Jacob Moorman
Independent
[email protected]
For Randy
This page is a HTML conversion of the original PDF paper.
Resource Constrained Action Model (RCAM) is a problem-solving technique designed for the unique patterns of resource problems. Using RCAM, an individual or team can attack a resource problem from several directions. RCAM visually organizes a problem and then synthesizes a diverse set of actionable recommendations. This technique resists premature closure bias. This paper describes the unique characteristics of resource problems, the RCAM model, and how RCAM can be used as a standalone analytic technique, in tandem with other structured analytic techniques, or with generative AI.
Contact the author for paid consultative support, commercial licensing, or to support our work as a patron. The author can be reached at [email protected]
Resource Constrained Action Model does not represent the views or experiences of any current, past, or future company or any specific company, organization, or individual’s product, product experience, processes, people, technologies, audit findings, security, risks, or risk decisions. This research has been conducted independently. The included examples are fictional, and the guidance is not intended to be followed. Guidance generated by AI has not been evaluated for quality. See “License Terms” for terms, including additional disclaimers.
Decisions are often made based on resource levels or limitations. SCUBA divers are dependent on the air they are carrying. Subscription businesses are dependent on their subscribers. Factories are dependent on raw materials. Order delivery is dependent on product availability. Project execution is dependent on staffing and time. These are examples of tangible resources, but decisions are also made based on intangible resources, such as the quality of the customer experience or managed risk levels.
Resource decisions are made at strategic, operational, and tactical levels. To aid these decisions, many organizations leverage a structured analytic technique (SAT) to provide a means to think through a resource problem systematically. One well-known structured analytic technique is SWOT Analysis (strengths, weaknesses, opportunities, threats). SWOT Analysis is a general-purpose strategic planning tool not optimized for resource problems or operational or tactical decision making.
Strategic planning is in many ways dissimilar from resource problem solving:
Resource Constrained Action Model (RCAM) is a structured analytic technique designed for strategic, operational, or tactical consideration of resource problems. Individuals and teams can use RCAM. RCAM can be used standalone or in tandem with techniques like SWOT. RCAM can be used on paper or whiteboards, mentally, electronically, and with or without the assistance of generative AI. RCAM can be used on newly discovered greenfield problems or in the middle of an existing problem response. RCAM can be used to produce an initial plan and then to consistently refresh that plan if conditions change.
Implementing the Resource Constrained Action Model (RCAM) requires no formal training or tooling. The technique can be understood in minutes. In this step, we will prepare the model and identify the Subject. RCAM can be drawn on a whiteboard, on paper, or electronically.
We begin by drawing a grid (horizontal and vertical axis). We label the right More. We label the left Less. We label the top Better. We label the center (origin) Same. We label the bottom Completely Different.
We now have positions for More, Better, Less, Same, and Completely Different, representing the five RCAM categories. We can embellish our model with action labels:
A new copy of the model should be drawn for each problem. So we know what problem we are solving, we should note the Subject of this model at the very top. A good subject reflects the constrained resource and the problem we are facing. For example, “Joe’s automobile has insufficient fuel for the 80-mile journey”.
Using the model drawn in Step 1, we will use available information to create a focused view of our problem. We will triage the current state. Each piece of information will be placed in one category of the model. In this step, we want to understand how the resource is constrained and how impact is generated from the resource.
We will begin with the Same category in the model’s center. Within this category, we should record the current resource state. What is the current state of the Subject resource? In our example (Joe’s automobile has insufficient fuel for the 80-mile journey), our current state is 3 gallons of fuel.
Working across the horizontal axis, we are concerned with constraint management. What (if any) losses are reducing this resource? What (if anything) is available to replenish this resource? We can think about using less fuel or getting more fuel. In the Less category, we can note that ½ gallon of fuel is consumed at idle. In the More category, we can note that the nearest fuel station is 37 miles away.
