divorce, vending machines, tax preparation software, blood pressure machines
Customer defections
Frederick and Sasser (1990):
- scrap heap for services: customers that don’t come back
- accounting systems don’t capture the value of a loyal customer
- big impact on the bottom line
- 10% reduction in unit costs is financially equivalent to a 2% decrease in defection rate
- Loyalty: price premium, reduce costs e.g. credit check, referrals, increase in purchases
- E.g. credit card spends $51 to recruit new customer
Supply chain examples
Levi Global sourcing andoperating guidelines: used to select business partners in 60 countries that are consistent with company values
Rana Plaza Bangladesh collapse 2013: Primark
Kobe Steel: falsifying data on quality of products. GM, Boeing, Toyota sub-standard materials.
VW: “S-ratings” zero-emissions vehicles to be built in factories relying on renewable energy.
- entire ecosystem
- batteries operating at slim margins
North Face Bluesign: meet rigorous standards
Air Canada: warning financial performance hit by grounding of Boeing 737
7 eleven Japan: HQ aggregate data for supply chain
Changing the supply chain
Disintermediation: easier with internet
Outsourcing: harder to trace problems, delays, wider capabilities
No. of suppliers:
Co-opetition: clusters - industrial commons (Pisano and Shih 2009) - collective capabilities / knowledge when geographically rooted)
Supply chain relationships
Slack 95: Contractual vs partnership relationships
- contractual: competition, flexibility, economies of scale
- partnership: long-term, joint learning, co-ordinate activities, spirit not letter of law, open-ended commitment to take initiatives for mutual benefit
Uzzi (1997): embeddedness vs arms-length
- fine-grain info transfer (economies of time), trust, joint-problem solving, asset specificity
- isomorphism
Marshall Fisher:
- Functional vs innovative markets
- efficient vs collaborative (agile)
Trust in supply chain
Helper and Sako (1998):
- trust: less hierarchies to attenuate opportunism.
- issues: self-interest seeking with guile is human nature
- inter-organisational vs personal trust
- societal norms are self-reinforcing: governance by trust more prevalent in Japan
Bruhn (2001): trust is easier to breach than to build
Can’t measure trust- interview - unconscious bias Bazerman et al (2002)
Supply chain challenges
Urban restrictions / transport costs (JIT)
Ethics
Govt influences
Sustainability
Japan’s earthquake in 2011 disrupted JIT - ‘too big to do without’ - buffers for the extremes
Fast fashion
Sull and Turconi (2008): shared situation awareness - recognise a pattern in fluid situation. Opportunity-pull vs designer-push
- Catwalk to rack - 15 days
Camuffo et al (2001) recent homogenisation of consumer lifestyles
‘1kg fabric generates 23kg greenhouse gases’
Mass customisation four faces
Gilmore and Pine (1997):
- collaborative
- adaptive e.g. iPhones
- transparent
- cosmetic e.g. Hertz Gold
want customer penetration as late as possible
Mass customisation adv
- reduce retail space and price cut and inventory costs
- reduces waste
- improves quality,
- ideas for new products
- builds loyalty
- Franke and Pillar (2003): 100% value increment in WTP
Mass customisation authors
Salvador et al (2009): Choice navigation
- quality of user interface
- product and process satisfaction
- where preferences diverge the most
Westbrook and Williamson (1994): need to prevent bottlenecks
- specially designed machines
- direct link to market
Zipkin (2001):
- Elicitation: 3D scans - Metal - AI scans in shop
- Process flexibility - modularity - only final assembly is customised product produced
- Logistics - unqiue operational techniques - Levi took decades to develop
Good where preferences diverge signficantly
De Holan et al (2009):
- change from traditional marketing, accounting procedures, strategy, value-chain
- overcoming inertia: not commonalities in customer needs
Mass customisation examples
Mini Cooper
Modular House building: Legal and General factory near Leeds - produce 3,000 homes a year, more affordable, customise interior
Nike by you
Safe hands / home depot
Big Data V’s
Volume, velocity (rate modified/updated), variety (unstructured), veracity (accuracy)
- new ones: value* (challenge to manage), variability
Big Data benefits
Cost-cutting - insurance - progressive
- enhanced fraud detection
Improve the offering:
- quick feedback (iPhone info to app developers)
Predict preferences: marketing, inventory, supply chain
Differential pricing
Mass customisation
Digital performance mgmt: McKinsey - dashboards fed real time data - not just improving the laggards. Improvements not one dimension. Removes some of standard, transactional work of TPS. Immediate solving (past 2 weeks)
