How might we broaden political discourse and understanding in people age 25-35 in the UK? – Design project from the Design Kit course

This is a writeup of the design project I carried out with Tamsyn Hyatt as part of Ideo and +Acumen’s Design Kit: The Course for Human-Centered Design. Check out the prototype we produced.



  • We spoke to people with a range of political views, and carried out participant observation of their news-consumption activity.
  • We learnt from experts: Framing and messaging from a third sector expert; Rules of social media and political discourse from a digital communications director.
  • Key quotes

    “I don’t need stirring up. I want information and to make rational decisions. It doesn’t need to be heated.”

    “It sometimes worries me that I only read news from a source that shares my views. Yet clearly there’s a whole other perspective, because the majority of people voted for Brexit. I don’t understand that, and I never will if I only ever consume news written by people like me. It would be helpful to see other perspectives, even if I don’t agree with them.”

    “If I can understand what their argument is, I’m better placed to counter it. But that would need to be from a reliable source, even if I don’t agree with it.”

    “Reading contrasting opinion pieces is helpful in forming ideas, and in developing new ways of thinking.”


Key themes

  • Desire for reasoned argument from both sides
  • Time and efficiency
  • Payment and paywalls
Insight Design Question
People are willing to consider alternative viewpoints but have limited time to do so. They’re struggling with business as usual, let alone anything new. How might we deliver diverse views that are quick to consume, but not sensational or click bait?
Users are concerned that they don’t know what a credible mainstream ‘opposing’ source looks like. How might we find the best content to represent diverse views?
People may not accept views wildly different to their own as untenable, and so not engage with them. How might we find content which will be acceptable/viable to people of different viewpoints?
People generally have a notion of reliability that transcends the political spectrum. i.e. they concede that publications that they may not agree with ideologically are capable of reporting something useful, despite ideological differences. How might we stretch and push people enough to challenge them but not repel them?
Most people didn’t pay for news. How might we make a product that is either zero direct cost to the consumer, or which is seen as sufficiently valuable to warrant purchase?

Ideas from Brainstorming

  1. A twitter account – or accounts – sharing ideologically diverse content. Users can subscribe to high-quality content outside of their ideological viewpoint.
  2. A news site where diverse content is submitted by users, grouped by theme and ranked for quality. This means that the most high-quality articles to represent a given ideological position on a given topic bubble to the top.

Storyboard overview

The person
Name: Brian Simmonds
Age: 29
Profession: Administrator for a small healthcare company

Step What is happening? What is the most important question to answer? How might we answer it?
Reads the day’s news following his normal routine Brian rushes to work. He squeezes on to the train and checks his regular news sites on his phone. [already explored in Inspiration stage] [already explored in Inspiration stage]
Becomes aware of the tool He receives a message from his friend suggesting that he check out a new news tool What channel of communication is likely to encourage someone to look at a new news source? Who would the message need to come from? Test with real content in people’s facebook feeds, versus whatsapp recommendations and personalised emails
Looks at the day’s news on the new tool Brian reviews the content on the news site and is prompted to read content from different ideological viewpoints to his own. They are high quality, so he finds them challenging but interesting and informative. Will people actually choose to read an article that they disagrees with ideologically? Build a basic wireframe prototype and carry out participant observation user testing
Rates an article that he read Brian gives a thumbs up or a thumbs down to each article he read, helping other users see which ones are best, from each ideological point of view. Do perceptions of quality actually cross ideological boundaries as our research suggested they could? Will people positively rate an article that they ideologically disagree with? Build a basic wireframe prototype and carry out participant observation user testing
Shares an article that he enjoyed in his own reading Brian notices that one good article he has read today isn’t listed. So he submits this article to the site Will he take the time to share content if? (Particularly as there isn’t currently any social reward to him for doing so) Trial submit form on test service. This could be achieved using a Google Doc. This would then be manually added to the site, if the article passed a basic quality check
Shares the tool with others Brian finds the tool useful so he shares it with others in his network. Will the service be able to spread to others without paid advertising? Test on a functional prototype, and invite users to share. Build in basic social sharing features to prompt and facilitate this. E.g. facebook and twitter.


