Decision Theory and Performance Measurement Systems Lecture Notes PDF

Decision theory and development of evaluation and performance measurement systems Bavarian Business School...

Decision theory and development of evaluation and performance measurement systems Bavarian Business School Sommersemester 2024 Prof. Dr Markus Kleinschwärzer Script ET&KS Version 1.3 Structure of the lecture: Decision theory and development of evaluation and performance measurement systems Decision theory (Prof. Markus Kleinschwärzer) Key figures and key figure systems (Prof. Markus Kleinschwärzer) ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 2 Decision Theory Overview 0 Introduction 1 Problems and solution concepts of decision theory: an overview 2 Value and target systems 3 Concepts of decision theory 4 Forecast models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 3 Decision Theory Overview 1 Problems and solution concepts of decision theory: an overview 1.1 Planning and decision-making in the system of business administration 1.2 Basic terms 1.2.1 Planning and decision 1.2.2 Phases of planning 1.2.3 Planning range 1.3 Models and modelling 1.3.1 Advantages of model-based planning and decision-making 1.3.2 Model concept 1.3.3 Model typology 1.3.4 Model-based planning 1.3.5 Methods at a glance ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 4 Decision Theory Overview 2 Value and target systems 2.1 Development of a value system 2.2 Development of a target system ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 5 Decision Theory Overview 3 Concepts of decision theory 3.1 Decision problem 3.1.1 Basic model of decision theory 3.1.2 Individual core process 3.1.3 Core social process 3.2 One-dimensional target systems 3.2.1 Decision in case of safety and one target 3.2.2 Decision at risk and one target 3.2.3 Decision in the case of uncertainty and a goal 3.3 Multidimensional target systems 3.3.1 Resolving conflicts of objectives 3.3.2 Decision in groups 3.4 Utility theory 3.4.1 Basics 3.4.2 Selected procedures 3.4.2.1 Outranking methods 3.4.2.2 Direct Rating 3.4.2.3 Halving method 3.4.2.4 Method of equal value differences 3.4.2.5 AHP 3.4.3 Bernoulli's principle ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 6 Decision Theory Overview 4 Forecast models 4.1 Statistical forecasting models 4.1.1 Moving averages 4.1.2 Exponential smoothing 4.1.3 Econometric models 4.1.4 Neural networks 4.2 Forecasting models 4.2.1 Network planning technique 4.2.2 Markov models 4.2.3 System Dynamics 4.2.4 Simulation 4.3 Expert forecasts ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 7 Information about the event 0 Introduction Information about the event ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 8 Information about the event Rules of the game - all students: Teaching methods: SWS Presence >= 30 Performance evaluation Written exam 100% (90 min) What the lecture builds on - Basic literature Planning and decision-making in work and everyday life Published by Steffen Fleßa Fleßa , Steffen 1st edition 2010, 190 p., softcover Oldenburg ISBN 978-3-486-59764-6 ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 10 What the lecture builds on - Basic literature ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 11 Chapter 1 - Basics 1 Problems and solution concepts of decision theory: an overview ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 12 Decision Theory & Indicator Systems Overview 1 Problems and solution concepts of decision theory: an overview 1.1 Planning and decision-making in the system of business administration 1.2 Basic terms 1.2.1 Planning and decision 1.2.2 Phases of planning 1.2.3 Planning range 1.3 Models and modelling 1.3.1 Advantages of model-based planning and decision-making 1.3.2 Model concept 1.3.3 Model typology 1.3.4 Model-based planning 1.3.5 Methods at a glance ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 13 1.1 Planning and decision-making in the system of business administration Ø Business administration: science of the economic activities of companies Ø Object of experience: company as a problem-solving unit Ø Object of knowledge: efficiency, economic action ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 14 Knowledge goals Ø Descriptive business administration Description and explanation of the economic activities of enterprises; no design Ø Decision-oriented (= practical-normative) business administration Derivation of recommendations for action for companies on the basis of given objectives Ø Ethical-normative (confessional-normative) BWL Derivation of recommendations for action and discussion of the target systems ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 15 BWL: Status Ø Practical-normative: focus of business studies; business studies as a science of action: decision-making and action are in the foreground Ø Decision theory as the "core" of business administration - Without a decision, no BWL is possible ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 16 Schools of thought in business administration (1) Ø Factor-theoretical approach (Erich Gutenberg) - Production as recombination of production factors - Main line of German-language business studies - Clear focus on customers, on production bottlenecks and on value creation Ø Decision-theoretical approach (Edmund Heinen) - Focus on operational decision-making processes - Target orientation: combination of Nicklisch and Gutenberg - Mathematical school ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 