Big data risk
Privacy
- smart meters - energy profiling - Brown 2014
- GDPR: fine €10m
- politically sensitive that spreads panic and distrust
Operational
- Butler (2015): ‘finding a needle in a haystack’
- ‘Recency bias’: Chatfield 2016 - recent data has more weight - 10x more data every 2 years for last 3 decades
- management: noise vs quality - false assumptions - spurious (corr not causation)
- unwitting bias: Boston street bump
- enabler not guarantee
Threats
- Telegraph: almost 50% firms detecting a data breach
- Hacks could become more potent - driverless cars
- Amber Rudd: intelligence service allow access to encrypted WhatsApp
- Agrafiotis et al (2014):
(democracy - targeted news)
Big Data authors
Brown 2014: privacy by design
Agrafiotis et al (2014): internal threats - trusted personnel using privileged access for an unauthorised reason
Greenberg (2017): sophisticated to prevent hackers but simple for consumer to understand
EY report:
- cloud agile and unparalleled scalability
- displayed in the realm of average business user
- need to be leveraged by people asking right Qs
- enabler not guarantee
Big Data examples
Willis (2016): anticipatory shipping model - send relevant items to warehouse / distribution centre - predictive analytics
Fannie Mae attach 2008: upload malicious code to execute 3 months later anderase data
Movie data: fast forward, rewind, paused
Weather forecasts - beer sales
Cambridge Analytica data scandal - data from fb profiles
Dell: social media listen and command centre - 25,000 messages everyday - evaluate trends
Mercedes-Benz TeleAid system: airbag triggered -> reports via phone network location / driver
Metail - AI scans in shops
Amazon Web Services
Maylor’s 4 D’s and implications
(2003) :
- define, design, deliver, develop
- Waterfall: talk to customer one: design then do it to get output
- now loops
Project definition
‘a unique venture with specified costs, time and quality requirements’
Project statistics
Flyvbjerg et al (2007): cost overruns and benefit shortfalls of 50% are common
Dived et al (2016): success rate of information systems as low as 39%
Runaway projects
Dexeter and Iacovou (2004):
- learning is key: not too late - manager role is to prevent problems from materialising and to cure problems if they do
- need a leader not a manager
- avoid emotional attachment: consider cancellation
- formulate an open communications plan
- break the remainder into small portions
Issues with govt projects
- Diverse user group - many stakeholders
- Uncertainty of funding (e.g HS2 spending review)
- Pressure to announce beneficial project to appease public even if not viable
- Social network not a hierarchy - complex governance
- mission creep
- gaming
Project forecasting issues
Flyvbjerg (2007): case of inaccuracy
1) technical
- Slack screen ideas: feasibility, acceptability, vulnerability
2) psychological - optimism bias
- unbiased exit champion
3) political-economic - deception as asymmetric info - political candidates
- deception under attack - Enron
Project problems
Alderman and Ivory (2005): multi-nodal
Ford and Sterman (2003): concealing rework - shoot the messenger
- locally rational but globally irrational
Williams (2005): decoupled from environment. Island order vs iron cage. Vicious cycles - systematic interrelated set of causal factors. Difficult to trace.
Factors to help problems with projects
Alderman and Ivory (2005):
- slack - redundancy/ contingency
- top-down (embed learning systems / bottom-up (local empowerment)
Walton (1985):
- control to commitment: flat hierarchy, responsibility, teams
Williams (2005): soft factors - work force morale, client-contractor trust, impact of overtime
Stakeholder-power interest grid
MacLeamy curve
Project examples
HS2 - £600m buying houses. Parish councils urging ministers to scrap. 5G not around when first planned - mission creep
Crossrail: stations, carriages, tunnels, software. Most original mgmt left. 2 years late.
- CEO argued MTR (running it) could have been involved earlier in the project.
2010 commonwealth games India: costs 16x planned. Stadiums empty. Volunteers quit.
Carillion: FT 2018 - govt lack basic skills in costing & project mgmt. Govt renegotiate £120m contracts since start of 2016.
Sydney opera house - overspent by factor of 17 but classed as beneficial.
Qatar world cup on time but workers like slaves /
Agile project
Bohem (2002):
- new generation: crushing weight of beaurcracy, rapid change in IT, dehumanisation
- increasing disparate needs:
- higher dependability (need planned) and rapid change (need agile). Balance both.
