Decision Making

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This section covers the application of supply and demand, demand and consumer behaviour, analysis of costs, perfectly competitive and imperfectly competitive markets, uncertainty and game theory, factor markets, unemployment, inflation, and fiscal and monetary policy.

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Demand, Supply and Elasticity • Law of demand: other things equal, quantity demanded falls as price rises (downward-sloping demand curve).

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Law of supply: quantity supplied rises with price.

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Equilibrium is where the curves intersect; a shortage pushes price up, a surplus pushes it down.

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A change in price moves along the curve; a change in income, tastes, related-goods prices, expectations, population (demand) or in input prices, technology, taxes, number of firms (supply) shifts the curve. • Price elasticity of demand Ed = (% change in quantity)/(% change in price); |E| > 1 elastic (total revenue falls when price rises), |E| < 1 inelastic (revenue rises with price), |E| = 1 unitary.

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Determinants: substitutes, necessity vs luxury, share of income, time.

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Income elasticity (normal, inferior, luxury goods) and cross elasticity (positive = substitutes, negative = complements). • Applications: pricing decisions, tax incidence (the more inelastic side bears more tax), agricultural price support, subsidies, and demand forecasting for capacity planning.

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Consumer Behaviour • Cardinal (utility) approach: law of diminishing marginal utility; consumer equilibrium by the equi-marginal principle MUx/Px = MUy/Py = MU of money. • Ordinal approach: indifference curves (convex to the origin, negatively sloped, never intersecting) and the budget line; equilibrium where the budget line is tangent to the highest indifference curve, i.e.

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MRSxy = Px/Py; income and substitution effects; consumer surplus = willingness to pay − price paid.

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Analysis of Costs Cost concept Meaning Fixed (FC) and variable cost (VC) FC does not change with output in the short run (rent, depreciation, salaries);

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VC changes (materials, power, wages) Average costs AFC = FC/Q (always falling), AVC = VC/Q, ATC = TC/Q (U-shaped) Marginal cost (MC) ΔTC/ΔQ — MC cuts AVC and ATC at their minimum points; the short-run U shape comes from the law of diminishing returns Long-run average cost Envelope of short-run curves; falls with economies of scale (technical, managerial, purchasing, financial), rises with diseconomies; minimum efficient scale Opportunity cost Value of the best alternative forgone — the basis of economic decision making Sunk cost Already incurred and unrecoverable — irrelevant to future decisions Accounting vs economic profit Accounting profit = revenue − explicit costs; economic profit also deducts implicit (opportunity) costs; normal profit = zero economic profit Break-even QBE = FC/(price − variable cost per unit); margin of safety and operating leverage (Chapter 10.2) Market Structures Feature Perfect competition Monopoly Monopolistic competition Oligopoly Number of firms Very many One Many Few Product Homogeneous Unique, no close substitute Differentiated Identical or differentiated Entry Free Blocked (barriers) Fairly free Restricted Price Price taker, P = MR Price maker, MR < P Some price control Interdependent pricing Long-run profit Normal only Supernormal possible Normal (excess capacity) Often supernormal Example Vegetable market, commodity crops Utility with a legal monopoly Restaurants, garments, retail shops Cement, telecom, airlines • Every firm maximises profit where MR = MC (with MC rising).

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Monopoly causes a deadweight loss and may practise price discrimination (first, second and third degree).

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Monopolistic competition gives variety but excess capacity.

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Oligopoly features the kinked demand curve (price rigidity), collusion and cartels, price leadership and non-price competition; it is analysed with game theory.

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Uncertainty and Game Theory • Risk = outcomes with known probabilities (use expected value, expected utility, decision trees); uncertainty = probabilities unknown (maximax, maximin, minimax regret, Laplace, Hurwicz criteria — see 7.4).

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Attitudes: risk averse, neutral, seeking; insurance, diversification and hedging reduce risk; asymmetric information gives adverse selection and moral hazard. • Game theory studies interdependent decisions: players, strategies, payoff matrix.

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Key ideas — dominant strategy, Nash equilibrium (no player gains by changing strategy alone), prisoner's dilemma (individually rational choices give a collectively poor outcome — explains cartel instability and price wars), zero-sum vs non-zero-sum, cooperative vs non-cooperative, repeated games and tit-for-tat.