What did you prototype? What question(s) were you trying to answer?

We ​tested a clickable digital prototype​ made using the Balsamiq mockup tool.

The most important question to research was:

  • “Will people actually choose to read an article of an ideological perspective that they disagree with?”

The most important secondary questions were:

  • Will people understand our way of representing ideologically diverse content? What interface design approaches might be best?
  • Do perceptions of quality actually cross ideological boundaries as our research suggested they could? In practical terms, will people positively rate an article that they ideologically disagree with?

What did you learn from testing your prototype?

  • People looked at ideologically diverse content But this may have been ‘under duress’ as they knew they were being observed. To have confidence in this result, we would need to test in a more anonymous fashion, and in a more real-life context.
  • Users were unclear what the ‘thumbs up’ and ‘thumbs down’ meant. Does a thumbs up signify endorsement, or liking, or was it a dispassionate quality measure saying that an article makes valid points and makes you think, even you don’t actually agree with it?
  • Some users found the left-right split a bit binary. Could this be improved, to better show nuances of different positions? Is it actually useful to divide content up into different ideological sections of the page?

What might you consider for future iterations?

  • Change the ‘thumbs up’ and ‘thumbs down’ to something more emotionally neutral. It will need to signify that this piece is well-constructed and reasoned, but must not imply liking.
  • Explore whether community submissions are the best model for discovering new content.
  • Explore pulling in content automatically from high-quality sources with different editorial perspectives.
  • Explore visual and layout approaches to presenting diverse content relating to a given topic that will scale well to mobile devices. (The approach tested was desktop only, as it required more horizontal space than is available in a smartphone or tablet)
  • Explore how to categorize content beyond a left-right binary. Consider in relation to the above point about visual design and layout.
  • Allow people to add tweets, and embed these directly on the page.

​​Design Kit: The Course for Human-Centered Design – summarised

Some highlights from my notes from Ideo and +Acumen’s Design Kit: The Course for Human-Centered Design.

The steps of the Human-Centered Design process

“Human-centered design is all about building a deep empathy with the people you’re design for; generating tons of ideas; building a bunch of prototypes; sharing what you’ve made with the people you’re designing for; and eventually putting your innovative new solution out in the world.”

Human-centered design is based on creating opportunities for “high-impact solutions to bubble up from below rather than being imposed from the top.”

You can use this approach for

  • Products
  • Services
  • Spaces
  • Systems

Start by choosing a design challenge. Collect your thoughts; review what you already know; define what you don’t know; review constraints or barriers.

You move through three spaces (not always sequentially):

  • Inspiration – exploring the design challenge. “Too abstract and the brief risks leaving the project team wandering; too narrow a set of constraints almost guarantees that the outcome will be incremental and, likely, mediocre.”
    “learn directly from the people you’re designing for as you immerse yourself in their lives and come to deeply understand their needs and aspirations.”

    “focus groups and surveys, rarely yield important insights. In most cases, these techniques simply ask people what they want..” But people aren’t good at expressing their needs or imagining possibilities. As Henry Ford said – people would have wanted a faster horse. (Case Study: Clean Team toilets in Ghana. When interviewed, people said they’d prefer to dispose of their own waste if it could save them money, and were reluctant to allow service people into their homes. However, when they prototyped: people quickly realised the value of someone else handling waste disposal. A good reminder that self-report is highly flawed. Similarly, Ideo: Rockefeller Foundation health project in Bangkok: a Burmese immigrant self-reported as having no network, but actually it became clear in interview that she had a strong network.)

    Research methods: Learn from people, learn from experts, immerse yourself in context, seek analogous inspiration
    After each research item: Regroup, pull out sound-bites, interesting or surprising stories, interesting interactions and remaining questions. Don’t try to interpret yet – that comes later.