- synthesise plan-driven and agile
- depends on size of loss and probability of loss
- low size, easy rework, less planning
Principles: harness change, continuous delivery of value, empower team
Project criteria for success
Slack (1995): corporate responsibility, end-user satisfaction, supplier satisfaction, team satisfaction, CSR, health and safety, cost, time, performance
Stakeholder-power interest grid
Level of influence vs level of interest
- keep satisfied
- keep informed
- manage closely
- monitor
Project factors out-with manager control
Dwivedi et al (2016): stakeholder communication inadequate, poor support from sponsor
BMW factory observations
Solar panels on roof Make to order All types on one production line Production line on roof Lorries delivering to track side on time Stations timed to 56 seconds
7 + 1 types of waste
Over-production Waiting time Transport Process Inventory Motion Defectives Human ingenuity
JIT definition
Tries to meet demand instantaneously with perfect quality and zero waste
- inc efficiency and reduce waste
3 key elements of JIT
Eliminating waste, total employee involvement, continuous improvement (kaizen)
Where JIT is most suited
Large volume repetitive manufacturing environments such as motor industry
TPS lessons for all organisations
Hopp and Spearman (2001):
- operational details matter strategically
- Controlling work in progress is important
- Quality can come first (Sandcone model)
- Flexibility is an asset
- Continuous improvement is a condition for survival
Definiton of a supply chain
A network or organisations that link together to deliver goods and services to the consumer
Evolution of thinking about supply chains
Kopczak and Johnson (2003): sequential handoff
- Linear model is traditional: primary source -> manufacturer -> distributor -> retailer -> customer
Chain vs network (collaborative effort)
- Focal organisation is tier 1
- Tier 2: supplies to tier 2 so on
- further along they get smaller
- Keiretsu: competition between distinct supply trees. Suppliers close proximity.
- Kito and New 2011: barrel. Work for many supply chains.. E.g. British Steel - 96% of network rail
Tools to manage the supply chain
ERP: enterprise r p - integrate planning
MRP: materials resource planning - take account lead times, amount supplies need, when needed
- aim to minimise throughput time. Scheduling resources. Push system. Build up stock as lead times.
Electronic Data Interchange
- enables real time mgmt, automated supply chain
- Slack
Internet impact on supply chains
- Tracking
- Social responsibility (media)
- Global sourcing (more specialised)
- Mass customisation (cut out retailer)
- fast procurement cycles
How to assess supply chains
Classic performance parameters
Lee (2004):
- Agile (handle disruptions), Adaptable, Aligned (incentives - share knowledge and responsibilities)
cannot measure trust !
Service recovery
Tax and Brown (1998):
- actions in response to failure.
- encourage complaints, learn and respond quickly
- increase customer satisfaction and loyalty
Points to make with mass customisation
- order point penetration: hold inventory if long-supplier lead times / problem with reliability. Where hold inventory?
- make-to-order (not high variety assembly)
- where preferences diverge the most (e.g. colour vs gearbox ratios in car, photo on mug vs shape of mug)
Project assessment framework
Iron triangle:
- scope (deliverables), resources (cost), time (due date)
Example green and the supply chain
Skanska - sweden construction company
- Only reporting direct emissions from their plants: 35,000 tonnes CO2. Larger part is supply chain: 400,000 tonnes - 10 fold. Data isn’t there to properly measure.
- carbon capture and storage
Tesla - Gigafactory to be renewable powered by end of this year
- VW: S-ratings FT 2019 - battery makers slim margins so don’t invest in green initiatives
Mass customisation definition
Production of items for individual consumption at the same cost, speed and quality associated with mass production
- exploits product structures where there is modularity within a common product architecture
Blurring the notion of IP
Berthon et al (2015)
- myriad of problems and opportunities
- Service-dominant logic whereby co-create value (Vargo and Lusch 2004)
- internet drives diffusion of user generated content
- grow as devices loaded with innovation tools
- Creator has emotional investment, firm concerned with control, don’t want to alienate consumers
Value in self-design
Franke and Pillar (2003)
- look at use of toolkits for innovation of watches designs
- WTP 100% value incremental - just aesthetic variability not individualised fit
Agile defintion
Rigby et al (2016):
- taking people out of their functional silos and putting them in self-managed and customer-focused multidisciplinary teams
When agile easiest to implement
Not a panacea:
- complex problem, solution unknown, requirements likely to change, work can be modularised, close collaboration with end users is feasible
Mass customisation disadv
- virtual vs reality
- customer confused by choice vs unaware of possibilities
- supply chain cope with variability - Amdahl
- blurs notion of IP - Berthon et al (2015)
- expense if doesn’t add value
Motives for mass customisation
- service dominant logic -Lusch and Vargo 2004 - co create
- avoid waste
- rapidly shifting fashions
- failure rates of new product introductions
- decreasing customer loyalty
Drum buffer rope
“Drum” sets the pace, synchronise all resources to the activity of the drum
- reduce inventory
- design out the bottleneck
- IKEA: design car parks, marked short cuts, express checkout
Inventory definition
Accumulation of resources (materials, customers, info) as they flow through processes or networks
- not perfect harmony
Keiretsu
Aoki and Lennerfors (2013):
- long exclusive relationships, light touch contracts
- Hallmarks: trust, collaboration, edu support
- study groups for suppliers - IKEA taught tech for printing veneer patterns on tables