17 Schools of thought in business administration (2) Ø Systems theory approach (Hans Ulrich) - Orientation to the system and system control - Widespread use in the social sector - Problem: Often overemphasis on personnel management, neglect of production or problem solving for the environment Ø Other approaches: - Work-oriented approach - Behavioural approach ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 18 Decision theory Ø Content: Systematic knowledge of the decision, i.e. the selection of an alternative course of action or a set of alternative courses of action from a set of alternative strategies in conformity with the target system. Ø Schools: - Prescriptive decision theory (normative): Development of guidelines for the rational selection of alternative courses of action. o practical-normative: goals given o confessional-normative: goals debatable - Descriptive decision theory (empirical realist): Description and explanation of people's decision-making behaviour ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 19 Control loop model DISTURBANCE SIZE STILL RULE- RULE SIZE STRUCTU SIZE RE CONTROLL LEADERSH ER IP SIZE ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 20 Steering, management and leadership Fulfilment of function, meaning, Analysis and design of one's own system of values and goals Vision Mission Needs Influence Planning Analysis and design Analysis and design of other subsystems Organisa- the markets Control tion Manage- ment Personnel Personnel manageme deployme Metaphysical level nt Sense nt Sustainability Function fulfilment Analysis and design of feedback systems ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 21 Functional view of management Organi- Plannin sation g MANAGER Personne Control l deploy ment Personne l manage ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 22 ment Ideal-typical management cycle Feedback PLANNING Organisation Staff deployment IMPLEMENTATION Human Resources Management CONTROL ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 23 Management concepts Dominant Control type Environmental features Management function Scheduled Low complexity Corporate management and dynamics Primacy of planning Control-determined corporate Increasing complexity and management dynamics Planning and control Organisation-oriented High complexity and Organisation Corporate management Dynamics Corporate management as Extreme complexity and Human Resources Management coaching dynamics ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 24 Corporate management, performance and financial management Planning Implementation Control PURCHASE PRODUCTION SECTION TILGING INVESTMENT FINANCING ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 25 Dynaxity zones Dynamic s turbulent Zone IV dynamic Zone III Zone II Zone I static Few elements, few Many elements, Many elements, Complexity relations few relations many relations ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 26 Dynaxity and system regime Chaotic system Zone I System Comple- xity Zone III system Zone II system Tim ET& KS / WiSe 2324 / Markus Kleinschwärzer e © 2023 - Hochschule der Bayerischen Wirtschaft 27 Side effects, repercussions and consequences ACTION Side effect, D t=0 Primary effect: B intended, D t=0 Consequential A Retroactivity, effect, D t>0 D t>0 C Retroactivity, T>0 ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 28 1.2 Basic terms Example: Until now, a youth centre had done its own laundry and employed a part-time worker for this purpose. As the youth centre has to reckon with cuts in state subsidies, a committee is formed to work out savings proposals. After some deliberations, the committee presents the following alternatives for decision: Ø Alternative A: everything remains as it is Ø Alternative B: an outside company washes Ø Alternative C: the leader's wife takes over the laundry on a voluntary basis ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 29 Example Ø Alternative A is preferred by the staff because it provides a job for the half-time worker. On the other hand, the annual costs are 22,500 euros and the quality of the washing service is relatively poor. Ø Alternative B costs only 17,500 euros and one expects a professional performance. Ø Alternative C is the cheapest with 7,500 euros pure material costs, but there is considerable resentment from the staff here because the volunteer work is seen as competition to paid work. In addition, quality and reliability have been questioned. Which alternative should one choose? ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 30 Step 1: verbal representation Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs Staff satisfaction Cleanliness and reliability ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 31 Step 1: verbal representation Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs bad medium good Staff satisfaction Cleanliness and reliability ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 32 Step 1: verbal representation Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs bad medium good Staff satisfaction good medium bad Cleanliness and reliability ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 33 Step 1: verbal representation Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs bad medium good Staff satisfaction good medium bad Cleanliness and bad good medium reliability ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 34 Step 2: Result matrix Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs 3 Staff satisfaction bad = 3 medium = 2 good = 1 Cleanliness and reliability ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 35 Step 2: Result matrix Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs 3 2 1 Staff satisfaction 1 2 3 Cleanliness and 3 1 2 reliability ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 36 Step 3: Benefit fusion Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs 3 2 1 Staff satisfaction 1 2 3 Cleanliness and reliability 3 1 2 Sum: 7 5 6 ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 37 Step 4: Weighting Alterna- Alterna- Alterna- Criterion tive A tive B tive C Costs 3 2 1 Staff satisfaction x5 5 10 15 Cleanliness and reliability 3 1 2 Sum: 11 13 18 ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 38 Abstraction: Elements of a decision Ø A concrete problem must be known and named. Ø Alternatives must be developed Ø Goals must be defined Ø Target weights must be introduced Ø Target achievement levels for alternatives must be determined Ø The best alternative or the best Ø Bundle of alternatives must be identified Ø Uncertainties may need to be considered ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 39  Group work Example: A mechanical engineering company had previously manufactured all components for the construction of the machine itself, by its own employees. Due to the competitive situation in the markets served, alternatives are to be developed to optimise costs. The division managers develop the following alternatives: Ø Alternative A: an outside company produces the components Ø Alternative B: everything remains as it is Ø Alternative C: A charitable institution produces the components free of charge Task: Ø Alternative A costs only 27,500 euros and one expects a professional performance. Ø Alternative B is preferred by the employees because it provides a job for their own employees. On the other hand, the annual costs are 35,500 euros and the quality produced is relatively poor. Ø Alternative C is the cheapest with 10,500 euros in pure material costs, but there is considerable resentment from the staff here because the volunteer work is seen as competition to paid work. In addition, quality and reliability have been questioned. Time: 40 min ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 40 Planning as a phase of management Feedback PLANNIN G Organisation Staff deployment IMPLEMENTATION Management CONTROL ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 41 Planning as starting point / end point / focus? Planning Organisation Control Manager Staff Manage deployment ment ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 42 Setting priorities Ø Plan-determined corporate governance o Plan is the starting point of all operational action in a static conversion system o Control is feedback control and serves to verify plan fulfilment o Origin: Military o Dissemination: administrations; annual budgets Ø Control-determined corporate governance o Plan is (often) at the disposition of feedforward control Ø Organisationally determined corporate governance o Organisation reacts flexibly to requirements o Plans are varied according to new requirements Ø Corporate management as coaching o Spontaneous working groups are led via personal influence ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 43 Planning as a cross-sectional task Planning Implementation Control PURCHASE PRODUCTION SECTION ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 44 Planning task Ø Task of planning: development of measures to achieve a desired state. Ø Trigger: usually deviation of desired and actual variables Ø Precondition: Deviation is perceived as a problem ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 45 Decision problem Ø Initial situation: Circumstances that cannot be influenced by the planner (data). Ø Alternatives for action: Design options for achieving the objective (variables) Ø Interdependencies: Relation of data and variables Ø Objective: Goals to be achieved by the alternative courses of action. Ø Action results: Degree of achievement of objectives for different alternatives ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 46 Features of the planning Ø Future orientation Ø Design orientation: o Presupposes the selection of alternatives. Mere "foresight" of events that cannot be changed is not planning! Ø Subjective process: o Objective and assessment dependent on personal preferences Ø Information process: o requires collection of information Ø Systematic process: Planning as a rational process Ø Preparation of decisions and actions ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 47 Definition Ø "Planning is a fundamentally systematic and rational process carried out by planners on the basis of imperfect information in order to solve decision-making problems while taking subjective goals into account" (Domschke). Ø "Planning is prospective thinking in the form of mental anticipation of future action with the aim of achieving strategic competitive advantages" (Steinmann). ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 48 Systematics and intuition Ø Systematics: planning process, data collection, etc. Ø Intuition: generating alternatives, overcoming uncertainty Systematics and intuition are not contradictory! ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 49 Phases of planning Ideal typical process Problem Determination of alternatives Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 50 Phases of planning Determination and definition of decision problems on the basis of excitation information Problem Determination of alternatives Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 51 Phases of planning Types of excitation information: -Target-actual deviations -changes in the amount of Alternative courses of action Problem -Change of data -Changes in objectives -Consequential problems (e.g. Determination of investment decision leads to plant alternatives utilisation decision) Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 52 Phases of planning Subphases: -Problem awareness: recording the symptoms of the problem, urgency, enforceability Problem -Problem analysis: understanding the causes of problems, basic interrelationships of effects Determination of -Problem formulation: precise alternatives description of the desired state and the restrictions; definition of objectives Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 53 Phases of planning Problem Search for suitable measures to solve the problem Determination of alternatives Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 54 Phases of planning Subphases: -Alternative search: action alternatives generated by intuition and systematics Problem -Alternative analysis: Examination of effectiveness and enforceability; certainty of effect in the face of uncertainty; Determination of effects on other plans alternatives -Determination of alternatives: Alternatives identified as adequate to solve the problem Evaluation and selection are specified in terms of details, resources and responsibilities. ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 55 Phases of planning Problem Determination of alternatives Final evaluation and decision Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 56 Phases of planning Subphases: -determination of benefits: As a rule, the different alternatives Problem have to be evaluated subjectively, i.e. results have to be transferred into subjective utility variables. -Resolving conflicts of Determination of objectives: Weighting of goals alternatives -decision: Determination of the alternative (or the best bundle of Evaluation and selection alternatives) that best corresponds to the target system. ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 57 Planning and forecasting Forecast of Problem the development of the conversion PROGNOSI system Determination of alternatives Forecast of the impact correlations S Evaluation and selection ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 58 Phases of planning Group work Develop a planning phase model for an investment decision for a CNC machine in a mechanical engineering company using the "ideal typical process" of planning. Aspects - Alternative: repair, leasing, purchase - Criteria: Benefits, costs, feasibility (time) - Consider all phases and define aspects (if not given) in the group - Important: Argue your approach Ø Visualise and present your result! Time: 45 min ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 59 Alternative filter Ø Quantity of resource-conforming alternatives Ø Quantity of environmentally compliant alternatives Ø Set of alternatives that conform to the target system ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 60 Planning range Planning dilemma - Short-term planning: - Low uncertainty, high level of detail - low resource commitment - Long-term planning: - Large amount of alternative actions ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 61 Alternative courses of action Field, alternatives Time of planning / time of action ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 62 Planning dilemma - Problem: long-term planning is necessary to exploit all alternative courses of action. However, it is burdened by such high uncertainty that detailed planning is not possible. - Solution: Different planning ranges - Long-term planning (> 2 years) - Medium term planning - Short-term planning (< 6 months) ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 63 Strategic, tactical and operational planning - Strategic planning: Defining the corporate strategy at the highest level. Consequence: Usually long-term - Tactical planning: In corporate medium construction, usually medium-term - Operational planning: At the implementing base, usually in the short term NB: Planning level and planning period are not identical! ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 64 Security and insecurity - Security situation: - the situation that will occur is known - deterministic decision model - Uncertainty situation - the situation that occurs is not fully known, as - certain environmental conditions are not known - certain environmental conditions occur with probabilities ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 65 Types of uncertainty - risk situation - Probabilities of occurrence for environmental states are known - Stochastic decision model - Situation of uncertainty - Environmental conditions are known - Probabilities of occurrence are not known - Game situation - Uncertainty results from a rationally acting adversary, e.g. competition ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 66 Uncertainty in Anglophone Literature - Risk: - Objective probabilities known - Uncertainty: - Subjective probabilities known - Ambiguity: - Ordinal probabilities known e.g. WS(s1) > WS(s2) - Upper and lower limits for probabilities are Known - Complete Ignorance - No probabilities known ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 