  • Ideation – Exploring your design opportunities. Generating, developing and testing ideas. Distil insights from research. Brainstorm to generate ideas – and withhold judgement.
    Linus Pauling: “To have a good idea you must first have lots of ideas” And you need multidisciplinary teams with multidisciplinary people to make interesting connections.
  • Implementation – Making your concept real and a sustainable success. “the path that leads from the project stage into people’s lives”. Prototyping is at the core of this – turning ideas into products and services that you test, iterate and refine. Prototyping – “cheap, quick, and dirty” helps de-risk the process. Have a main idea for the prototype to convey, and test that idea.

Mindsets of a Human-Centered Designer

  • Learn from Failure: “Don’t think of it as failure, think of it as designing experiments through which you’re going to learn.” Through prototyping, you’re de-risking by making something simple first, and checking it early in the process.
  • Creative Confidence “the notion that you have big ideas, and that you have the ability to act on them.”
  • Empathy – you can’t come up with any new ideas if you don’t go beyond your own life.
  • Embrace Ambiguity – Let multiple ideas exist simultaneously. You don’t know upfront what’s going to work out.
  • Be Optimistic
  • Iterate, Iterate, Iterate “we gain vaildation along the way… because we’re hearing from the people we’re actually designing for.”

Cognitive Technologies – the real opportunities for business – course notes

In late 2015 I completed an online course on cognitive technology. Here’s a summary of my notes. (NB the course is free to take, and is running again from 14 March to 13 June 2016)

What is AI?

AI is not about machines ‘thinking’ like humans. AI is the theory and development of computer systems able to perform tasks that would usually require human intelligence.
e.g. cognitive (planning, reasoning, learning) and perceptive (recognising speech, understanding text, recognising faces)

“As soon as it works no one calls it AI any more”

We expect AI agents to:

  • operate autonomously
  • perceive their environment
  • persist over time
  • adapt to change

Drivers of change in AI

  • Moore Law – microprocessors are 4 million times more powerful than they were in 1971.
  • Big data – low cost sensors, social media, mobiles, the internet gives us more data; combined with better techniques for working with this data.
  • The internet and cloud computing
  • Improved algorithms


Representing knowledge in a computer, using it to reason and plan automatically.

  1. Rules-based systems: Rules base, inference engine (to apply rules), working memory (contains all the information it has to assess). Best for situations with a small number of variables.
  2. Taxonomy: Helps to organise data into a hierarchy.
  3. Bayesian networks (Bayes nets): Useful for situations in which your confidence about a belief may change as your knowledge changes. They can represent assertions, and degrees of certainty. Can help with diagnosis, reasoning from symptom to cause, or for prediction. Less good when you have lots of variables, or when you want to recalculate the entire network.

Some algorithms used in machine learning:

  • Neutral networks – Good for pattern recognition. e.g. speech recognition. (segment audio signal onto phonemes, then associate phonemes with words in the dictionary; named entity recognition.)
  • Support vector machines – good for classification and regression. Often used for off-the-shelf supervised learning. Straightforward to train and implement, and allow a lot of variables. Helpful for Feature engineering.
  • Ensemble learning – using a collection of different models, and combining the output to obtain a stronger result. IMB Watson used this when playing jeopardy. Better than just using any one method.


Automatically devising a plan of action to achieve goals given a description of the initial state, the desired goal, and the possible actions.
e.g. getting from Times Square to the Bronx Zoo.
Search through possible actions to find a sequence that achieves the goal.
Challenge: managing complexity and computation time: combinatorial explosion.
Replanning is important too, to deal with developing situations.
Applications include: Google navigation, unmanned vehicles, robotics.


Improving performance automatically. Machine learning is the process whereby machines improve their performance without explicit programming. Machines discover patterns, make predictions, and become better over time with exposure to data. This helps in situations where we can’t anticipate all situations, or when we don’t know how to program the solution (e.g. facial recognition)

Types of machine learning:

  1. Supervised learning – learning by example.
    An agent is given pairs of information – input (or a number of inputs) and output.
    This allows the agent to understand how to produce the desired output, even for unknown inputs.
    It’s called supervised learning because we use labelled data to train the model.

    Main tasks: Classification (output is one of a set of discreet values) or Regression (output is a number)

    Applications: Sales forecasting, image recognition, text classification, health.