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Factor Markets • Demand for a factor is derived demand; a firm hires up to the point where the marginal revenue product (MRP = marginal physical product × marginal revenue) equals the factor price (wage, rent, interest). • Labour: wage determination by supply and demand, human capital, trade unions, collective bargaining and minimum wage legislation; wage differentials; in Nepal, remittance-driven labour migration affects domestic labour supply and wages. • Land: rent is the payment for a factor in fixed supply; economic rent = payment above transfer earnings (Ricardian rent). • Capital: interest is the price of loanable funds; investment is undertaken while the marginal efficiency of capital exceeds the interest rate (the basis of NPV/IRR, Chapter 10.2).

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Entrepreneurship earns profit for risk and innovation.

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Unemployment and Inflation • Unemployment rate = unemployed ÷ labour force × 100.

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Types: frictional (between jobs), structural (skills/location mismatch), cyclical (demand deficiency in a recession), seasonal, and disguised/underemployment (very common in Nepali agriculture and the informal sector).

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Full employment allows frictional and structural unemployment (the natural rate). • Inflation = a sustained rise in the general price level; measured by the consumer price index (CPI), wholesale price index and GDP deflator.

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Types: demand-pull (too much money chasing too few goods), cost-push (rising wages, fuel or import prices — important for import-dependent Nepal), and structural/built-in.

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Effects: falling real income and savings, arbitrary redistribution from lenders to borrowers, loss of competitiveness, uncertainty; extreme cases are hyperinflation, while deflation and stagflation (inflation with stagnation) are also harmful.

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The Phillips curve suggests a short-run trade-off between inflation and unemployment.

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Fiscal and Monetary Policy Policy Instruments and effects Fiscal policy (government) Government spending and taxation; budget deficit/surplus and public borrowing; expansionary (more spending, lower taxes) to fight recession and unemployment; contractionary to fight inflation; multiplier effect; automatic stabilisers; in Nepal the annual budget and Finance Act (Ministry of Finance) set tax rates, capital expenditure and subsidies Monetary policy (central bank) In Nepal, Nepal Rastra Bank issues an annual monetary policy; instruments: policy/bank rate, repo and deposit-collection rates, cash reserve ratio (CRR), statutory liquidity ratio (SLR), open market operations, refinance and directed/priority-sector lending; objectives: price stability, financial stability, adequate credit for productive sectors and external-sector (foreign-exchange reserve) stability under the pegged exchange rate with the Indian rupee • Both policies matter to industry: interest rates and credit availability affect investment and working capital; taxes, customs duty and subsidies affect project viability; exchange-rate and inflation trends affect imported raw material costs.

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This section covers the basic principles and components of concurrent engineering, its benefits and cooperative teams, manufacturing competitiveness, products and services, processes and methodologies, performance measurement, process re-engineering approaches and enterprise models, systems thinking, complexity and integration, and sequential versus concurrent engineering.

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Concept and Principles • Concurrent (simultaneous) engineering is the systematic, integrated and parallel design of a product and all its related processes — manufacturing, assembly, testing, logistics, service and disposal — carried out by a multidisciplinary team from the start of the project. • Principles: parallel rather than serial execution of activities; early involvement of manufacturing, quality, purchasing, suppliers and customers; a single shared product data model (CAD/PDM/PLM) so everyone works from the same information; design for X (manufacture, assembly, quality, cost, maintenance, environment); decisions based on the whole life cycle; team responsibility and empowerment; continuous improvement; front-loading — spend more effort early, where changes are cheap. • Components (the 4 Ts / pillars): people and teams (cross-functional, co-located or virtual, empowered, trained, with a team leader), process (structured stage-gate development, standard procedures, concurrent tasks, design reviews), technology (CAD/CAE/CAM, simulation, rapid prototyping, PDM/PLM, groupware and networks) and organisation/management (structure, culture, incentives, supplier partnerships). • Benefits: development lead time cut typically by 30–70%, engineering change orders reduced by 50%+ (and made earlier, when they cost far less), lower product and manufacturing cost, better quality and manufacturability, fewer prototypes, higher customer satisfaction and earlier market entry (revenue gained by launching first).

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Difficulties: needs teamwork culture, good information systems, management commitment and tolerance of early ambiguity.

