A quantum leap in resourcefulness

David Hodes

In Part One, I wrote about the kind of challenges we all face when trying to define the human, material, information and financial resources available to us. In small groups, we can estimate what is reasonable and possible simply by talking to people and seeing what they have on their plate. But as our projects get larger and more complex, if we can’t properly quantify our resources, we end up flying blind.

This is Part 2 of a series. Read the other parts here:

Part 1: On being resourceful: the problem

Part 2: A quantum leap in resourcefulness

[Listen to audio version, read by David Hodes]

How can we match the workload demanded by the schedule to the supply pool articulated by some system of capacity management? The Theory of Constraints (TOC), by its very name, is a theory about how we come to understand what stops us getting more of what we want. It points to how we can more productively use what we have—and know when and where to invest for a step-change in performance.

In the first instance we need to quantify our capabilities in a way that a computer can store, enabling us to calculate load and capacity. Since TOC was invented by a physicist, let’s call each task a quantum of work, a quantum being the minimum amount we need to articulate in our schedule. Just as any electrician knows exactly what a volt is, so must we come to know the conceptual categories we need to consider to carry out each quantum of work. Thus, we have:

Capability: The ability to perform or achieve certain actions or outcomes through a set of controllable and measurable faculties, features, functions, processes or services.

Resource: An asset capable of creating value through work, generally under the broad classification of human, material, financial and information.

Resource type: The classification of a resource using structured data as defined by the organisation’s taxonomy of resource types.

Position: A role within an organisation that belongs to a node in its hierarchy that has, as a minimum, a manager, a start date, end date, capability and, often but not necessarily, associated cost and revenue.

Position assignment: The assigning of a resource to any given position with matching capability and the requisite authority, for no longer than the duration of the position. The assignment should either cost less than or deliver more revenue than the budget for that position.

Roster: The days and times a given individual, team or other resource will be available to do useful work.

Schedule: A sequence of tasks that includes, as a minimum, each task’s description, predecessor(s), successor(s), resource type(s), effort by resource type and duration.

Schedule assignment: The assigning of a resource, resources or a team to a task in a schedule.

In the diagram below, many of these concepts come together:

So what does a quantum of work look like? In this example, the position is a role on a project which has a budget approved, with an expectation of a start and an end date. The burn rate multiplied by the duration of the position gives the total burn. Assignments are created when an assignee is recruited into a vacant position. In most cases, when someone joins a project, there is an induction period, before useful work can be done. This so-called useful work is articulated in blue and we call it the scheduled load. It is the load that comes from the systems and tools used for work management.

You can see that this example doesn’t ever quite reach the normal capacity of an eight-hour day, and it certainly doesn’t require any sprint capacity such as overtime or weekend work to satisfy the demand. Incidentally, running at sprint capacity the whole time is a recipe for burnout. What, then, is the optimum number when we look at load versus capacity, or put another way, demand versus supply?

“Running at sprint capacity the whole time is a recipe for burnout”

Before we can answer that question, we need to define how we will structure the data in order to define our resource taxonomy.

In the table below you’ll see an example of an engineering environment that defines the following: the domain; the discipline within that domain; a specific skill within that discipline; a speciality, if there is one; their level in the organisational hierarchy (front line, supervisor, manager and executive being the most common); and the organisation (or perhaps P&L) they belong to.

In many ways, this definition is no different from creating a code for an item of stock in a warehouse. Understand the code and you know what the item is, where it belongs, how many of them are in stock, what we need to do to get more of them, and so on. Defining capability in this way can be seen as defining a stock code for knowledge workers. There’s no reason, then, why it couldn’t equally be used to define a piece of equipment, such as a crane, or materials such as electrical cable.

Once we have this idea nailed, we can assign these codes to roles within a project or the organisation, as well as to tasks in a schedule. If the same coding is used to define the capability of any given individual—who will likely have more than one capability—then we have satisfied the necessary and sufficient conditions of being able to establish a match between demand from our scheduling system and supply from our system of capability management. The implications are enormous. We can know, reliably, what can be done, by when and for how much—at any level of the enterprise. Now you’ve done the difficult task of planning out the quanta of work and their sequence, you have a way of assigning resources to achieve your aims.