Working down the vertical axis, we are concerned with the impact generated by the resource. How does this resource produce value? For example, the miles per gallon of fuel. In the Better category, we can note 22 miles per gallon.
The Completely Different category is used to inventory the dependencies on the resource and what other resources are available. What are the critical dependencies on this resource? What alternatives are known to be available? This category is our primary space for outside-the-box thinking. For example, what if Joe used another vehicle and no longer depended on his automobile? In the Completely Different category, we can note that Joe has a scooter that gets 70 miles per gallon and has 1.5 gallons of fuel.
We have now formed a focused view of our problem space.
How a problem manifests itself, how the problem response is triggered, and the timeline for response is unique to the situation. Facing a new problem, we need to figure out whether this is a real problem that warrants action or a false alarm. We also need to determine how much time we can take to mount a response to the problem before we are impacted.
Using the evidence we collected in Step 2, we will evaluate if a critical state has already been reached or will be reached, as well as the impact. In this step, we will consider how soon we expect to reach a resource-critical state if nothing changes. First, we will describe what constitutes a critical state.
Adding to our information in the Same category, we should record what we believe is the critical resource state. In what state would we consider this resource critical – that it would no longer be able to support operation at the required level? In our example (Joe’s automobile trip), the critical state would be 0 gallons of fuel since the automobile would no longer operate.
Is the critical state always zero? No. For many types of resources, such as staffing, a critical state is greater than 0 because full operation is no longer possible absent minimal staffing. Some problems, such as product inventory, may have multiple critical states, reflecting that there can be too little or too much of a resource on hand. We may also purposefully choose to define a critical state higher than our current state to use the model to consider growth problems requiring increased resource capacity or to provide a greater safety margin.
With the critical state defined, we can now determine our trajectory. Our trajectory will indicate whether we are likely to reach a critical state and when that is likely to happen. If we will never reach a critical state or the impact of reaching a critical state is insignificant, we can present that this is probably not a real problem, and the analysis can stop here. We otherwise continue analysis with a better understanding of the available window for response before a critical state will likely be reached.
If a critical state has already been reached and we have been impacted, we can know this is a real problem. Below the Subject line in the model, we can state, “Critical state has already been reached”. We may further articulate the impacts we have observed from being in the critical state.
If we have not yet reached a critical state, we either have caught the problem early, conditions have recovered, or this is not a real problem. We need to determine which case applies.
For problems with a tangible resource, we can consider the run rate of resource consumption by considering over time the current state (Same), resources added (More), resources lost (Less), and efficiency (Better). A graph can help us understand changes in run rate and whether we are on the trajectory to reach a critical state.
For problems with an intangible resource, we can consider whether requirements or acceptable risk thresholds are likely to be breached. Governance or regulation may define what is considered reasonable.
To convey urgency, below the Subject line in the model, we can state when we believe a critical state will be reached and the expected severity of impact if a critical state is reached. It is one thing to run out of fuel in an automobile a short distance from a fuel station and quite another to reach fuel exhaustion at altitude in an aircraft.
Using the evidence we collected in Step 2, we can now develop a list of possible solutions. In this step, we want to consider how to remove constraints and negatives and how to retain desirable or necessary conditions. Form a bulleted list of ideas below or beside the model. Attribute each idea to one of the categories (More, Better, Less, Same, Completely Different).
The More category is focused on applying resources to extend the runway to criticality by infusion. What available or acquirable capacity could help replenish the resource? Capacity may come from reserves, retasking or reassigning existing resources, acquisition or purchasing, promotion, or marketing. In our example, Joe might purchase fuel at the fuel station. Joe might also siphon fuel from his scooter into his automobile. Both approaches would apply resources, so they belong in More.
The Better category is focused on increasing efficiency, optimizing, and increasing the value generated per resource unit. How can the resource more optimally produce value? In our example, Joe might alter his driving habits to keep his car in Eco-friendly mode and more efficient than the 22 miles per gallon he usually averages. Business problems involving customers can often reflect efficiency in dollars, as it is more efficient to make $1000 with a $200/customer average than a $20/customer average.