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Sequential ('over-the-wall') engineering Concurrent engineering Phases done one after another: design → prototype → process planning → production → service Phases overlap and run in parallel with continuous exchange of information Each department optimises its own part; problems found late Cross-functional team optimises the whole life cycle; problems found early Many late engineering changes, high cost of change Early changes, cheap; design frozen later but with fewer surprises Long time to market, higher cost, manufacturability problems Short time to market, lower cost, design that can be made and serviced easily Manufacturing Competitiveness, Products and Processes • Manufacturing competitiveness rests on cost, quality, delivery speed and reliability, flexibility, innovation, service and sustainability — supported by skilled people, technology, supply chains and infrastructure; 'world-class manufacturing' combines lean, TQM, TPM and employee involvement. • Product and service design methodologies used inside CE:

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QFD (voice of the customer → design and process characteristics), DFMA (design for manufacture and assembly — minimise part count, use standard parts, design for easy insertion), FMEA (failure modes, effects and criticality — risk priority number), Taguchi robust design (parameter design to make performance insensitive to noise; loss function), TRIZ (systematic inventive problem solving), rapid prototyping and virtual prototyping, group technology, modular and platform design, and life-cycle assessment. • Performance measurement of development: time to market, number and timing of engineering changes, first-pass yield and design-related defects, development cost vs budget, percentage of parts standardised, manufacturing cost vs target, break-even time, customer satisfaction — often gathered in a balanced scorecard (financial, customer, internal process, learning and growth).

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Process Re-engineering and Enterprise Models • Business Process Re-engineering (BPR) — Hammer and Champy: 'the fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical measures of performance such as cost, quality, service and speed'.

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Principles: organise around outcomes not tasks; let those who use the output perform the process; capture information once at its source; treat geographically dispersed resources as centralised (through IT); link parallel activities; push decision making down to where the work is done. • Approach: identify and map core processes (as-is) → set stretch targets → redesign (to-be) using IT as an enabler (ERP, workflow, e-business) → implement with change management → measure.

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Risks: high failure rate when driven only by cost cutting, resistance from employees, disruption; contrast with continuous incremental improvement (kaizen), and with lean process improvement. • Enterprise models describe an enterprise's processes, information, resources and organisation so that they can be analysed and integrated:

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CIM (computer-integrated manufacturing) architecture, reference models such as CIMOSA, GERAM and PERA, the SCOR model for supply chains, Porter's value chain, IDEF0/UML process modelling, and ERP-based enterprise architecture.

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Systems Thinking, Complexity and Integration • Systems thinking looks at the whole rather than the parts: interconnections, feedback loops (reinforcing and balancing), delays, emergent behaviour and unintended consequences; it warns against sub-optimisation — improving one department at the expense of overall performance (e.g., buying in huge lots to cut purchase price while inventory cost explodes). • System complexity: structural (number and variety of elements and interfaces — many parts, suppliers, product variants) and dynamic (behaviour changing over time, uncertainty, non-linearity); complexity is managed by modularity, standardisation, decomposition into sub-systems, clear interfaces and information systems. • System integration: linking machines, software and people into one functioning whole — vertical integration (shop floor sensors → PLC → SCADA → MES → ERP) and horizontal integration (across the value chain from supplier to customer), enabled by common data standards (STEP/ISO 10303, OPC-UA, EDI), interoperability, networks and, in Industry 4.0, IoT, digital twins and analytics.

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This section covers the concept of value engineering, value management and value analysis, the application of value engineering in product design, the phases and process of a value-engineering study, and the relationship of value engineering with quality and productivity.

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Concept of Value • Value = function ÷ cost (worth of the function delivered per unit of cost).

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Value is improved by increasing function at the same cost, keeping function at lower cost, or increasing function more than cost — never by reducing the function or quality the customer needs. • Types of value: use (functional) value, esteem (prestige) value, exchange (market) value and cost value (total cost of producing it).

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Worth is the lowest cost of providing the function reliably; the value gap is cost − worth. • Value engineering (VE) is applied at the design/development stage of a new product; value analysis (VA) applies the same method to an existing product or process; value management (VM) is the wider management framework covering both, including services and projects.

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The method was developed by Lawrence D.

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Miles at General Electric in the 1940s. • When to apply: high-cost or high-volume items (Pareto — the vital few), products with cost overruns or falling margins, redesign and cost-reduction drives, new product development, imported items being localised, and public projects (VE is mandated in many construction contracts).

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Function Analysis • Each item's function is expressed in two words — an active verb and a measurable noun ('support weight', 'transmit torque', 'prevent leakage'); functions are classified as basic (the reason the item exists) and secondary (supporting, aesthetic or caused by the chosen design). • FAST diagram (Function Analysis System Technique) arranges functions in a how-why logic: moving right answers how a function is achieved, moving left answers why it is performed; the critical path of basic functions runs through the diagram. • Costs are then allocated to functions (function-cost matrix) and compared with their worth; functions with a large cost-to-worth ratio are the targets for improvement.