Back to the earlier question. Your information system should be designed in such a way that you can instantly report on all resource types where load versus capacity is greater than 80% over the next planning horizon. Why 80%? As a rule of thumb, if demand is greater than 80% of supply, you increase the risk of turbulence. Demand doesn’t come in a neat line at 80% of capacity—there will be peaks and troughs. Running at 80% of aggregate capacity allows for a degree of smoothing. Thus, if the load is shown as less than 80%, it means you have resource capacity available to do useful work. If not, you need to get more supply.

Once you have a good handle on the data, one way of tapping into existing capacity to solve the problem of resource bottlenecks is to query that data for anyone in the resource pool who has ever held a similar position, and establish where they are now. If they are still in the resource pool, establish their current assignment and test if their position can be backfilled. In other words, can someone else, a non-constraint, be assigned to the constraint’s existing role and thus free them to do only that which only they can do? (We call this the ‘double-only’ question.) After all, constraints are simply resources whose demand outstrips their supply. Finding ready supply within your existing resource pool is a fine way of maintaining the rate at which value is created.

If adding resources in the above manner is either not possible or not enough to close the capacity gap, you need to initiate your recruitment process. It’s worth noting that backfilling is a lot easier than one thinks. The principle about constraints applies here. Where there is a constraint, by definition, non-constraints have capacity available. The productivity magic happens when non-constraints collaborate to support a focus on getting the constraint’s priorities done at any given point in time.

“Constraints are simply resources whose demand outstrips their supply”

The flipside of looking for the bottleneck is to identify unconstrained positions—for argument’s sake, all resource types where load versus capacity is less than 50% over the planning horizon. List the names of all those people on the project whose assignment relates to the unconstrained positions. Ask if the unconstrained people have ever held a position or have a capability that could alleviate a current or future constrained position. If the answer is yes, then reassign that person to the constrained position. If no, the choice is either reskilling (training the person to do another job) or off-boarding (take the person off the project team).

A quantum of solace

Quantum work management probably sounds quite technical and thinking through the details is not everyone’s cup of tea. But this is a big issue within the domain of resource management and optimisation. You can’t avoid the criteria for each quantum of work outlined above; they are implicit in your system. But if you deliberately name and tag them in your resource database, and do it consistently, you’ll be ahead of the game.

There’s simply no substitute for the discipline of applying your standards for planning, scheduling and capabilities in a rigorous and proactive way. Applying TOC in this way is guaranteed to give you the quantum leap I promised in the title and sustainably deliver continuous improvement in business performance.

At all levels of work, your people will know what they have to do, and know that sufficient resource has been provided to get the work done on time and to quality. That goes a long way to fulfilling the criteria for ‘just work’ ensuring people are fairly treated while achieving the ambitious and worthy goals of the organisation.

This is Part 2 of a series. Read the other parts here:

Part 1: On being resourceful: the problem

Part 2: A quantum leap in resourcefulness

If you want to learn more about how TOC can help you focus where it really counts, why not schedule a video call.

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What’s next? The change from standard thinking to Theory of Constraints (TOC) is both profound and exhilarating. To make it both fun and memorable, we use a business simulation we call The Right Stuff Workshop. We’d love to run it with you. To learn more:

download the brochure (no email required)
schedule a call

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Complicated computer code [Background photo: ‘The score’ by Dayne Topkin on Unsplash]

“Start where you are, use what  you have, do what you can.” —Arthur Ashe

MedTech

Validating medical device concepts before major investment

David Hodes

Concept validation

A concept can look convincing in a presentation and still make an important task harder. A patient may misunderstand an indicator. A clinician may need information the design does not provide. A promising workflow may depend on support unavailable in the intended setting.

These problems become more expensive when the organization discovers them after committing to architecture, tooling or a launch plan.

We must ask how effectively concepts are validated before significant resources are committed. Patient Centered Design treats this as a learning discipline: turn ideas into testable solutions rapidly using low fidelity prototyping, put them in front of relevant users and gather evidence about whether they improve the outcomes that matter.