    Challenges: Acquiring and labelling training data; can be expensive to create data set.

  2. Unsupervised learning – discovering patterns in data even though no specific examples are provided.
    e.g. clustering – given a large set of similar items, discover ways to group them into subsets

    Challenges: algorithm has to determine which attributes should be used to group items; sometimes it’s hard to decide where to place an item.

    Applications: Customer segmentation; Social network analysis; Defining product baskets; Topic analysis; Anomaly detection – e.g. looking for outliers in manufacturing.

  3. Semi-supervised learning – unsupervised learning with human interaction to fine-tune
    e.g. giving feedback on the number of clusters, or suggesting attributes for matching.
  4. Reinforcement learning – learning by trial and error.
    Agent acts in unknown environment, responding to sensory input. Responses shaped using rewards or punishment.
    Agents take into account actions and sequences of actions when associating them with rewards or punishments.
    Works best with closed-loop problems – i.e. ones in which there are no inputs other than those caused by the action of the agent

    Challenges: time consuming with many actions or chains of actions; requires a lot of computing power; trial and error has a cost – e.g. learning how to trade on the stock market, so use it when the costs of trial and error are low.

    Applications: physical control systems e.g. elevators or helicopters, or recovering from damage by learning new ways of walking; in some domains it’s our only option.


The ability to take in information in a human-like way: through speech, text or vision.

  1. 1. Natural language processing (NLP) – software that processes human language.
    e.g. understanding or producing. Break down doc to sentences, then words, which are understood using grammar rules

    Challenges: context is tricky: e.g. “he saw her duck”

    Applications of NLP: summarising documents, translation, extracting info, question answering, writing stories, analysing customer feedback. Medicine and Law

  2. Speech recognition – recognising words, tone and emotion of human speech
    Steps: break wave form into phonemes, then match these to words, then put these into an appropriate sequence.

    Challenge: accents, background noise, homophones, need to work quickly. (I wonder how we could add contextual information to understand the set of phonemes)

    Applications: hands-free writing e.g. medical dictation, controlling devices, computer system control, surveillance,

    Future: mine broadcasts and recordings of human speech.

  3. Computer vision – the ability to identify objects, scenes and activities in images. e.g. face recognition.
    Has to build up from pixels to coloured areas, and then objects.
    Machine learning can be used to train object recognisers. error rate 2010-14 reduced four-fold

    Applications: Handwriting, medical imaging, autonomous driving, surveillance, gesture detection. One useful current application is recognition of where spare spaces are in a car park.

    Future: recognition in video, and events detection. This is hard because of the complexity: connecting recognition over time

Physical interaction

Types of robot:

  1. Manipulators – physically anchored to their workplace
  2. Mobile robots – e.g. drones
  3. Mobile manipulators – e.g. humanoid robots in films

Elements of robotic systems:

  • Mechanical and electrical engineering
  • Machine learning
  • Computer vision
  • Planning
  • Speech recognition
  • Sensors – e.g. range finders, location sensors, proprioceptive sensors (knowledge of own position), force and torque sensors
  • Effectors

Applications of robotics:

  • Manufacturing
  • Agriculture
  • Healthcare
  • Hazardous environments
  • Personal services
  • Entertainment
  • Human augmentation

Uncertainty is a challenge for robotics – e.g. needing to take action based on incomplete information, or dealing with an unexpected environment.

Business applications for cognitive technologies

  1. Product
  2. Process
  3. Insight


Embed cognitive technologies in a product or service to help the end user.
e.g Netflix film predictions, which drive 75% of Netflix usage; Google Now / Siri; predictive text.

How cognitive technologies can improve products:

  1. Convenience
  2. Simplicity
  3. Confidence
  4. Emotion

Questions to help you decide whether to embed cognitive technologies in your product/service:

  • Would people like to use it hands-free?
  • Is your product too complex?
  • Do customers have to make complicated choices to buy your product
  • Would a natural interface help customers bonds with your product?


Embed technology into an organisation’s workflow, to increase speed, efficiency, quality.