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VE Job Plan (Phases) Phase Activities 1.

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Information (orientation) Select the project; collect drawings, costs, quantities, specifications and customer requirements; form the team; define the scope 2.

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Function analysis Define functions (verb-noun), classify basic/secondary, build the FAST diagram, allocate cost and estimate worth, select functions to study 3.

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Creativity (speculation) Generate alternatives without criticism — brainstorming, checklists, analogy, TRIZ, supplier and expert suggestions 4.

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Evaluation (analysis) Screen ideas for feasibility, cost, quality, reliability and risk; weighted evaluation matrices; short-list the best ideas 5.

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Development (planning) Work up the selected ideas into firm proposals — designs, cost estimates, savings, implementation plan, test data and supplier quotations 6.

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Presentation (recommendation) Present to management with costs, savings, investment required and risks; obtain approval 7.

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Implementation and follow-up (audit) Make the change (drawings, tooling, processes), verify the savings actually achieved and standardise • Common techniques: use standard and available parts, simplify the design, combine parts, eliminate unnecessary operations and finishes, change materials or process, relax non-critical tolerances, use vendors' expertise, eliminate 'nice-to-have' secondary functions.

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What is it? What does it do? What does it cost? What else will do the job? What would that alternative cost? • Application in product design:

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VE is most powerful early — roughly 70–80% of the eventual product cost is committed by design decisions, while only a small part of the cost has been spent.

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Examples: replacing a machined part with a casting or moulding, standardising fasteners, reducing part count, simplifying packaging, redesigning a fabrication to cut welding.

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VE, Quality and Productivity • VE is not mere cost cutting: cost cutting reduces cost even at the expense of function, whereas VE removes only unnecessary cost — cost that adds neither use nor esteem value, so quality and reliability are maintained or improved. • Links: better value → lower material and processing cost → higher productivity (same output with fewer resources); simplified designs are easier to make and inspect → fewer defects → better quality and lower cost of quality;

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VE overlaps with DFMA, standardisation, lean (eliminating non-value-adding activity) and TQM (customer-defined value). • Results are measured as savings per year, return on the VE study's cost (often very high), reduced part count and weight, improved delivery and quality indicators.

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This section covers mathematical modelling, regression analysis, linear programming with duality and sensitivity analysis, time-series and moving-average forecasting, game theory formulations, the simulation process, probability distributions, types of simulation and Monte Carlo sampling.

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Operations Research and Mathematical Modelling • OR applies scientific and mathematical methods to decision problems involving the allocation of scarce resources.

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Steps: define the problem and objective → construct a mathematical model → obtain a solution (analytical, algorithmic or by simulation) → test/validate the model and solution → establish controls → implement and monitor. • Types of models: iconic (scale models), analogue (graphs, flow diagrams) and symbolic/mathematical; deterministic (LP, transportation, assignment, EOQ, CPM) vs probabilistic/stochastic (queuing, PERT, Markov chains, simulation); descriptive vs normative (optimising). • Main techniques: linear and integer programming, transportation and assignment, network models (CPM/PERT — Chapter 10.3), inventory models, queuing theory, game theory, dynamic programming, Markov analysis, decision theory and simulation. • Decision making under uncertainty criteria: maximax (optimistic), maximin/Wald (pessimistic), minimax regret (Savage), Laplace (equal likelihood) and Hurwicz (coefficient of optimism); under risk use expected monetary value (EMV), expected opportunity loss, EVPI and decision trees.

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Regression and Correlation • Simple linear regression fits y = a + bx by least squares: b = [nΣxy − ΣxΣy]/[nΣx² − (Σx)²] = Σ(x − x̄)(y − ȳ)/Σ(x − x̄)², and a = ȳ − b x̄. b is the change in y per unit change in x. • Correlation coefficient r (−1 ≤ r ≤ +1) measures the strength and direction of a linear relationship; coefficient of determination R² is the proportion of variation in y explained by x.

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Correlation does not prove causation. • Multiple regression uses several independent variables; assumptions: linearity, independence, constant variance (homoscedasticity), normally distributed errors and no serious multicollinearity.

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Uses in industry: cost estimation, demand forecasting, learning curves, process parameter modelling and design of experiments.