“A convincing presentation shows what the team hopes will happen. A useful test reveals what people actually do.”

Begin with a question that can change the decision

A prototype without a learning objective can generate interesting comments while leaving the key decision unresolved. Start by identifying what must be true for the concept to succeed.

Does the patient intuitively understand the required action without explanation? Can the professional recognize when the result is ready to use? Does the proposed approach fit the work surrounding the task? Will people take meaningful steps toward adoption?

State the assumption and the evidence needed to assess it. Agree what would support proceeding, what would require revision and what could make the concept unviable. Gather quantitative data along with qualitative verbal feedback.

Prioritize questions with substantial consequences for the investment. An attractive enclosure deserves less attention than an unresolved issue that could prevent safe or practical use of the whole solution.

Choose the simplest useful representation

A picture is worth a thousand words. A simple user experience prototype is worth a thousand pictures. Choose artifacts that maximize learning with the least effort. The appropriate artifact depends on what the team needs to discover.

A sketch may help users explain how they interpret a proposed interaction. A physical mock-up may expose handling difficulties. A simulated interface can support a sequence of tasks. A more complete prototype may be necessary to examine timing, feedback or combined interactions.

Build enough realism to test the assumption accurately. Excessive detail can consume resources without improving the evidence, while insufficient fidelity can conceal the issue being investigated.

Make limitations clear to participants and observers. A manually simulated function may help test an experience, but it does not establish that the technology can deliver that function reliably.

Recruit people who represent the intended use

Patients and healthcare professionals can differ in experience, confidence, physical ability, responsibilities, and access to support. Those differences influence whether the concept helps them complete the job.

Define the participants and contexts relevant to the test. Include people whose circumstances could expose difficulties the development team has overlooked.

A familiar expert may work around a confusing interface that challenges a new user. A clinician from a highly supported center may assume resources unavailable elsewhere. Research needs to reveal these dependencies.

Match the study to its purpose. Exploratory sessions can identify problems and guide iteration. They do not automatically establish population-wide performance or replace the evidence required for later design validation.

Create an Experience Café around real tasks

An Experience Café gives participants a structured opportunity to encounter concepts and talk about their experience. Its value comes from the quality of the scenario, observation and follow-up questions.

Set up situations that reflect what the person is trying to accomplish. Let participants interact with the artifact rather than rely solely on a demonstration by its designer. Build rough and rapid.

Assign roles to facilitators and observers. Agree how comments, actions and difficulties will be captured. Keep the session focused on learning rather than defending the proposed solution. Hold a conversation around the prototype. “Can you show me how you would use this?” “Tell me what you are thinking here.”

Ask participants to explain what they believe is happening and what they would do next. Observe where the concept supports that understanding and where they hesitate, improvise or seek help.

Listen without teaching away the problem

A facilitator can unintentionally make a weak concept appear successful by explaining each step or supplying missing information.

Use neutral prompts and allow participants to reveal their interpretation. When assistance is necessary, record it along with the point at which the person became stuck.

Follow observations with questions about purpose and reasoning. What did the participant expect? Which information did they look for? Why did they choose that action?

Separate a preference from evidence about task performance. “I like this one” may reflect appearance or familiarity. Successful completion, appropriate interpretation and manageable effort provide a stronger basis for assessing whether the concept addresses the need.

“Every explanation the facilitator must supply is a question about what the design expects the user to know.”

Examine the complete experience

The product interaction belongs within a wider job. Test preparation, readiness, execution, monitoring and what follows, wherever these activities affect the concept’s value.

A device may work well once configured while setup introduces avoidable complexity. A result may be easy to view while the professional still needs to transfer it manually before acting.

Include relevant dependencies on caregivers, staff, information and other systems. Investigate whether the concept reduces burden for one participant by increasing it for another.

Use realistic scenarios appropriate to the development stage. Important exceptions and recovery steps deserve attention alongside routine success. An incomplete or interrupted task can reveal weaknesses that a carefully guided demonstration leaves hidden.

Make cognitive burden visible

Ease of use involves what a person must notice, understand, remember, decide and physically do. A tidy interface can still demand substantial mental effort.