The Less category is focused on reducing waste or losses to extend the runway to criticality by bleeding less. How can current losses be counteracted? In our example, Joe might turn off his vehicle whenever he parks to conserve fuel rather than idling. It is often cheaper and more ethical to preserve existing resources rather than acquire more if waste can be reduced without excess impact.
The Same category is focused on maintaining desirable or necessary aspects of the status quo. What aspects of the current state do we want or need to keep? Ideas in this category minimize change or variation from the current state, accept the risks of inaction, and delay response while information is gathered or the problem is watched. In our example, Joe might look to get on the road, hoping to find an additional fuel station. Knowing he is low on fuel, he might also be more observant of the fuel gauge. It can be helpful to improve our understanding of the problem and detect changes.
The Completely Different category is focused on rejecting current conditions by eliminating the dependency on the constrained resource or rejecting a current requirement that stems from that resource. Ideas in this category take the “nuclear option” and may discard or cannibalize the existing audience, product, or mission to implement something completely different. How might we eliminate dependencies on this resource? In our example, Joe can now consider options that eliminate his automobile from the equation or change the trip experience. Joe could take public transportation. Joe could take his scooter. Joe could cancel the trip.
Our model is now fully populated. By considering each category, we have reduced the likelihood of premature closure. If we have one category with a long list of ideas and another with none, we can recognize the need to consider the situation further. The alignment of evidence to ideas or evidence gaps can also help us to identify where we may have remaining blind spots that require consideration.
With our list of ideas from Step 4, we can now consider the viability of ideas, package them as a solution, and form a recommendation. In this step, we will consider individual ideas and groups of ideas.
What constitutes a viable idea will depend significantly on the environment. Governance, or regulations, or the team’s charter may define specific criteria to determine whether a solution is viable. If we are unsure about how the people or politics of the situation might complicate solution viability, we might need to enlist a domain expert to guide us. Some things that may be appropriate to consider in evaluating risk vs. reward are:
In determining whether an idea is viable, we should recognize that viability can change over time and may depend on implementing complementary solutions. We should document our understanding of idea non-viability in case that idea viability changes later or needs to be explained.
Our recommendation should be formed from one or more viable ideas. For some problems, one recommendation is sufficient. In other cases, we may need to present several cost options or short-, medium-, and long-term recommendations.
If appropriate, a copy of the RCAM model can be prepared to present a proposed solution package concurrently with supporting evidence. It can be helpful to reference More, Better, Less, Same, and Completely Different to demonstrate how the problem would be attacked from several directions.
The following samples include a fictional case, some with sample data, and draft ideas.
To practice RCAM, consider:
Subject: An air leak is reducing the remaining air time on the fictional LADON-5 rocket ship.
Trajectory: 15 hours 17 minutes to air criticality, when all on board the ship will die.
Ideas
A bit more than 15 hours of air may not be enough, or it could be plenty. If the ship is minutes away from its destination, the astronaut may choose to wait this out. More likely, loss of air time is a critical situation that warrants immediate attention. Depending on the location of the leaks, it might be most viable to close compartments, resupply from reserves, and reduce air consumption until repairs can be made.
The astronaut knows their ship best and can use the model to assess options rapidly.
Subject: Subscriber falloff at the fictional Bougainvillea Courier print newspaper.
Trajectory: 14 months to subscriber criticality when the paper would be forced to cease operations.
Ideas
There has been a global trend in which people get their news online. Since the newspaper does not know why people are leaving, they should likely start with some research to rule out seasonality or other causes. A significant shift to a digital format would unburden the paper from print costs. However, this would also require research on whether the market and existing subscriber support are sufficient to pivot. With some research,, they will be better equipped to consider their options.
Subject: A fictional factory making three products now has excess demand for one product.