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Linear Programming • Components: decision variables, a linear objective function (maximise profit or minimise cost), linear constraints and non-negativity restrictions.

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Assumptions: linearity (proportionality and additivity), divisibility (fractional values allowed — otherwise integer programming), certainty of all coefficients and finiteness of alternatives. • Graphical method (two variables): plot constraints, find the feasible region (a convex polygon), and evaluate the objective at the corner (extreme) points — the optimum always lies at a corner point (or along an edge if there are multiple optima).

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Simplex method for more variables, using slack, surplus and artificial variables (Big-M or two-phase). • Special cases: multiple optimal solutions (objective parallel to a constraint), unbounded solution (maximisation with an open feasible region), infeasible problem (conflicting constraints), degeneracy and redundant constraints. • Applications: product mix, blending, diet, transportation and assignment, production planning, cutting stock, media selection, capital budgeting and workforce scheduling.

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Duality and Sensitivity Analysis • Every LP (the primal) has an associated dual: a maximisation primal with m constraints and n variables gives a minimisation dual with n constraints and m variables; the primal's constraint coefficients are transposed and its RHS constants become the dual's objective coefficients. • Duality theorems: any feasible dual solution gives a bound on the primal objective (weak duality), and at optimality the two objective values are equal (strong duality); complementary slackness links slack constraints to zero dual variables. • The optimal dual variable of a constraint is its shadow price — the improvement in the objective per unit increase in that resource, valid over the constraint's range of feasibility.

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A binding (fully used) resource has a positive shadow price; a resource with slack has a shadow price of zero. • Sensitivity (post-optimality) analysis asks how much data can change before the optimal solution or basis changes: ranges of optimality for objective-function coefficients (allowable increase/decrease with the solution unchanged) and ranges of feasibility for right-hand sides (over which the shadow price stays valid); reduced cost shows how much a non-basic variable's profit must improve before it enters the solution.

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Time Series and Moving-Average Forecasting • Components of a time series: trend (T), seasonal (S), cyclical (C) and irregular/random (I); models Y = T × S × C × I (multiplicative) or Y = T + S + C + I (additive). • Simple moving average:

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Ft+1 = (sum of the last n actual values)/n — larger n gives more smoothing but slower response.

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Weighted moving average puts more weight on recent data.

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Exponential smoothing:

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Ft+1 = αDt + (1 − α)Ft (0 < α < 1); higher α responds faster;

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Holt's method adds trend and Winters' method adds seasonality. • Trend projection by least squares (y = a + bt) and seasonal indices = average of (actual ÷ moving average) values, used to deseasonalise data and to reseasonalise forecasts. • Forecast error e = actual − forecast; measures:

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MAD = Σ|e|/n, MSE = Σe²/n, MAPE = (Σ|e/actual|)/n × 100, bias (mean error) and the tracking signal = cumulative error ÷ MAD (should stay within about ±4).

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Qualitative methods (Delphi, market survey, sales-force opinion) are used when data are scarce.

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Game Theory • Two-person zero-sum game: one player's gain equals the other's loss; strategies and payoffs are shown in a payoff matrix (entries = payoff to the row player). • Pure strategies and saddle point: the row player uses maximin (maximum of row minima), the column player minimax (minimum of column maxima); when maximin = minimax a saddle point exists and that value is the value of the game.

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A game with value zero is fair. • Dominance rule reduces the matrix by deleting dominated rows (all payoffs smaller) and columns (all payoffs larger).

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If no saddle point exists, mixed strategies are used: for a 2 × 2 game with payoffs a, b (row 1) and c, d (row 2), p = (d − c)/[(a + d) − (b + c)] and the value V = (ad − bc)/[(a + d) − (b + c)]; larger games are solved graphically (2 × n) or by converting them to a linear programme. • Non-zero-sum games (prisoner's dilemma, bidding, price competition) use Nash equilibrium (7.1); applications in pricing, tendering, capacity expansion and negotiation.