Automate internal processes, e.g.:

  • The Hong Kong subway system’s preventative maintenance programme. Scheduled by algorithm.
  • Georgia’s campaign finance commission. Uses handwriting recognition to handle the volume of work.
  • Cincinatti Children’s Hospital. Uses NLP to read freeform clinical notes to find patients who might be eligible for clinical trials. Reduced nurse workload on this area of work by 92%.

Automate expert decisions.
Relieved skilled workers of unskilled tasks.
Automate unskilled work.


Improve decision making by analysing large amounts of data – including unstructured data – to discern patterns or make predictions.
e.g working out someone’s risk of developing metabolic syndrome, and which medical interventions were most likely to improve patient health.

Benefits: better, faster decisions that can improve operating and strategic performance

How to find opportunities: See where you have large or unstructured datasets that haven’t been fully analysed; look for processes where the value of improved performance is high.

How to decide whether and where to incorporate cognitive technologies in your organisation – use the “Three Vs” framework

  1. Viable – e.g. perceptual tasks (involving vision, speech, handwriting, data entry, first tier customer service), analytical classification and predictive (forecasting, document review and summarizing), decision-making tasks (situations where knowledge can be expressed as rules, data-driven decisions), planning and optimisation tasks (e.g. scheduling)
  2. Valuable – where it’s worth applying. Involve business processes with costly labour, where expertise is scarce, where there is a high value in improving performance, or where you can deliver features or experiences that your customers care about.
  3. Vital – may be required if: industry standard levels of performance demand their use: online product recommendation, spam filtering, fraud detection; scalability – e.g. processing handwritten or printed data, analysing large amounts of social media.

The impact of cognitive technologies on work

There’s a debate – will machines take our jobs, or will they increase productivity and growth – and demand for human skills? Tasks requring adaptability, common sense, human interaction, ambiguity and creativity will be beyond the reach of machines for a long time. AI is most likely to replace highly-structured back-office roles that don’t involve many customer interactions.

Risks of automated systems:

  • Not infallible. They may eliminate operational human error, but that doesn’t mean that they’re always right.
  • Humans can lose skills if they don’t practice them
  • Humans are bad at monitoring information that remains constant for long periods of time, which may lead to errors being undetected.
  • Poorly automated systems can undermine worker motivation

Approaches to automation:

  1. Replace – completely replace a human performing a job with a machine
  2. Atomize and automate – break jobs into narrow tasks, and automate as many of these as possible. Humans are still employed, but in more of an oversight/remedial capacity.
  3. Relieve – automate tasks that are dull, dirty or dangerous.
  4. Empower or augment – make workers more effective through technology, e.g. by automating brand-new processes.

Strategic choice for approaching automation:

  • Cost strategy – use technology to cut costs by reducing the workforce, or through reducing errors and rework.
  • Value strategy – use technology to make workers more effective, or reassign workers to higher-value work.

Skills that will probably be desirable in the future:

  • The ability to work with cognitive technologies
  • Hyper-specialisation of skills or knowledge that are unlikely to be automated by computers
  • Empathy, creativity, emotional intelligence

Paulo Freire: Pedagogy of the Oppressed – the banking and libertarian models of education

This is a summary of Paulo Freire’s explanation of the banking and libertarian models of education, from The Pedagogy of the Oppressed (1996 Penguin Edition).

The point of education and human action is “the individual’s ontological and historical vocation to be more fully human.” (37)

Two models of education

The banking model of education is about depositing information into passive students

“an act of depositing, in which the students are the depositories and the teacher is the depositor. Instead of communicating, the teacher issues comminiqués and makes deposits which the students patiently receive, memorize, and repeat. This is the “banking” concept of education,…” (53)

The banking model requires students to adapt to the world, and encourages servility

“the banking concept of education regards men as adaptable, manageable beings.” (54)

“The more completely they accept the passive role imposed on them, the more they tend simply to adapt to the world as it is…” (54)

“Implicit in the banking concept is the assumption of a dichotomy between human beings and the world: a person is merely in the world, not with the world or with others; the individual is spectator, not re-creator.” (56)

