A Simple Model for Management

 And why do we continue getting management wrong

Wikipedia defines management as a ”process of managing the resources of businesses, governments, and other organisations.” [Wikipedia] But management can also be understood as "the transformation of resources into utility" [Fredmund Malik] or as "to forecast and to plan, to organise, to command, to co-ordinate and to control" [Henri Fayol] or as ”the coordination and administration of tasks to achieve a goal.” Quite generic and somewhat controversial definitions. The article aims to describe management through a single model that includes most aspects of resources, transformation, tasks, utilities, and goals.

Creating a Simple Model

The simple model merges the above into one system view, as illustrated in Figure 1, composed of:

• Inputs are the resources or efforts, like personnel, time, money, material, information, and immaterial components.
• Actions are tasks that the organisation is doing to produce outputs.
• Outputs are the products, services, artefacts, information, or performances that the organisation produces, delivers, or creates using inputs and actions.
• Outcomes are the impacts, effects, or value that the outputs have on targeted systems of systems like businesses, consumer market, audience, valuation, or the theatre for conflict.
• Feedback is the information management collects from outcomes, outputs, actions and inputs in an attempt to steer the process through foreseeing, planning, coordinating, and appraising. This may be called data-driven decision-making  or a learning organisation.  

Figure 1: A Simple Model for Management

Using the Simple Model to Analyse Management Mistakes

The simple Management Model helps analyse, at a high level, the existing management operation models and guides possible remediation in Table 1.

Table 1: Samples for analysing and remediation of current management using  the Simple Model for Management

Component	Typical Misconduct	Possible Remediation
INPUTS	Management focuses on efforts and resources, such as hours/money/ material consumed, or manning /machines/contracts activated. This may lead to management by cost centres.	Follow resource consumption relative to outputs, and focus on core resources, such as competencies[1] (Pareto: 20% of inputs deliver 80% of outputs[2]).
ACTIONS	Management focuses on transactions, utilisation of service points, and performance of subsections. This may lead to suboptimisation and a stovepiped operation model.	Measure throughput and quality of the entire value stream rather than subcomponents, and then focus on eliminating ”muda” or wastefulness in LEAN process thinking.[3]
OUTPUTS	Management focuses mainly on manufactured products, delivered utilities, or what can be quantified, while neglecting everything else[4]. This may lead to burning resources or missing the strategic purpose.	Outputs are not linear causes of outcomes, so a Balanced Scorecard-type approach needs to identify the relationships among inputs, actions, outputs, and outcomes.
OUTCOMES	Management focuses on ends or goals but measures them using key performance indicators without an overarching context, such as benchmarking, market share, share valuation, and new user acquisition. This may lead to Machiavelli’s view[5] that the ends justify any means, like in Russian operational thinking.[6]	Effects or impacts are essential to identify and measure to keep the system/business on track towards its objectives.  Management may use a results-based approach to make progress toward the destination statement (Ends).[7] Military planning or design uses Lines of Operation and Courses of Action to deliver impact on Centres of Gravity.[8]
FEEDBACK	Management may focus on collecting feedback or data from a few components and on either lagging or leading indicators. This may lead to a stagnant organisation/system that lacks agility or flexibility, which will be replaced by competitors or destroyed by adversaries.	Management must create a balanced mix of leading and lagging indicators to improve reaction and prediction.[9] Leading Indicators are early signals that predict future trends (e.g., employee engagement predicting retention rates). Lagging Indicators are confirming past performance but don’t allow for proactive adjustments (e.g., revenue after a quarter ends).

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[1] https://en.wikipedia.org/wiki/Core_competency

[2] https://en.wikipedia.org/wiki/Pareto_principle

[3] https://en.wikipedia.org/wiki/Muda

[4] https://en.wikipedia.org/wiki/McNamara_fallacy

[5] https://simple.wikipedia.org/wiki/The_end_justifies_the_means

[6] https://www.wilsoncenter.org/blog-post/ends-justifies-means-logic-led-russia-war-and-repression

[7] https://en.wikipedia.org/wiki/Balanced_scorecard

[8] https://www.coemed.org/files/stanags/01_AJP/AJP-5_EDA_V2_E_2526.pdf

[9] https://metricsexplained.com/resource/balancing-leading-and-lagging-indicators-a-strategic-approach

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Improving the Model

When the model in Figure 1 is compared against a sample of other management models, one may find that:

• The model aligns with Boyd’s OODA loop in decision-making while including several feedback loops to learn and adjust the system. Nevertheless, it does not illustrate the cognitive processes of sense-making and decision-making.  
• The model facilitates Deming’s PDCA circle for iterative improvement, but does not illustrate the management functions of planning and checking.
• The model aligns with parts of the ISO37000 governance framework by reflecting the organisational purpose, oversight, strategy, and interfaces with beneficiaries and sources. 
• Porter’s five forces may be applied to the model in a specific environment or market. 
• One can also understand Hamel’s and Prahalad’s value chain for core competencies (inputs), products (outputs), and business (outcomes), but the model does not explain the utilisation of core competencies, particularly.
• The model misses some of the stakeholders and the strategic dynamics of Kaplan’s & Norton’s 3rd generation Balanced Scorecard. 
• The model lacks the functionally layered structure of Beer’s Viable System Model , but it may fulfil the viability definition by responding to environmental changes and achieving its purpose.
• The model fails to support Hamel’s Humanocracy viewpoint because the action component lacks the details of structure, bureaucracy, and human relationships. Therefore, it fails to illustrate the evolutionary tension between hierarchies and networks of competency. 
• Furthermore, the model in Figure 1 does not describe the environment or the theatre/market where the outcome takes place, as defined in EFQM  and studied in Introduction to Environmental Systems and Processes. 

While trying not to increase the complexity of the model, but address some of its essential gaps, the improvement presented in Figure 2 adds:

1. relationship between the system and its environment for adapting to environmental changes, 
2. sources for the inputs residing outside of the system for supply chain management,
3. relationships between actioning elements to recognise human relationships and lines of communication, and 
4. elaboration of the target systems (e.g., BtoB, BtoC, audience, theatre of war) of outcomes with the Cynefin Framework of four domains based on situational awareness.

Figure 2: Somewhat improved management model

With the above improvements, the model extends towards:

• Collecting data from the system’s environment supports capturing mega and microtrends and improving foresight.
• Understanding the short and long-term chances in sources for inputs improves predictions concerning supply chain changes, educational levels, labour market fluctuations, etc.
• A simple relationship graph of the organisational structure and a connection graph of the unofficial relationships help to know yourself, as Sun Tzu teaches.
• Understanding that not all markets, societies, and theatres of war are similar when it comes to making an impact. Collecting data points from a simple target area (i.e., known) requires sensing, categorising, and sense-making, which may use best practices. A more complicated target area (i.e., knowable) requires more effort in analysis (see Orientation in OODA-loop) before decision-making. In complex and chaotic situations (e.g., digital transformation, battlefield, or unknown unknowns), the target system needs probing or action to elicit a reaction that can be detected, identified, and analysed.