Trajectory: Demand already exceeds supply. (Money is being left on the table.)
The factory can envision various options in response to the shortfall in available production capacity. Depending on the shortfall’s duration, the possible solution’s overhead, and the amount of money involved in unmet demand, this may be a time to wait to see if this is a short-term trend, a time for strategic investment, or a balanced approach with smaller investments. If the production demands are expected to increase more sustainably, it may be time to consider doing something completely different. A decision could be made using data, understanding execution capabilities, and risk appetite.
Subject: The fictional company is determining the next steps from an initial risk audit.
Trajectory: The risk identified to date exceeds the board-defined acceptable risk threshold.
Typical risk management activities have been included in the model above. These can be replaced with specific details when modeling a problem.
RCAM can be used in several ways. One way is to aid the evaluation of risk responses, either by a single risk response or by considering a risk portfolio. By mapping existing risk management activities or risk responses with RCAM, we can consider whether the balance between RCAM categories is appropriate or if some response options have not adequately been considered. This approach can illuminate capability blind spots, such as the need to identify insurance options to support Risk Transfer.
RCAM can also be used to support problem response. The ability to use RCAM at any point in the response process and revisit prior RCAM analysis can be beneficial when an existing proposed or implemented risk management approach is found to be non-viable or insufficient. RCAM can aid in considering solution viability and help identify whether complementary solutions exist.
Subject: The fictional company has determined that product users struggle to complete critical tasks.
Trajectory: Key customers have declared intent to leave unless issues are resolved.
A sampling of approaches has been included in the model above. These can be replaced with specific details when modeling a problem.
Designers, engineers, product teams, support technicians, sales, marketing, and the management team can all want a high-quality product or service experience while being saddled with a product that needs improvement.
The user experience (UX) discipline provides tools and methods for examining the problem space and identifying the optimal solution for the user and the business. While experience suffers for some products and businesses who think that analysis is too expensive, many businesses have their improved chances of success by adopting lightweight “Guerilla” (sic “Gorilla”) style UX analysis with rapid turnaround and low cost.
If the product issues cannot be resolved, the data gathered can provide the critical insights needed to re-envision the product or service experience. RCAM may help identify blind spots in user experience strategy and problem response.
Subject: Fictional service customers are struggling to implement new regulatory requirements.
Trajectory: Key customers have declared intent to leave unless issues are resolved.
A sampling of departmental participants and potential actions is envisioned in the model above to support a 360-degree view of the problem. These can be replaced with specific details when modeling a problem.
When industry standards or regulatory changes are coming (or have arrived), it can be an “All Hands” response, drawing people from across the business. Marketing and Sales may need to find new clients who are not impacted by the issue. HR or partnerships may need to expand their available expertise. If critical problems cannot otherwise be resolved, seeking regulatory relief or restricting the product market may be deemed necessary but could have substantial risks.
RCAM may help to organize the problem and possible solutions while ensuring a highly democratized process that considers stakeholder input and, in so doing, reduces the potential for blind spots.
AI is not one thing. It is dozens of technologies, evolving with new versions and behavior and underlying data. It is non-deterministic, and can generate different outputs given the same inputs. It may comply with its instructions. It may be firewalled from external data, refreshed periodically, or continuously accessing new information. It may contain biases within its weighting of information. It may produce viable results and non-viable results, and it may not be possible for us to determine which we have been provided.
Response from ChatGPT 4o 2024-08-13:
The text you provided is largely accurate in its description of AI. Here’s a breakdown: [...] "It may produce viable results and non-viable results, and it may not be possible for us to determine which we have been provided." This is generally accurate but requires some nuance. AI systems can indeed produce both viable and non-viable results. However, determining the quality of the results often depends on the context, the availability of verification methods, and the expertise of those evaluating the output. While it can be challenging to assess the quality of AI-generated results in some cases, it is not always impossible. Overall, the text accurately reflects the complexities and characteristics of AI systems. |
Structured analytic techniques have many uses. When humans interact with AI, structured analytic techniques can be used to provide a common structure to help organize and improve understanding of answers. Structure can also make it easier for us to systematically verify answers are correct or optimal.