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Simulation, Distributions and Monte Carlo Sampling • Simulation imitates the behaviour of a real system over time with a model, and is used when the system is too complex, stochastic or costly to analyse mathematically or to experiment upon (queues, inventory with variable demand and lead time, maintenance and spares, production lines, project risk, traffic). • Steps: define the problem and objectives → collect data and identify the probability distributions → build the model (logic, entities, resources, events) → validate and verify → design experiments (run length, replications, warm-up) → run and analyse statistically → implement. • Types: discrete-event (state changes at events — queues, job shops), continuous (differential equations — chemical plants, system dynamics), deterministic vs stochastic, static (Monte Carlo) vs dynamic, and agent-based; software — Arena, Simul8, FlexSim, AnyLogic, or spreadsheets for simple models. • Common distributions: binomial (number of successes in n trials), Poisson (arrivals/defects per unit time or area, mean = variance = λ), exponential (time between Poisson events — memoryless), normal (measurements, sums of many effects), uniform, triangular (expert estimates), beta (PERT activity times), Weibull (failure/life data), lognormal. • Monte Carlo sampling:

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(1) build the cumulative probability distribution of the random variable;

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(2) assign random-number intervals in proportion to the probabilities;

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(3) draw random numbers (tables or a generator) and read the corresponding values;

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(4) repeat many times and analyse the average results and their variability.

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Accuracy improves with the number of trials (error ∝ 1/√n).

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This section covers the concept and measurement of productivity, factors affecting productivity and improvement techniques, the concept of work study, recording techniques of method and motion study, time study and work measurement, and the determination of standard time.

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Productivity • Productivity = output ÷ input — a ratio of goods and services produced to the resources used.

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Partial productivity (labour, machine, material, energy, capital), multi-factor and total factor productivity; productivity index compares with a base period.

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Productivity is not the same as production (which is total output) or profitability. • Factors affecting productivity: technology and equipment, product design and standardisation, plant layout and material handling, raw-material quality and availability, workers' skill, training, health and motivation, working conditions and ergonomics, supervision and management systems, maintenance, capacity utilisation, power and infrastructure, and government policy and labour relations. • Improvement techniques: work study (method study and work measurement), automation and better technology, value engineering, standardisation and simplification, quality improvement and TQM, preventive maintenance/TPM, lean techniques and 5S, better layout and material handling, incentive schemes and participation, training and skill development, energy and material conservation, and information systems. • In Nepal, low industrial productivity is commonly attributed to small scale, old technology, power and transport constraints, limited skills and weak management practices — the classic field for industrial engineering.

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Work Study • Work study (ILO) = the systematic examination of the methods of carrying out activities so as to improve the effective use of resources and to set standards of performance.

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It has two parts: method study (motion study) — to find the best way of doing the job; and work measurement (time study) — to find how long the job should take. • Advantages: higher productivity with the same resources, lower cost, better layout and working conditions, a sound basis for planning, scheduling, costing, manpower estimation and incentive payment, and standardised methods.

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It requires the cooperation of workers and unions — the human aspect must be handled carefully.

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Method Study and Recording Techniques • Steps (SREDDIM):

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Select the job (economic, technical and human considerations) → Record the present method → Examine critically (the questioning technique: purpose — what/why, place — where, sequence — when, person — who, means — how; eliminate, combine, rearrange, simplify) → Develop the improved method → Define/Evaluate it → Install it (training and acceptance) → Maintain the new method (periodic audit). • Charts: operation process chart (outline process chart) — only operations and inspections; flow process chart — man/material/equipment type, using the five ASME symbols: operation (circle), transport (arrow), inspection (square), delay (D) and storage (triangle); two-handed (left-hand right-hand) process chart for short-cycle bench work; multiple activity chart (man-machine or gang chart) to study idle times and machine coupling;

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SIMO chart (simultaneous motion cycle chart) using therbligs from film analysis. • Diagrams and models: flow diagram (path of material on a scale plan), string diagram (movements traced with a thread — useful for measuring travel distance), travel chart (from-to chart), cycle graph and chronocycle graph (light traces of hand motion), templates and 3-D models, and video/micro-motion (analysis at 16–32 frames/s) and memo-motion (slow-speed) study. • Therbligs — 17–18 fundamental hand motions named by the Gilbreths (search, select, grasp, transport loaded, position, assemble, use, release, inspect, hold, rest, unavoidable delay, plan, etc.). • Principles of motion economy (three groups):

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(a) use of the human body — both hands should begin and end motions together and should not be idle at the same time, motions should be symmetrical and simultaneous, continuous curved motions are better than zig-zag, use momentum and ballistic movements, use the lowest classification of movement possible;

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(b) arrangement of the workplace — fixed locations for tools and materials, within the normal working area, gravity feed bins and drop delivery, good lighting and correct work height with a suitable chair;

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(c) design of tools and equipment — combine tools, pre-position them, use jigs/fixtures or foot-operated devices to relieve the hands, and shape handles for a comfortable grip.

