Question 1

Risk analysis and profit uncertainty: which approach is most suitable for generating a probability distribution of profit (rather than just a single point estimate)?

Accepted Answer

Introduction / Context: Managers often need not only an expected profit but an understanding of uncertainty around that profit. A probability distribution captures the range of possible outcomes and their likelihoods, supporting risk-aware decisions such as capacity planning and capital budgeting. The question asks which approach is appropriate for producing such a distribution, not merely a single scenario or deterministic forecast. Given Data / Assumptions: We seek methods that propagate uncertainty through a model to yield a distribution of profit. Inputs (prices, volumes, costs) may be random variables with specified distributions. We distinguish general tools (spreadsheets) from methodological approaches (simulation, stochastic modeling). Concept / Approach: Management science techniques—especially Monte Carlo simulation and stochastic modeling—draw random samples from input distributions, evaluate the profit model repeatedly, and aggregate outcomes to form an empirical distribution. This directly yields not only the mean but also variance, quantiles, and tail risk. Spreadsheets can implement these techniques, but the technique itself is the essential ingredient. Time-series analysis forecasts a variable over time; it does not, by itself, produce a profit distribution that integrates multiple uncertain drivers. Sensitivity analysis varies one factor at a time and reports impact; it does not assign probabilities to outcomes. Step-by-Step Solution: Model profit = revenue − cost with uncertain inputs (e.g., demand, price, unit cost). Specify probability distributions for each uncertain input. Run Monte Carlo: iterate many trials, sampling inputs and computing profit. Summarize outcomes into a histogram, mean, variance, and confidence intervals. Verification / Alternative check: Risk analysis practice widely uses simulation (a management science technique) to produce distributions and decision metrics such as Value at Risk or probability of loss. Why Other Options Are Wrong: Electronic spreadsheet: a container/tool, not inherently a probabilistic method. Artificial intelligence: broad field; not specifically targeted to profit distributions. Time-series analysis: forecasts single series; does not combine multi-input uncertainty. Sensitivity analysis: explores what-ifs without probability weights. Common Pitfalls: Confusing the software used with the technique; assuming what-if tables equate to probabilistic risk analysis. Final Answer: management science techniques

Question 2

End-user education for new systems: who can legitimately conduct training and educational programs during and after MIS implementation?

Accepted Answer

Introduction / Context: Successful MIS implementation depends on effective training and change management. Different stakeholder groups bring complementary strengths: HR can design curricula and facilitate sessions, the IS team knows the system's technical details, and supervisors contextualize training for day-to-day workflows. Coordinated involvement raises adoption and reduces post-go-live issues. Given Data / Assumptions: Training spans system navigation, security, data entry quality, and process changes. Adult learning benefits from practice, relevance, and reinforcement on the job. Multiple trainers ensure coverage from principles to hands-on application. Concept / Approach: HR (or a learning and development unit) designs programs, schedules sessions, and measures training outcomes. IS staff deliver technical demonstrations, answer configuration questions, and handle troubleshooting. Supervisors connect system use to performance expectations and local procedures, offering coaching and reinforcement. This division of labor reflects best practice in organizational change initiatives. Step-by-Step Solution: Map training needs: conceptual understanding, system operation, and job-specific application. Assign instructors accordingly: HR for pedagogy, IS for technology, supervisors for context. Combine efforts into a comprehensive training plan. Select the inclusive option “All of the above.” Verification / Alternative check: Change frameworks (for example, ADKAR) emphasize leadership alignment, training, and reinforcement at the local level in addition to central instruction, supporting this multi-party approach. Why Other Options Are Wrong: Any single group alone risks gaps in either pedagogy, technical accuracy, or contextual relevance. None: incorrect because all listed groups can and should contribute. Common Pitfalls: Relying solely on technical demos without job context; failing to follow up training with on-the-job reinforcement from supervisors. Final Answer: All of the above

Question 3

Linear programming terminology: the variable introduced with no real-world meaning, used solely to obtain an initial basic feasible solution, is called what?

Accepted Answer

Introduction / Context: In linear programming, the simplex method often requires an initial basic feasible solution (BFS). When such a starting solution is not readily apparent from the constraints, the “two-phase” method or “Big-M” method introduces special variables to force feasibility temporarily. Understanding the purpose and nature of these variables is essential for interpreting algorithm steps correctly. Given Data / Assumptions: We are dealing with LP feasibility and simplex initialization. The added variable does not represent a physical quantity. The goal is to remove it once feasibility is established. Concept / Approach: An artificial variable is appended to constraints to create an initial identity basis so that the simplex method can start. In Phase I of the two-phase method, the objective is to minimize the sum of artificial variables, driving them to zero and yielding a feasible solution in the original variables. If they cannot be driven to zero, the LP is infeasible. With the Big-M approach, large penalties in the objective discourage artificial variables from remaining positive in the optimal solution. Step-by-Step Solution: Identify the need: no obvious BFS from slack/surplus variables alone. Introduce artificial variables to form an initial basis. Use Phase I objective (minimize sum of artificial variables) to achieve feasibility. Proceed to Phase II (original objective) once artificial variables are eliminated. Verification / Alternative check: Standard LP textbooks describe artificial variables as computational devices without physical interpretation, introduced purely to obtain an initial BFS. Why Other Options Are Wrong: Basis/basic variable: general simplex terms, but not the special introduced variable. Algorithm: refers to the method (simplex/two-phase), not the variable. None: incorrect because “artificial variable” is the precise term. Common Pitfalls: Assigning a physical meaning to artificial variables; forgetting to remove them before interpreting the final solution. Final Answer: artificial variable