Response from ChatGPT 4o 2024-08-13:
| Overall, the text correctly highlights the role of structured analytic techniques in enhancing the interaction between humans and AI by improving organization, understanding, and verification of AI-generated outputs. |
In the following example, we will use RCAM with generative AI to modulate the AI output. As we open our minds to the way this technology can be used, remember that there are many other ways we can use generative AI, such as sanitizing or presorting input data, and generating exhaustive lists to consider what we might have missed. Working with human-in-loop we remain responsible for the resulting work. AI cannot currently think for you. Understand risks before integrating AI into your analytic process.
Let us consider an overturned boat with people in the water who need to survive.
Responses from ChatGPT 4o 2024-08-13, combined:
When a boat overturns and sailors find themselves in the water, there are several steps they can take to improve their chances of survival:
|
Prompt: Consider the situation we discussed where a boat overturns and sailors find themselves in the water. We want to determine how these swimmers can improve their chances of survival once already in the water. Please use RCAM to structure your response. [an earlier RCAM draft was uploaded]
Response from ChatGPT 4o 2024-08-13:
Using the Resource Constrained Action Model (RCAM) to determine how swimmers can improve their chances of survival once in the water after a boat overturns, we can analyze the situation as follows: RCAM Model Subject: Survival of sailors in the water after a boat overturns. Trajectory: Immediate danger due to exposure, potential hypothermia, drowning, or exhaustion. Same (Maintain Status Quo)
Less (Reduce Losses)
More (Apply Resources)
Better (Improve Efficiency)
Completely Different (Reject Current Conditions)
Estimated Trajectory
Packaging Viable Solutions
Recommendation
By following these structured steps using RCAM, swimmers can systematically assess their situation and make decisions that maximize their chances of survival in the water. |
New options and prioritization have emerged. By directing the conversation to use RCAM, we are now certain that there are options which reduce losses, optimize efficiency, and consider the possibility that more radical options might be the only options viable.
On the date of this interaction, ChatGPT could not successfully diagram the response, but could provide a detailed description of how the response could be diagrammed, with embellishments such as:
| The background should depict a maritime scene with an overturned boat partially submerged in the water. The water should be slightly choppy to indicate the difficulty of the situation. |
The following simple diagram was prepared with Excalidraw (excalidaw.com). An imprecise font was selected to represent the imprecise nature of this first draft.
Some environments allow or require a prior analysis to be revisited when conditions change. This may be triggered by monitoring the current status, which indicates that a point of criticality has been reached, that the point of criticality has been averted, that inputs have changed, or that solution viability has changed. We will refactor and communicate.
When significant problems strike, the problem might be seen from many different perspectives, each with different data. We can benefit when we broadly consider who can provide relevant data.
By tracking not just the evidence but the source of evidence used in decision making, we can corroborate different data sources to increase trust in the evidence, change to a more authoritative data source, or know where to source updated data to reflect changes in the problem.
As decisions are implemented, RCAM enables project teams to know what was known at the time of the decision. This knowledge capture can help identify possibly new conditions requiring disclosure to the approver.
RCAM is a MECE model. MECE means Mutually Exclusive, Comprehensively Exhaustive. In a MECE model, an item can be categorized in exactly one category, categories are non-overlapping, and categories cover all possibilities. The rigid MECE structure allows RCAM to resist premature closure bias because we can observe if one category is loaded with ideas while another is empty.
When categorizing, the axes and origin definitions are the primary sources for disambiguating correct item placement. The origin is about maintaining the status quo or understanding the problem. The horizontal axis is about resource management. The vertical axis is about the impact produced using the resource.