Human performance depends on limited attention, working memory and feedback. Examine how the concept supports those processes during a task.

Does it make the relevant information perceptible? Can the user understand its meaning? Is the next action clear? Can they recognize whether that action succeeded?

Look for unnecessary steps, demands to remember information across screens and alerts that interrupt without helping a decision. The best change may simplify a sequence or improve feedback rather than add another feature. Confirm its effect through observation and appropriate measures.

Measure effort alongside task success

Choose measures that reflect the priority outcome and the study’s purpose. These may include completion time, errors, requests for help, repeated actions or failure to notice important information.

Subjective workload measures can complement observed performance. NASA Task Load Index can help structure participants’ assessment of workload, while PURE provides an expert assessment of the effort required to complete tasks. Satisfaction ratings on a Likert scale can be used to compare concepts directly with each other and a baseline solution.

Do not treat one measure as a complete account of usability. Faster completion may accompany misunderstanding. A participant may complete the task correctly while reporting effort that makes repeated use impractical.

Interpret results together and examine the conditions under which they occurred. Record the limitations so decision makers understand what the test has established.

Test demand separately from technical feasibility

Pretotyping examines whether people take meaningful action toward using or adopting an idea before the full solution exists. It addresses a different question from whether a working product can be engineered.

A clearly described concept, service simulation or expression-of-interest process may provide evidence about engagement. The strength of that evidence depends on the action required and the conditions surrounding it.

A click demonstrates less commitment than a substantive implementation conversation. Neither establishes clinical efficacy or successful device use.

For medical concepts, keep representations accurate and make the test status clear. Interpret behavioral evidence alongside clinical appropriateness, access requirements and the responsibilities of the people making adoption decisions. Interest alone cannot establish that the solution should proceed unchanged, particularly when workflows in the clinical setting are involved.

Refine the value proposition with evidence

A value proposition explains which customer the concept serves, the job it supports and the difficulties it intends to reduce. It should connect directly with the outcomes identified through research.

Compare that proposition with the evidence from concept tests. Did participants experience the intended benefit? Did another difficulty become more important? Did the concept require assumptions that the intended setting cannot support? Did the concept do something better than a competitor’s current solution?

Capture both positive and negative findings. Use them to revise the concept, the proposition or the team’s understanding of the need.

Then test the important changes. Iteration should answer a question left open by the previous round. Repeating sessions without changing the artifact or research objective can accumulate comments while leaving uncertainty untouched.

Keep early findings within their proper limits

Early concept validation supports decisions about direction and investment. It does not establish all the evidence needed for a finished medical device.

Technical performance, reliability, clinical benefit and intended-use safety require appropriate subsequent work. A convincing simulated experience may depend on functions the team has not yet demonstrated.

Make these boundaries visible in the decision record. State who participated, what was tested, the conditions, findings and unresolved issues. Explain how those findings affect the next commitment.

Overall, having a “winning concept” in hand, backed by user Experience Café data along with user needs in the form of outcome statements formally structured with a direction of improvement and unit of measure, sets a development team up for success. The outcome statement user needs provide a solid foundation for the design history file.

Rapid prototype experience testing gives the team answers to key decisions and sets a target to pursue early on. This results in faster development cycles, more robust regulatory submissions, and better returns on investment spending.

A Value Management Office can connect this learning with resources and stage decisions. Testing should help the organization commit at a pace justified by evidence and concentrate effort on the uncertainties that govern progress.

“Early validation creates value when it changes the concept or the commitment before an unresolved assumption becomes expensive.”

What comes next

Examine one proposed investment. Which assumption could defeat the concept, and what is the least costly credible way to test it?

Prepare an appropriate artifact, involve representative users and observe meaningful tasks. Review the evidence, revise the concept and record the next learning decision. This gives Patient Centered Design a practical route toward solutions people can successfully use and organizations can responsibly develop.

MedTech

Developing medical device concepts around patient needs

David Hodes

A development team can become committed to a concept before it has seriously considered an alternative. The idea is familiar, a senior sponsor favors it or an available technology makes it attractive. Detailed engineering begins while the reason for choosing that approach remains largely untested.