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Work Measurement and Standard Time • Techniques: time study (stopwatch), work sampling (ratio-delay/activity sampling), predetermined motion time systems (PMTS — MTM, MOST, Work-Factor), synthesis from standard data, and analytical estimating for non-repetitive work. • Time-study procedure: select the job and the qualified average worker → obtain and record full details of method and conditions → break the job into elements (repetitive, occasional, constant, variable, foreign) → time the elements with a stopwatch (fly-back or continuous timing) for enough cycles (the number is determined statistically for a required accuracy and confidence) → rate the operator's performance → compute the basic time → add allowances → obtain the standard time. • Performance rating compares the observed pace with the standard pace (100 on the BSI 0-100 scale, 100% on the normal scale); rating systems include speed rating, Westinghouse (skill, effort, conditions, consistency), synthetic and objective rating. • Basic (normal) time = observed time × (rating ÷ 100). • Allowances: relaxation allowance = personal needs (≈ 5–7%) + fatigue/basic allowance (≈ 4% and more for heavy or awkward work, heat, noise, poor posture), contingency allowance (small unavoidable extra work or delays, ≤ 5%), process/unoccupied time allowance, interference, special and policy allowances. • Standard (allowed) time = basic time + allowances (often expressed as basic time × (1 + allowance fraction)); standard output = available time ÷ standard time.

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Standard times are used for costing, wage incentives, scheduling, capacity and manpower planning, line balancing and performance measurement. • Work sampling: many random instantaneous observations classify an operator/machine as working or idle; the proportion of observations equals the proportion of time.

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For 95% confidence the number of observations n = 4p(1 − p)/L² where p = proportion of the activity and L = absolute limit of error; it suits long-cycle and non-repetitive work, needs no stopwatch, and is less disturbing to workers.

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This section covers the concept of ergonomics, the design of man-machine systems, displays and controls, the design of workplaces, the measurement of work on the human body, and computer-based ergonomics.

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Concept of Ergonomics • Ergonomics (human factors engineering) is the scientific study of the relationship between people, their work and their environment, applied to fit the job to the worker rather than forcing the worker to fit the job.

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It draws on anatomy, physiology, psychology, anthropometry and engineering. • Domains: physical (posture, materials handling, repetitive motion, workplace layout, safety), cognitive (perception, memory, mental workload, decision making, human error, human-computer interaction) and organisational (macro-ergonomics) (work systems, shift work, teamwork, job design). • Objectives and benefits: health and safety (fewer musculoskeletal disorders and accidents), comfort, reduced fatigue, higher productivity and quality, fewer errors, better job satisfaction and lower absenteeism and turnover.

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Man-Machine System • A man-machine system is a closed loop: the machine's display presents information → the human senses and processes it (perception, decision) → the human acts on a control → the machine responds → the display changes.

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The environment (light, noise, heat, vibration) affects every stage. • Allocation of functions (Fitts' list): humans are better at pattern recognition, judgement in unexpected situations, improvisation, inductive reasoning and dealing with incomplete information; machines are better at speed, power, precise repetition, simultaneous multi-channel work, computation, long-term storage and monotonous vigilance.

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Systems are classified as manual, mechanical (semi-automatic) and automatic, with the human as supervisor/monitor in the last case. • Human information processing and error: attention, short-term memory limits (7 ± 2 items), reaction time, mental workload; errors are classified as slips, lapses, mistakes and violations, and are designed out by simple layouts, mistake-proofing (poka-yoke), warnings, checklists and training.

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Design of Displays and Controls • Visual displays: quantitative (exact value — digital counters best for precise reading; moving-pointer fixed-scale dials best for rate-of-change and checking), qualitative (trend or zone — coloured bands), check/status (warning lamps), and representational (mimic diagrams).

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Design rules: adequate size and contrast, simple scale markings (progression of 1, 5 or 10), pointer close to the scale, no parallax, meaningful colour coding (red = danger/stop, amber = caution, green = safe). • Auditory displays are preferred when the message is short and simple, calls for immediate action, the visual channel is overloaded, the operator moves about, or lighting is poor; most effective in the ≈ 500–3 000 Hz range, at least 10 dB above ambient noise. • Controls: hand-operated (push buttons, toggle switches, knobs, cranks, levers, handwheels, joysticks) and foot-operated (pedals for large forces, when the hands are busy); selection depends on the force, precision, speed and range required.