67 1.3 Models and modelling - Principle: A model is the representation of reality through another medium - Example: - Stone house is depicted by paper model - Human through animal model - Animal model through simulation programme - Landscape through map ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 68 Example: Tanaland - Source: Dörner, The Logic of Failure - Content: Choosing the optimal strategy for the development of Tanaland in East Africa. - Starting point: - nomadic population - Health care - Livestock - Natural sources - Grass stock - Rodent and prey stocking ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 69 Strategies - Settlement and agriculture - Well drilling - Human medical care - Veterinary care - Hunting rodents ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 70 A typical course of play - Well drilling programme Ø Livestock population grows, diseases decline Ø Population grows, natural sources dry up Ø After ten years, the land is overgrazed and the population has grown so much that it can no longer be fed Ø Famine! Ø After 15 years, the situation is worse than in the initial situation ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 71 A typical course of play - Agriculture Ø Better nutrition, rodents become a problem, soils are depleted. Ø Population grows, rodents are strongly controlled Ø Insects are increasing strongly. Money for insecticides and fertilisation is not available Ø After ten years, agricultural yields have fallen so much and the population has grown so much that it can no longer be fed Ø Famine! Ø After 15 years, the situation is worse than in the initial situation ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 72 Surprise! - Even experts are not able to balance the system, although - the system is not very complex - the system consists of clearly comprehensible interrelationships - the system is to be controlled for only 15 years - Experts also tend to do this, - To intervene too much in the first few years - Override in case of negative developments - Dynamic developments not recognisable ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 73 Control loop model POPULATION AGRICULTURE RINDER WATER GRAS PIRATES NAGETIERE INSECT ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 74 Control loop model HUMAN MEDICINE POPULATION AGRICULTURE VETERMEDICIN E RINDER WATER GRAS DRILLIN PIRATES G WELLS NAGETIERE INSECT HUNTING RODENTS ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 75 Problems of a decision-making situation according to Dörner - Complexity - Numerous elements - Interconnectedness: interdependencies, systems thinking - No decomposition possible - Dynamics - Changes in time - No linearity - Incompleteness of the information - Uncertainty - False hypotheses about cause-effect relationships ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 76 Individual criteria - Time pressure - Decisions are made sub-optimally due to objective or subjective time pressure - Intransparency of the situation - Inability to think in systems - "Stubbornn - ess" No willingness to turn away from false hypotheses - "Oversteer" - Too strong countermeasures for undesirable results ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 77 Advantages of model-based planning - Model considered - large number of elements - Interdependencies - Dynamics - Uncertainty - Model is - faster - cheaper - harmless - Increase models - Transparency ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 78 Intuition and creativity - "Flash of inspiration" - As a rule, the result of intensive, systematic study of the topic - usually outside the workplace and intensive employment - "Gut decisions" by management are usually based on decades of experience with model-based methods! ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 79 Model term - Depiction of reality in another medium - Example: Map - Step 1: Photographic image - Step 2: Neglecting details, e.g. trees and houses - Step 3: Adding details that do not exist in reality, e.g. contour lines - Step 4: Exaggeration of details, e.g. thickness of the footpath ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 80 Destination guidance - Fundamental question of modelling: - which details can be neglected? - what details should be added? - which details need to be overdrawn? - Answer: The goal of the user decides on this - E.g. hiking map versus topographic map ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 81 Summary - Model ≠ Reduction of reality - Abstraction: certain details disappear - Addition: certain details are added - Amplification: certain details are exaggerated - The model purpose determines the model type and procedure ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 82 Sequence for model - A model is never the "truth", but always a partial tool developed for a very specific purpose - Two models representing the same reality can / must be completely different if they are to serve different purposes ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 83 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Information Deterministic and stochastic models security Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 84 1.3.3 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models Description model: presentation Representation of the elements and their relationship in real Information systems Deterministic and stochastic models security Ø No hypotheses about cause-effect relationships Ø No