Concept engineering provides a more disciplined basis for commitment. It develops several viable approaches, compares them against important needs and combines their strongest elements where doing so improves the solution.

Within Patient Centered Design, the purpose is to identify a concept that can help patients and healthcare professionals accomplish their jobs successfully. Question 2.1.1 examines the effectiveness of that work.

“A winning concept earns its place by addressing important needs and surviving comparison with credible alternatives.”

Start with a problem the team agrees on

Concept work begins with a concise problem statement and an agreed objective. Both should describe the progress the intended users need, rather than assume the answer.

An illustrative problem might concern a clinician’s difficulty identifying whether the information needed for a treatment decision is complete. “Build a new dashboard” commits to one response before exploring the problem.

Clarify the users, operating context and desired outcome. Review the patient journey and the clinical work surrounding the interaction. Establish which unmet needs the concept must address.

Resolve major differences in interpretation before the selection exercise. A mechanical engineer, clinician and commercial leader can otherwise compare ideas while quietly applying different definitions of success. Shared language makes their conversation more useful.

Bring priority needs into the criteria

Concept selection needs criteria grounded in research and requirements. Include the outcomes patients and professionals consider important, particularly those existing approaches satisfy poorly.

Then identify necessary capabilities and constraints. Relevant considerations may include safety, reliability, usability, manufacturing, service requirements, implementation and the resources available to develop the solution.

Separate mandatory conditions from characteristics that permit tradeoffs. A concept that cannot satisfy a necessary safety condition needs resolution or rejection; attractive performance elsewhere cannot compensate for that failure.

Define the criteria clearly enough for consistent comparison. “Easy to use” leaves the team guessing. The number of required actions, time to complete an activity or ability to recognize readiness can provide more concrete grounds for conversation, depending on the need.

Generate options before narrowing the field

Concept engineering encourages broad idea generation, with constraints applied more deliberately during selection. This gives the team room to consider alternatives before familiar solutions dominate.

Use sketches and short descriptions to make ideas visible. Invite different functions to contribute approaches, concerns and connections the team might otherwise miss.

The concepts should represent meaningful alternatives. Several versions of the same preferred design may leave the underlying assumptions untouched.

Explore how the required function could be achieved differently. Could information arrive earlier? Could preparation become simpler? Could a service change remove work the proposed product was intended to automate?

Make concepts understandable enough to compare

A concept should communicate its operating principle, important interactions and dependencies. A simple drawing can expose differences that remain hidden in a verbal description.

Explain what the patient or professional would do, what the system would provide and what supporting resources would be required. Include the activities before and after the visible product interaction.

Build enough detail to support the decision at hand. Early concept selection does not require complete engineering specifications, but an appealing image alone gives little basis for comparison.

Record what is known and what is assumed. If one idea appears stronger because its description omits difficult tasks, the matrix will reward an incomplete account of the solution. Consistent descriptions support a fairer evaluation.

Use a simple comparison matrix

The Pugh matrix provides a structured way to compare concepts. List the criteria as rows and the candidate concepts as columns. Choose a reference concept, known as the datum, against which alternatives will be assessed.

For each criterion, judge whether a concept is better than, similar to or worse than the datum. The simple notation is positive, same or negative.

Discuss the basis for each rating. Record supporting evidence and unresolved questions. The matrix makes differences visible and gives the team a shared place to examine them.

Totals can help identify promising candidates, but they should not make the decision automatically. The significance of a weakness depends on the criterion involved. Several small advantages may leave a concept unable to address its most important requirement.

“The matrix is useful when it exposes the reasoning behind a choice and the evidence still needed to defend it.”

Match the method to the available evidence

Early conversations often rely on incomplete information. Simple comparisons can be more useful than elaborate scores that suggest a level of precision the team does not possess.

Weighted methods may help when criteria genuinely differ in importance. Paired comparisons can support more structured judgments about relative priority. When measured engineering performance is available, use those data directly in the evaluation.

Choose the method for the decision and evidence, rather than adding complexity by default. Explain how weights or ratings were established and which assumptions could change the result.

