Introduction to Means-Ends Analysis
Means-End Analysis is a problem-solving technique that identifies the current state, defines the end goal and determines the action plan to reach the end state in a modular way. End Goals are split into sub-goals, and sub-sub goals and then action plans are drawn to achieve sub-goals first and then move towards achieving the main goal progressively. Most of the problem-solving strategies will have either forward actions or backward actions.
But MEA will have a mixture of action plans in either of the directions to solve the problems in a modular way, in the sense that it attempts to solve the major problems first and get back to minor problems later or vice versa. Means-End Analysis (MEA) is a problem-solving technique, and it is used in AI programming, General Management, and Psychology areas. Let us study its features in detail in this article.
What is Problem Solving?
Problem-solving has a different meaning in several contexts. In computer science, as part of Artificial Intelligence, it refers to the application of algorithms, heuristics, and root cause analysis to find solutions. In psychology, it deals with the logic of reaching a desired goal state from the problematic current state cognitively by introspection, analysis, and experimentations.
The problem-solving activity starts with gathering data on the problem using surveys, Brainstorming and identifying gaps. Failure Mode Effects analysis helps to identify potential problems in a system. The next step is the Analysis of the data gathered using the Fishbone diagram, Pareto chart, etc., to find a solution.
Where is it used?
Means-End Analysis is used in the following disciplines:
- MEA is a creative problem-solving technique used in Artificial Intelligence application for a quite long time. From the search space of possible solutions available in the system, AI selects the best possible solution by applying the right search strategy or algorithm. This algorithm deals with an initial state and end state and the action plan and movement in forwarding and backward directions. This recursive algorithm optimizes the search operation and achieves the end goal with minimum effort.
- In the General Management area, MEA facilitates organization planning to attain the goals. Goals are split into objectives, and each objective is further split into action steps. Breaking up goals into actionable tasks helps management to reach the end state successfully.
- MEA helps in implementing Business transformation projects by identifying as it is a state, defining to state and listing the new business processes to be developed. These new processes are further split into sub-process for effective implementation.
- In personal life also one can follow MEA methodology to solve problems or attain a specific goal. It helps to manage overwhelming situations by clearly understanding the reasons for current status and reach the end status by executing the planned actions. MEA helps in avoiding frustrations and mental depression and lead a peaceful life.
How does the Means-Ends Analysis work?
Let’s learn how it works.
- Measures the current state and identifies the problems and pain points faced in the current state.
- Defines the to-be state (goal state) to be reached.
- Splits the goals into sub-goals and sub-goals into further sub-sub-goals. For example, long term goals can be split into short term goals and further.
- Determines the actions to be executed to reach the goal state.
- Connect each sub-goal with executable actions.
- Includes all intermediate steps, relevant actions to address the issues faced in the current state.
- Makes these steps detectable and device ways and means to track even small changes in the actual and to-be state.
Takes corrective actions wherever it is required MEA steps are depicted below:
Algorithm for Means-Ends Analysis
The algorithm provides the best possible solution to a problem, and it contains the well defined step-by-step correct resolution to a given problem. One will have to follow this mathematical template of steps blindly, and it is expected to produce the end results. These Algorithms can be used as input to develop computer programs and implement a solution. Test cases can be built for each, and every step and the program can be tested thoroughly before implementing it. The algorithm offers a guaranteed solution by following logical steps, whereas Heuristics follows trial and error, past precedence and judgmental methods in solving the problem and the solution is not guaranteed always.
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Algorithms are generally used where accurate results are expected and the time to complete the activity is not a major criterion whereas Heuristics are used in places where the activities need to be completed in the shortest possible time, and few mistakes can be compromised. Algorithms are deployed in planned activity (e.g. Organization Planning) and Heuristics are used in the emergency activity (e.g. Physician treating a patient should decide the treatment quickly).
The algorithm for MEA consists of the following steps:
- Step 1: Measure the current state of things by doing as is the study and capture the status at a macro level and to a possible micro level.
- Step 2: Capture the deficiency in the current state and avenues for improvements (wish list) and define the goal state (to-be state). Define the to-be state at a macro level and to a possible micro level.
- Step 3: Compare the Current state and Goal state, and if they are at the same level, the problem is resolved.
- Step 4: List the differences between the current state and goal state at macro and micro levels.
- Step 5: Convert the differences into deletions/modifications to current state and new additions.
- Step 6: Define the action to implement the changes as defined in step-5.
- Step 7: Implement the changes and measure the actual results with the planned goals.
- Step 8: Do course correction and achieve the final goal.
Artificial Intelligence applications, General Management scenarios, and phycology use cases deploy the above MEA algorithm steps selectively and recursively in achieving the end goal.
Conclusion
The means-End analysis provides a logical action plan to overcome any problems in General Management, Personal life. In AI, MEA offers a methodology to optimize the search operations to save time and effort.
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