Libertarian education

Education is not about integrating people into an oppressive society, but about understanding and transforming the world

“Authentic liberation – the process of humanization – is not another deposit to be made in men. Liberation is a praxis: the action and reflection of men and women upon their world in order to transform it.” (60)

“Problem-posing education affirms men and women as beings in the process of becoming – as unfinished, uncompleted beings in and with a likewise unfinished reality.”(65)

“Whereas banking education anesthetizes and inhibits creative power, problem-posting education involves a constant unveiling of reality.” (62)

“Education as the practice of freedom – as opposed to education as the practice of domination – denies that man is abstract, isolated, independent, and unattached to the world; it also denies that the world exists as a reality apart from people. Authentic reflection considers neither abstract man nor the world without people, but people in their relations with the world.” (62)

What does libertarian education look like in practice?

“Through dialogue, the teacher is no longer merely the-one-who-teaches, but one who is himself taught in dialogue with the students, who in turn while being taught also teach. They become jointly responsible for a process in which all grow… Here, no one teaches another, nor is anyone self-taught. People teach each other, mediated by the world, by the cognizable objects…” (61)

How to create a libertarian program of education

“The starting point for organizing the program content of education or political action must be the present, existential, concrete situation, reflecting the aspirations of the people.” (76)

“education… cannot present its own program but must search for this program dialogically with the people,” (105)

“the investigation of thematics involves the investigation of the people’s thinking – thinking which occurs only in and among people together seeking out reality… Even if people’s thinking is superstitious or naive, it is only as they rethink their assumptions in action that they can change. Producing and acting upon their own ideas – not consuming those of others – must constitute that process.” (89)

“the team of educators is ready to represent to the people their own thematics, in a systematized and amplified form. The themetics which have come from the people return to them – not as contents to be deposited, but as problems to be solved.” (104)

“after several days of dialogue with the culture circle participants, the educators can ask the participants directly: ‘What other themes or subjects could we discuss besides these?’ As each person replies, the answer is noted down and is immediately proposed to the group as a problem.” (104-5)

What I learnt from Coursera’s Operations Management course

Recently I completed Coursera’s Introduction to Operations Management course. The course was made up of 5 units.

Course outline

  1. Process analysis
    Measuring the flow of units through a production process; Little’s Law; inventory turns; inventory buffering: make to stock (McDonald’s) or make to order (Subway); working out bottlenecks when there are different types of flow units, processes with attrition loss; reasons for inventory.
  2. Productivity
    Lean operations and waste reduction; the seven sources of waste; KPI trees and sensitivity analysis; overal equipment effectiveness framework (OEE); reducing idle time through line balancing and standardising processes; labour, material and capital productivity; return on invested capital (ROIC) trees;
  3. Variety
    Motives for variety; batch processes and setup time; working out a good batch size; Single-Minute Exchange of Die (SMED); benefits of partial flexibility; delayed differentiation (via product design) to reduce costs of variety;
  4. Responsiveness
    Reasons for waiting: insufficient capacity and variability of arrival times and/or processing times; coefficient of variation of demand and processing time; how to compute the averate waiting time; measuring inventory over the course of a day; usefulness of pooling; strategies for prioritising work: first come first served aka first in first out, versus sequencing, shortest processing time rule; problems with appointment systems; efficiency gains are often about process redesign rather than just optimising/balancing: value stream mapping aka process mapping aka service blueprints: Vyes Pigneur’s 7 ideas for redesigning processes; waiting time and attrition loss (using Erlang Loss table);
  5. Quality
    Reasons for defects – performance and conformance quality; redundancy; impact of scrapping and rework on flow; buffers reduce risk of resources being starved or blocked, to keep flow rate up; in contrast: Toyota production system: reduce inventory to expose problems; Kanban – demand pull: work is authorised by demand, so you reduce the number of Kanban cards over time; six sigma: checking units produced against a specification; control charts: normal and abnormal variation; Jidoka system sacrifices flow for quality; Kaisan and Ishikawa diagram for root cause problem solving.

Short of summarising the outline above, I won’t attempt to share everything I learnt. Instead, I’ll share what was most relevant to my own practice.