explanation Time reference Static and dynamic models Ø No forecasts Scope Example: FinancialTotalaccounting and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 85 1.3.3 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Explanatory model (=causal model): Modelling and explanation of cause-effect relationships Information Deterministic and stochastic models between exogenous (independent) and endogenous security (dependent) variables very simplistic e.g. production function Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 86 1.3.3 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Forecast model: Model for predicting future environmental developments and for Information Deterministic and stochastic models estimating the effects of alternative courses of action (impact security prediction) Time reference e.g. estimation Static of sales and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 87 1.3.3 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models Simulation model: presentation Model for "playing through" alternatives; usually designed as a Information complex forecastingDeterministic model and stochastic models security What-If? How-to-achieve? Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 88 1.3.3 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Information Deterministic Decision and optimisation models: and stochastic models security Models for selecting the best possible alternative courses of action reference Time Decision model (=choice model): Static and Explicit dynamic specification of the set of alternative models actions; set is finite and limited Optimisation model:Total Scope Implicit specification and partial models of the set of alternative actions through restrictions or constraints; the set is limited, but can be infinite. ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 89 1.3.3 Model typology Feature Model types Intended use Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Information Deterministic and stochastic models Qualitative and quantitative models:: security Quantitative (mathematical) models: all aspects depicted in the model are described by cardinal Time reference Static scales and dynamic models Qualitative models: at least one aspect is described by a nominal or ordinal Scope scale Total and partial models Goal: Quantification of qualitative models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 90 Excursus: Scales (1) - Nominal scale: - Differentiation of the expression is possible - Ranking is not possible - z. e.g. red, green, black - Ordinal scale: - Ranking of the expressions is possible - Differences between expressions are without significance - z. E.g. school grades - Cardinal scale: - Differences between expressions provide information about the gradation - z. E.g. temperature ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 91 Excursus: Scales (2) - Interval scale - Phenomenon does not disappear in the zero point - z. e.g. degrees Celsius - Ratio scale - Phenomenon disappears in the zero point - z. e.g. degrees Kelvin - z. E.g. profit ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 92 1.3.3 Model typology Physical models: Reduction of a reality and usually representation by another physical reality, e.g. ventilation in a mine by means of electric circuits. Formal models: representation by special symbols, e.g. musical notes, variables and functions Feature Model types Graphical models: visualisation of real phenomena. usually only 2- Intended use 3 dimensions Descriptive, possible. explanatory, causal, prognostic, simulation, decision-making, Verbal models:optimisation model Textual description, e.g. lecture Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Information Deterministic and stochastic models security Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 93 1.3.3 Model typology Deterministic models: abstraction of data uncertainty so that uncertainty is completely excluded Feature Ignoring uncertainty Model types Short planning horizon Intended use Descriptive, explanatory, causal, prognostic, simulation, Rolling planning decision-making, optimisation model Alternative plans or scenario technique Stochastic Measurement level models: Explicit Qualitative andrepresentation of uncertainty in the quantitative models model, e.g. by random variable Form of Physical, formal, graphical, verbal models presentation Information Deterministic and stochastic models security Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 94 1.3.3 Model typology Static models: abstraction from the time course Dynamic models: Consideration of temporal processes in the model, especially multi-period model Feature Discreet timing Model types Intended use Continuous time management Descriptive, explanatory, causal, prognostic, simulation, decision-making, optimisation model Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Information Deterministic and stochastic models security Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 95 1.3.3 Model typology Feature Model types Intended usemodel: the entirety Total Descriptive, explanatory, of a system causal, prognostic, simulation, is represented decision-making, Partial model: deliberate restrictionoptimisation to a specificmodel section of the system or to a smaller temporal range Measurement level Qualitative and quantitative models Form of Physical, formal, graphical, verbal models presentation Information Deterministic and stochastic models security Time reference Static and dynamic