If an idea would add to the constrained resource, it is More, while if it would use the constrained resource to achieve greater things, it is Better. If an idea aims to reduce customer attrition, that is Less, while if we need fewer customers to achieve the same outcome, it is Better. If we are changing how we collect information about the problem, it is Same. If we are making a revolutionary change, it is Completely Different, while any evolutionary changes would be in the other categories.
RCAM’s model can be used in brainstorming, independently of the structured analytic technique. The labels or categories in a model can be used to guide our brainstorming. When the model is a MECE (Mutually Exclusive, Comprehensively Exhaustive) model like RCAM, and we place at least one idea in each category, we can enforce a level of idea diversification. When working alone on a time-critical problem, RCAM can provide a helpful structure to our mental brainstorming or think-aloud brainstorming. More, Better, Less, Same, Completely Different.
While RCAM can be used to think about problems we are currently facing, RCAM can also be used to reconsider past problems or consider potential future problems. In our AI example, we considered how sailors could respond if they were in the water. While RCAM can be used to size up a problem quickly, it is better to think about low-likelihood/high-impact scenarios like this in advance. If a problem has been experienced previously, actual data may be available to support a more robust analysis.
In our astronaut air leak example, we used sample data about air pressure and reserves. We considered options like sealing off parts of the ship or reducing air usage. Critical situations like an air leak on a rocket ship likely require very rapid evaluation and action. If our astronaut needs to collect data from various systems manually and then manually manipulate those systems to implement the desired changes, time overhead may become a factor in determining the actions they can take before they run out of air. By modeling a complex problem response in advance, the user interfaces of the rocket ship could be designed to place situationally relevant information close at hand and to provide access to the automation of relevant actions. The astronaut would be well-positioned to focus on the problem and take the necessary actions if they can override defaults and also perform actions manually.
RCAM is optimized for resource problems. What are some indicators that a problem is a resource problem?
Qualitatively, describing the problem in words, resource problems might be expressed in dire terms about resource criticality, bounds, limits, or limitations. The resource may have a description like customers, time, or money.
Quantitatively, describing the problem in numbers, resource problems might be expressed in dire numbers that only the management team and specific experts understand without explanation.
The trigger for problem response can vary. In some cases, the response may begin when the business implements or enhances its measurement or monitoring program, discovers that its prior understanding of a situation was incorrect, and determines action is warranted.
In other cases, the response begins when current conditions are not understood. People often fear the unknown. It is difficult to determine how much time is available for response without an understanding of current conditions and the runway to criticality. It may be necessary to rapidly size up the current situation, determine how bad the problem is, and then enhance that understanding during response.
The response to some problems begins from an already critical state. The status quo may represent a point where criticality has already been reached, and some actions may already have been taken. It is vital in these cases to understand what has already been tried, the impact of these actions, and evaluate any unintended consequences. This consideration can help guide more successful subsequent action.
For overt problems (like our astronaut), the resource involved is clear (an air shortage). For business problems, the resource involved may be less clear. Consider what records are the key records in the business database, how customers are billed, or what the business is in the business of doing. Is this an inventory problem? A business selling widgets might have a resource constraint on sellable widgets.
Remember that not every condition is an emergency or a problem. Change happens continuously. Our ability to accept or adapt is a critical factor. Awareness of conditions can trigger alarm when everything is okay. RCAM can help us to consider whether we are facing a genuine concern or something regular.
What we know and what exists are examples of positive space. Examples of negative space are what we do not know and what does not exist. Unknowns dwell in the negative space, whether we are aware of them or not.
When acting on resource problems, changes that reduce losses (Less), apply resources (More), or make improvements (Better) are usually either understood or understandable. These actions build on something that already exists. We typically know that we can depend on our existing domain knowledge or are aware that domain knowledge is insufficient or no longer available.