The distinction between project management and process management

This course was about process management – about doing the same thing over and over. My job is incorporating more process management elements, so I took this course to improve my understanding, and so that I could begin to make more effective process improvements.

Some elements of my work are process management – communicating, planning, and running an agile sprint production cycle. I run through this process every couple of weeks.

Other elements of my work are project management – leading a large web development project; providing consultancy on a project; overseeing the scoping, planning and creation of a new element of functionality or a user experience improvement: each discrete piece of development work is unique. So sometimes it’s useful for me to think in terms of projects, and other times it helps to think more generically and look at underlying processes.

Little’s Law

In any process, the average inventory (number of units in the process) = the average flow rate x the average flow time (the time it takes a flow unit to go from the start to the end of the process)

Key implication: if the flow rate is constant, reducing inventory will reduce flow time, allowing work to be completed more quickly.

See more about Little’s Law.

The seven sources of waste (Taiichi Ohno)

  1. Overproduction – to produce sooner, or in greater capacities than demanded. These goods need to be stored; their production slows the rate with which you turn your inventory; they could become obsolete or be stolen.
    The solution: match supply with demand.
  2. Transportation – unnecessary movement of people or parts _between_ processes.
    The solution: relocate processes, then introduce standard sequences for transportation.
  3. Rework – repetition or correction of a process.
    The solution: do it right the first time. Find out the reason for the quality problem and put a stop to it.
  4. Overprocessing – processing beyond what the customer requires.
    The solution: make sure you have guidance for what your standards are.
  5. Motion – unnecessary movement of parts or people within a process.
    The solution: create and use standard workspaces that have been created to minimise movement.
  6. Inventory – the number of flow units in the system. The biggest source of waste. Bad for inventory turns, increases customer wait time and flow time. Inventory needs to be stored, which is costly.
    The solution: improve production control system and reduce unnecessary “comfort stocks”.
  7. Waiting – underutilising people or parts while a process completes a cycle. i.e. a flow unit waiting for a resource. Often a direct result of inventory. Waiting can happen at the resource: this is idle time.
  8. Intellect – an eighth source of waste. Don’t waste workers’ abilities to help solve problems and improve processes.
  9. Increasing profitability is easier if you’re constrained by capacity than if you’re constrained by demand

    If you’re constrained by capacity, increasing the productivity of the bottleneck can help you significantly increase profits. (This is particularly the case for businesses with low variable costs and high fixed costs)

    If you’re constrained by demand, you’ll only be able to significantly increase profitability if you’re able to lay off workers.

    Variability increases wait times, even if resource utilisation is less than 100%

    If people arrive at regular intervals, and take a fixed length of time to process, then you can plan your processes to avoid waiting time. But real life is less predictable.
    Variability of arrival times and processing times can lead to inventory in a process, even if utilisation is 80%.
    So variability means that even if you aren’t utilising all your resources all the time, you’ll still have people or products waiting in the process.

    Two reasons for waiting: insufficient capacity; variability of arrival times and/or processing times.

    If you’re constrained by capacity, you don’t need to worry about demand variability as you already know there will be bottlenecks. If demand is the constraint, and it’s variable, then you need to think about it, as it will cause waiting times.

    Partial flexibility is usually the best way to deal with variety

    If there is variability in demand, you need to accommodate it. Total flexibility is expensive, and usually not needed.

    Eg. It’s sensible to hire developers who have skills in two areas, so you have flexibility, but don’t have to pay the costs of a developer skilled at everything.
    For each area of your work, hire at least two people with those skills.

    “The way we frame a problem determines the types of solutions we come up with.”

    A surprisingly philosophical insight. Often it pays to be more creative than just doing queuing analysis and line balancing.
    Question your processes at a strategic level – don’t just think tactically and inside the box.

    Value stream mapping is a tool to help you focus your process on valuable activities

    Value stream mapping, aka process mapping, aka service blueprints – map out the steps the customer has to go through, then divide them into ones that add value and ones that don’t, or which are waiting time.