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Coding by shape, size, colour, location, labelling and mode of operation prevents confusion in the dark or under stress; important controls are guarded, recessed or interlocked against accidental operation, and emergency stops are large, red, mushroom-headed and easy to reach. • Compatibility: spatial (control next to or arranged like its display), movement (population stereotypes — clockwise, up or right means increase/on; a pointer moves in the same direction as the control), conceptual and cultural.

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Control-display ratio balances the coarse (travel) and fine (adjustment) movements; layout follows importance, frequency of use, sequence of use and functional grouping.

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Design of Workplaces • Anthropometry supplies body dimensions (static and dynamic/functional).

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Design approaches: design for extremes (clearances such as doorways and legroom for the 95th percentile male; reach distances for the 5th percentile female), design for adjustability (chairs, seat and monitor height — the preferred approach, usually 5th–95th percentile) and design for the average (only where the other two are impossible). • Working heights: measured from the elbow height of a standing or seated worker — precision work ≈ 50–100 mm above elbow height (with elbow support), light assembly at about elbow height, and heavy work ≈ 100–250 mm below elbow height.

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Work should be within the normal working area (sweep of the forearm, ≈ 350–400 mm) with occasional items in the maximum working area (full arm reach). • Sitting vs standing: sitting suits precision, light loads and long duration (with a good chair — adjustable height, lumbar support, footrest); standing suits heavy forces, large reaches and frequent movement; sit-stand arrangements reduce static load.

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Avoid prolonged static postures, bending and twisting of the trunk, overhead work and holding loads away from the body. • Environment: illumination appropriate to the task (roughly 100–200 lux for rough work, 300–750 lux for ordinary bench and office work, 1 000–2 000 lux for fine inspection), glare control; noise — the common occupational exposure limit is 85 dB(A) for 8 hours, with engineering control preferred over hearing protection; thermal comfort (temperature, humidity, air movement, radiant heat, work-rest cycles in hot work); vibration limits for hand-arm and whole-body exposure; safe access, housekeeping and guarding.

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Measurement of Work on the Human Body • Physiological measures of workload: oxygen consumption and energy expenditure (kcal/min or kJ/min — light work ≈ 2.5–5, moderate 5–7.5, heavy > 7.5 kcal/min); sustained work for an 8-hour shift should stay below roughly 5 kcal/min for men and 4 kcal/min for women, with rest allowances calculated for heavier work; heart rate (a simple field measure), blood pressure, body temperature, EMG for local muscle fatigue, and the Borg rating of perceived exertion. • Postural and manual-handling assessment tools:

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RULA (rapid upper limb assessment), REBA (rapid entire body assessment), OWAS, strain index, and the NIOSH lifting equation — RWL = LC × HM × VM × DM × AM × FM × CM with a load constant LC = 23 kg, and the lifting index LI = load weight ÷ RWL (LI > 1 indicates increasing risk). • Work-related musculoskeletal disorders (WMSDs) — low-back pain, tendinitis, carpal tunnel syndrome — arise from force, repetition, awkward and static postures, vibration and cold; controlled by redesign of the task and workplace, tool design, job rotation, rest pauses and training. • Rest allowances in work measurement (7.5) are derived from these physiological data.

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Computer-Based Ergonomics • VDT/computer workstation design: top of the screen at or slightly below eye level, viewing distance ≈ 50–70 cm, screen tilted to avoid glare and placed at right angles to windows; keyboard and mouse at elbow height with wrists straight and forearms supported; adjustable chair with lumbar support and feet flat (or a footrest); document holder at screen height; frequent micro-breaks and the 20-20-20 rule (every 20 minutes look 20 feet away for 20 seconds) against eye strain; standards such as ISO 9241 cover ergonomics of human-system interaction. • Software and cognitive ergonomics: usability (learnability, efficiency, memorability, error tolerance, satisfaction), Nielsen's heuristics, consistent screen layout, meaningful error messages, alarm management in control rooms, information display in SCADA/HMI. • Computer tools for ergonomic design: digital human modelling and digital mannequins (Jack, RAMSIS, CATIA Human) for reach, vision and posture analysis in CAD; motion capture and video analysis; simulation of assembly workplaces; computerised checklists (RULA/REBA software), and CAD-based anthropometric databases — allowing workplaces to be evaluated before they are built. • Emerging areas: exoskeletons and collaborative robots to reduce physical load, virtual-reality training, wearable sensors for posture and fatigue monitoring, and the ergonomics of remote and hybrid work.