If, instead, we determine that the situation is unworkable, that we must reject the current conditions and do something Completely Different, we are stepping into the unknown. This action has the gravity of taking the nuclear option, accepting that the grass is only greener on the other side, that the baby can be emptied with the bath water, that we can trade the customers we have for the customers we want, that the extreme choice is the right choice, that we have the moxie to make this decision and the resolve to stand by it with all resulting consequences. The Completely Different option should not be taken lightly.
It is not always safe, viable, or beneficial to reject problems and accept an entirely new set of problems. Conditions could improve, could become worse, but at best are unknown.
Where we had domain knowledge in our current problem, taking a Completely Different option, we may have gaps in domain knowledge if everything is different, and we may not know what we do not know (unknown-unknowns). We may not be equipped to execute the new strategy, which may take longer. Our competitors may not have these same blind spots, and they may already understand the hidden constraints we are blissfully ignorant. Our existing customers and employees might not understand the pivot. We may be unable to make these changes concurrent with operating the current business. Fear, uncertainty, and doubt abound. However, then again, it might be marvelous.
This is a time to consider risks carefully within the decision-making process.
SWOT Analysis is a structured analytic technique broadly used in business and analytic environments. With this technique, data and ideas are classified into the categories of Strengths, Weaknesses, Opportunities, and Threats.
RCAM is a structured analytic technique designed for resource problems. With this technique, data and ideas are classified into the categories of More, Better, Less, Same, and Completely Different.
SWOT and RCAM have several shared benefits, and both are:
SWOT has characteristics that RCAM is designed to mitigate:
RCAM can be used in several ways to overcome the limitations of SWOT:
| RCAM only | For resource problems, use RCAM as an alternative to SWOT. |
|---|---|
| RCAM then SWOT | Use RCAM first to evaluate a resource problem, then constrain the SWOT exercise to a scope that explores or evaluates the relevant RCAM findings. |
| SWOT then RCAM | Use SWOT first, then use RCAM to focus on an identified resource problem and produce an actionable recommendation. |
RCAM can be implemented as an initial analytic or after some analysis has already occurred. When an ideas list already exists, the list of synonyms below can be used as an expedient map of keywords to RCAM categories.
After an initial sort, corrections will be needed. More and Less items relate to the management of the constrained resource; Better and Completely Different items relate to the impact generated from the resource; and the Same items relate to existing conditions.
| Keyword | Category Guess |
|---|---|
| Acquire | More |
| Adapt | Better |
| Adopt a new market | Completely Different |
| Adopt a new use case | Completely Different |
| Amplify | Better, More |
| Assess | Same |
| Assign | More |
| Assume | Completely Different |
| Build | Better |
| Cannibalize | Completely Different |
| Conserve | Less, Same |
| Create a new line of business | Completely Different |
| Create a new product | Completely Different |
| Create a new service | Completely Different |
| Defer | Same |
| Delay | Same |
| Deprioritize | Completely Different, Less |
| Enhance | Better |
| Expand | More |
| Experiment | Completely Different |
| Hire | More |
| Improve engagement | Better |
| Improve experience | Better |
| Improve longevity | Less |
| Improve a product | Better |
| Improve a relationship | Less |
| Improve service | Better |
| Include | More |
| Keep | Less, Same |
| Know | Same |
| Market | More |
| Measure | Same |
| Merge | Completely Different |
| Monitor | Same |
| Omit | Less |
| Optimize | Better |
| Prevent | Less |
| Prioritize | Better, More |
| Promote | More |
| Purchase | More |
| Reduce waste | Less |
| Reject | Completely Different |
| Retain | Less, Same |
| Revolutionary | Completely Different |
| Satisfy | Less |
| Shut down | Completely Different |
| Speculate | Completely Different |
| Stagnate | Same |
| Support | Less |
| Understand | Same |
| Wait | Same |
Resource Constrained Action Model © 2024 by Jacob Moorman is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.en
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Resource Constrained Action Model was initially released on 2024-08-29. This document is the third release of Resource Constrained Action Model, dated 2024-11-07.