Check whether the leading concept remains attractive under reasonable changes to uncertain judgments. A decision that reverses after a minor rating adjustment deserves closer examination before the organization makes a substantial commitment.

Look for stronger combinations

Hybridization is a central part of concept engineering. A concept with several strengths may also have a weakness another option handles well.

Examine whether the useful elements can be combined into a stronger approach. For an illustrative monitoring solution, one concept may support straightforward preparation while another provides clearer feedback when a connection fails.

A hybrid might preserve both benefits, but it needs its own evaluation. Combining features can introduce new interfaces, added complexity or conflicting operating requirements.

Redraw the combined concept and reassess it against the criteria. Investigate whether it actually removes the weakness or merely moves the burden elsewhere. The aim is a coherent design whose interactions work together, rather than an accumulation of everyone’s favorite features.

Manage disagreement as a source of learning

Concept selection brings technical judgment, professional experience and organizational interests into the same conversation. The quality of that conversation affects the outcome.

Agree on meeting ground rules and use a team with relevant expertise across functions. Ensure that a strong personality or senior title does not become a substitute for evidence.

When disagreement cannot be resolved, capture the question and define an action. Assign an owner, the evidence required and a completion date. An independent facilitator can help when team dynamics obstruct useful comparison.

The method can make judgments more transparent, but it does not eliminate subjectivity. Leaders should expect the team to explain its reasoning and show how important uncertainties will be resolved.

Test the assumptions that could defeat the concept

The leading idea may depend on uncertain technical performance, user behavior, supplier capability or implementation conditions that significantly affect time to market. Identify those dependencies before detailed development absorbs significant resources.

Plan focused experiments, research or prototypes to address them. Match each activity to a question that could change the selection decision.

If a concept depends on clinicians adopting a new preparation routine, a technical bench test will not establish that assumption. If it depends on a material performing under particular conditions, user enthusiasm will not resolve the engineering question.

Prioritize uncertainties with serious consequences for the concept’s viability. Early learning should help the team avoid expensive redesign while preserving options until the relevant evidence is available.

Select a direction with a clear explanation

At this point in the design process, using patient and healthcare professional journey mapping satisfies the Empathy stage of Design Thinking. Understanding the jobs to be done and desired outcomes satisfies the Define stage of Design Thinking. Concept engineering at its core is a practice that ensures the best solution direction is chosen before significant expense is incurred and satisfies the Ideate stage of Design Thinking.

With a selection of “winning concepts” in your pocket, complete the Design Thinking stages by rapidly prototyping and testing solutions with customers to gather data on which concepts your customers desire most. The Design Thinking stages provide an explanation of why a solution is preferred and a record of why alternatives were not chosen.

Retain the criteria, important comparisons, hybridization decisions and unresolved risks. This record helps future teams understand the consequences of a proposed change.

Selection establishes a direction for further learning and development. It does not prove that the solution meets user needs or performs safely in practice. Concept validation and subsequent engineering evidence must support those conclusions.

Keep the relationship with the original needs visible. A chosen architecture can gradually lose important benefits as specifications and implementation choices evolve. Traceability helps the team notice when a tradeoff undermines the reason the concept was selected.

Connect concept decisions with enterprise priorities

The Voice of Business and Voice of Technology should inform selection alongside customer evidence. A concept needs a realistic development route and an operating environment capable of supporting it.

A Value Management Office can connect these choices with resources and portfolio priorities. Systems thinking helps the team examine whether the proposed improvement addresses a constraint governing patient access or successful use.

Evaluate the complete contribution. A technically impressive device can introduce training, service or clinical work that limits its benefit in the wider pathway.

“Concept engineering should leave the organization with a reasoned choice, a manageable learning plan and a clear connection to patient benefit.”

What comes next

Review one current concept decision. Can the team show credible alternatives, agreed criteria, the basis for selection and the assumptions requiring further evidence?

Bring the relevant disciplines together, compare options and examine stronger combinations. Then test the uncertainties that could change the decision. That is how Patient Centered Design builds a defensible route from important needs to better products.