models Scope Total and partial models ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 96 Structural properties and defects - Principle: From the first stimulation information to the finished problem solution, there are numerous steps to take and difficulties to overcome. - Representation: Linear sequence of steps - Reality: Sequence as loops and repetitions of steps ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 97  Decision problem ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 98  Decision problem Are the type No and number of variables known? Y es ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 99  Decision problem Are the type and number of No variables known? Demarcation defect Y es Delimitation defined ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 100  Decision problem Are the type and number of No variables known? Demarcation defect Y es Delimitation defined Are data No Impact defect and impact correlation s Y known es Impact defined ? ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 101  Decision problem Are the type and number of No variables known? Demarcation defect Y es Delimitation defined Are data and impact No correlations Impact defect known? Y es Impact defined Is the No Valuation defect evaluation of the hand- Y ageing natives es Evaluation defined possibl e? ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 102  Decision problem Are the type and number of No variables known? Demarcation defect Y es Delimitation defined Are data and impact No correlations Impact defect known? Y es Impact defined Is it possible to evaluate the No alternative Valuation defect courses of action? Y es Evaluation defined If there is a No Targeting defect clear and operational target, the Y func- es Target defined tion? ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 103  Decision problem Are the type and number of No variables known? Demarcation defect Y es Delimitation defined Are data and impact No correlations Impact defect known? Y es Impact defined Is it possible to evaluate the No alternative Valuation defect courses of action? Y es Evaluation defined Does a clear and No operational Targeting defect objective function exist? Y es Target defined Is there an efficient No resolution Solution defect process? Y es Efficiently solvable ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 104  Decision problem Structural defects Are the type and number of No variables known? Demarcation defect Y es Delimitation defined Are data and impact No correlations Impact defect known? Y es Impact defined Is it possible to evaluate the No alternative Valuation defect courses of action? Y es Evaluation defined Does a clear and No Targeting defect Well-structured operational objective function exist? Y es Target defined Is there an efficient No resolution Solution defect process? Y es Efficiency solvable ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 105 Production programme planning - 13.04.2023 Structure planning Group work You are a manufacturer of industrial electronics and are asked to make a decision about in-house or external production for a new product. Task: Ø Discuss the production programme planning, according to structural characteristics, for the new product to be produced. Address each of the above steps and determine the appropriate aspects for each stage. Ø What do you do in case of delimitation, impact, evaluation, goal setting and solution defect? Ø What were the challenge / problems in the elaboration Ø Visualise and present your result! Time: 45 min ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 106 Structuring process - Principle: Develop appropriate models for each step - Problem: Different model types are difficult to convert into each other - Reality: loops, feedbacks, feedforwards ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 107  Perceived real decision problem Generation and pre-selection of alternative courses of action Delimitation- defined problem Data forecasting and determination of the impact correlations Effect defined problem Evaluation through criteria selection and benefit determination Assessment defined problem Resolving conflicting goals, operationalisation Target defined problem Optimisation / Selection Solution / Plan ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 108  Perceived real verbal model decision problem Generation and pre-selection of alternative courses of action Delimitation- Variables of the model defined problem Data pr ognosis and Erm itting the we k- together cesses Effect defined Explanation, forecast, problem simulation model Evaluation g by criteriaa uselect and N ion investi utzen- gated lung Assessment defined Multicriteria problem optimisation model Resolut of ion conflicts, Target alisation Conf Operation Target defined problem Single-objective optimisation model Optimisation / Selection Solution / Plan ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 109  Perceived real verbal model decision problem Generation and Feedback pre-selection of alternative courses of action Delimitation- Variables of the model defined problem Data pr ognosis Feedback and Erm itting the we k- together cesses Effect defined Explanation, forecast, problem simulation model Evaluation g by criteriaa uselect Feedback and N ion investi utzen- gated lung Assessment defined Multicriteria problem optimisation model Resolving Feedback conflicting goals, operationalisation Target defined Single-objective problem optimisation