    Yves Pigneur has a framework for this: Customer actions; onstage actions, backstage actions; support processes.

    7 ideas for redesigning processes

    1. Move work off the stage.
      E.g. online airport check-in.
    2. Reduce customer actions / rely on support processes.
      E.g. rather than requiring customers to fill in all their medical details each time they come to visit, you could have a database to store them.
    3. Instead of optimising the capacity of a step, see if you can remove it altogether if it isn’t really needed.
      E.g. Hertz Gold removed the airport check-in step as it provided no value.
    4. Avoid fragmentation of work due to specialization / fragmentation of roles.
      E.g. in a bank, it’s annoying to have to fill in different forms for different people, rather than just doing everything at once.
    5. If customers are likely to leave the process due to long wait times, move the waiting time to later in the process if you can.
      E.g. Starbucks making you pay first, then wait for your coffee.
    6. Have the waiting occur out of a line.
      E.g. restaurants in malls using buzzers to let people know that their food or table is ready, rather than having them wait in a line.
      E.g. appointments to see a doctor.
    7. Communicate the wait time to the customer – set expectations.
      E.g. theme parks.

    How much do defects cost? It depends on where they are detected.

    If defects are detected before the bottleneck, the cost is driven by the input prices.
    If defects are detected after the bottleneck, the cost is the opportunity cost of the lost sale.

    The step at which the defect happens isn’t important – what’s important is the step at which it is detected.

    Therefore it’s very important to test flow units as much as you can before you put them into the bottleneck.

    For me, this means that we want to catch problems in the specification stage if possible. Minimise potential problems by making sure that the work is sensible, and that the requirements are clearly articulated.

    Kaisan and Ishikawa are tools for root cause problem solving

    Kaisan equips front line workers to identify and solve problems.

    Ishikawa diagram – structured brainstorm. Shaped like a fish bone. Try to identify root causes. Asking ‘why’ 5 times helps.

    Once you’ve done this exercise, go out and measure instances of the identified defects.
    Plot these on a pareto chart.
    See which defect is most frequent and focus on that first. Generally the pareto principle applies: 80% of the defects are caused by 20% of the root causes.

    These methods are recommended because they oscillate between thought and reality, gaining the benefits of both:

    Reality: Jidoka – the process is triggered by real-world defects.
    Thinking: Ishikawa diagram, to think about what might be causing the problem.
    Reality: Pareto chart – collects data to see which causes are most frequent.
    Thinking: Think up alternative solutions.
    Reality: Experiment with the solution you choose.

    Actions I will take as a result of this course

    • Map out the production process so everyone knows what steps are required. If people don’t understand all the steps in a process, they might have unrealistic expectations of waiting times.
    • Create formal processes for new jobs – development/UX work and bugs. This will reduce variability of inputs and reduce risk of defects by improving the quality of inputs/briefs.
    • Formalise waiting time processes. Draw up (collaboratively?) and obtain organisational agreement for a set of organisational priorities for bugs and for new work. This will mean that all prioritisation decisions are made according to a clear set of standards. E.g. number of users affected, financial implications, strategic priorities. Currently I don’t do first-come-first-served, but rather prioritise according to business need and urgency, but this does require me to be wise like Solomon. Better to have some commandments to live by, and a supreme court to interpret them.
    • Reduce the number of units being processed at any one time. Little’s Law states that this will result in reduced waiting times. This might be a hard sell, but the truth is that we are already constrained by how many hours of development we have in each two week cycle.
    • Identify and reduce the sources of waste in my work. eg transporation – reduce transportation costs in communication with agency and stakeholders (make it clearer), reduce movement of work around internal stakeholders (currently I report to internal stakeholders outside of the tools I use for day-to-day project management, which adds costs and the risk of misinformation.)
    • Harness worker intellect through more regular review cycles. Constitute regular reviews of processes with all people involved in them, to see how they feel they are going, and what could be improved.
    • See if processess could be redesigned to be more efficient. Could steps be removed or automated?
    • Conduct value stream mapping – figure out which steps add value and which ones don’t. Use Pigneur’s framework for process redesign to improve these processes.