model Feedback Optimisation / Selection Solution / Plan ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 110  Perceived real verbal model decision problem Generation and Feedback pre-selection of Action alternatives Delimitation- Variables of the model defined problem Feedforward Data Feedback forecasting and determination of the impact correlations Effect defined Explanation, forecast, problem simulation model Feedforward Evaluation through criteria Feedback selection and benefit determination Assessment defined Multicriteria problem optimisation model Feedforward Resolving Feedback conflicting goals, operationalisation Target defined Single-objective problem optimisation model Feedforward Feedback Optimisation / Selection Solution / Plan ET& KS / SoSe 2021 / Markus Kleinschwärzer © 2021 - Hochschule der Bayerischen Wirtschaft 111 Methods at a glance - management techniques - Planning methods = planning procedures - Decision-making methods = decision-making procedures - Management Science - should also include Organisational Behaviour - As a rule, however, primarily operations research ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 112 Overview of methods - Analysis techniques - Obtaining excitation information - System and problem analysis - e.g. indicator systems, SWOT analysis, portfolio analysis, Sales analysis, benchmarking - Creativity techniques - Techniques for generating alternative courses of action - especially in complex and novel Problem situations - e.g. brainstorming, brainwriting, morphological box, synectics ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 113 Overview of methods - Analysis techniques - Obtaining excitation information - System Very common and problemtechnique analysis - z. e.g.Group indicator systems, of 5-8 SWOT participants, analysis, portfolio interdisciplinary "freeanalysis, thinking".formation of association chains;Phase 20 - 401:min. Collection of many, intuitive ideas without Turnover analysis, discussion; benchmarking formation of chains of associations; 20-40 min. Phase 2: Sifting through and evaluating the ideas - Creativity techniques (screening) - Techniques for generating alternative courses of action - especially in complex and novel Problem situations - z. e.g. brainstorming, brainwriting, morphological box, synectics ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 114 Overview of methods Written form of brainstorming e.g. Method 635: Six group members have to write down three proposed solutions on a form within five minutes. - Analysis techniquesThen they pass it on to their neighbour, who in turn has five minutes to (further) develop three ideas. Either own ideas, - Obtaining excitation information or "spinning on" the ideas of the predecessor. - System and problem analysis End: When everyone has had each form( 6*5=30 minutes); - z. e.g. indicator systems, This isSWOT analysis, followed portfolio analysis, by an evaluation phase like brainstorming. Turnover analysis, benchmarking - Creativity techniques - Techniques for generating alternative courses of action - especially in complex and novel Problem situations - z. e.g. brainstorming, brainwriting, morphological box, synectics ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 115 Overview of methods Structuring and systematisation of complex problems; problem and possible solutions can be described by - Analysis techniques different decision variables and their characteristics. They - Obtainingareexcitation information stored in a table. Solution variants result as a line - System and problem analysis through the table. - z. e.g. indicator systems, SWOT analysis, portfolio analysis, Turnover analysis, benchmarking - Creativity techniques - Techniques for generating alternative courses of action - especially in complex and novel Problem situations - z. e.g. brainstorming, brainwriting, morphological box, synectics ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 116  Structuring and systematising complex problems; Overview of methods Problem and possible solutions can be identified through Overview different decision variables and their describe the characteristics. They are stored in a table. Solution variants result as a line through the - Analysistabletechniques - Ge wing of an em regioNal mations - Syst and probl infor mation - z. B. Kennzahlensy Illuminated Large-area stems, SWOT- Analysis, Spotlighting Multipoint olio analysis, field At rate analysis, lighting nchmarking Attachment lighting Portf - Creatibe Glare-free vityFrosted glass enfilter Spreading - Tech pane eration of H agesieve of native technology Telescopic Scissor joint Flexible Hanging on the Adjustable. - esp especial nics for in co mplexes and n development gene eu-like arm plastic arm rope Probmmercial n Circuit- z. E.g.. situations Pressure Brainstormin Pull switch g, brainwriting, Acoustic morphologisc hheMotion box, switch Synketics signals detector Umbrella Roun Pyramid-shaped Elongated No d umbrella Material Plastic Lacquered metal Chrome Combination Illuminant Light Halogen light Fluorescent tube Energy ET& KS / WiSe 2324 / Markus Kleinschwärzer © 2023 - Hochschule der Bayerischen Wirtschaft 117 bulb saving lamp  GANG through the variable fields, Overview of methods e.g. - Analysis techniques - Ge wing m of an em regioNal ations - Syst and probl infor mation - z. B. Kennzahlensy Illuminate Large-area stems, SWOT- Analysis, Spotlighting Multipoint olio analysis, d field At rate analysis, lighting nchmarking