Question 4

Expert systems taxonomy: expert systems belong to which broader area of research and application?

Accepted Answer

Introduction / Context: Expert systems are computer programs that emulate the decision-making ability of human specialists in narrow domains, using encoded knowledge (rules, frames) and inference engines. They were among the earliest commercially successful applications of the broader field that studies intelligent behavior in machines. Given Data / Assumptions: The question asks for the umbrella discipline, not a product or organization name. Common acronyms: AI = Artificial Intelligence; ES = Expert Systems (a subarea label, not the umbrella). Other options (STUDENT, RAND) are unrelated distractors in this context. Concept / Approach: Artificial Intelligence encompasses multiple paradigms: rule-based reasoning (expert systems), search, planning, natural language processing, machine learning, and more. Expert systems specifically embody symbolic AI techniques, particularly production rules and certainty factors, to provide explanations and recommendations in domains such as medicine, configuration, and finance. Step-by-Step Solution: Identify expert systems as a subset of symbolic AI. Map subset to umbrella term “Artificial Intelligence (AI).” Select “AI.” Verification / Alternative check: Historical surveys of AI list expert systems as a major wave of AI commercialization during the 1980s, reinforcing that AI is the parent field. Why Other Options Are Wrong: ES: shorthand for the subset itself, not the broader area. STUDENT / RAND: do not denote the umbrella discipline for expert systems. None: incorrect because “AI” is explicitly correct. Common Pitfalls: Confusing category (AI) with subcategory (ES) or with unrelated acronyms. Final Answer: AI

Question 5

Inventory control (EOQ context): if safety stock is 6 units and the economic order quantity (EOQ) is 50 units, what is the average inventory level assumed under a continuous review system?

Accepted Answer

Introduction / Context: Average inventory is a key performance and cost driver in inventory management. Under the classic EOQ model with continuous review and steady demand, on-hand inventory cycles between a maximum level and a minimum level. When safety stock is maintained to protect against uncertainty, the average on-hand amount increases by the safety buffer. Knowing how to compute average inventory is important for holding-cost estimation and service-level planning. Given Data / Assumptions: Safety stock s = 6 units. Economic order quantity Q = 50 units. Standard EOQ assumptions: constant demand and lead time, immediate replenishment of Q, and continuous review. Concept / Approach: In the basic EOQ framework with safety stock, the cycle (or “cycle stock”) component averages to Q/2 over time because inventory depletes linearly from Q down toward zero during each cycle. Safety stock is a constant buffer added to whatever cycle stock remains. Therefore, average on-hand inventory equals safety stock + average cycle stock. In symbols: Avg_Inventory = s + Q/2. Step-by-Step Solution: Compute average cycle stock: Q/2 = 50/2 = 25. Add safety stock: s + Q/2 = 6 + 25 = 31. Compare 31 against the provided options. Determine that none of the numeric options match 31; therefore, select “None of the above.” Verification / Alternative check: If demand or lead-time variability were modeled differently, the safety stock value might change, but with s = 6 and Q = 50 the EOQ average inventory formula yields 31 consistently. Cross-checking with inventory textbooks confirms Avg_Inventory = s + Q/2 under standard assumptions. Why Other Options Are Wrong: 80, 85, 86, 90: each implies either a much larger safety stock or a different modeling assumption. None matches the standard calculation using the given numbers. Common Pitfalls: Confusing “maximum inventory” (s + Q) with “average inventory;” forgetting to add safety stock to the cycle-stock average; or using end-of-period rather than time-weighted averages. Final Answer: None of the above

Question 6

Operations Research — Transportation models: What do we call a Transportation Problem in which the total supply available at all origins exactly equals the total demand required at all destinations (so no dummy source or dummy destination is needed)?

Accepted Answer

Introduction / Context: In Operations Research, the Transportation Problem allocates shipments from multiple origins (suppliers) to multiple destinations (customers) at minimum cost while respecting supply and demand constraints. A key diagnostic before solving is whether the problem is balanced or unbalanced, because that choice determines if we must add a dummy source or destination and which methods apply directly without modification. Given Data / Assumptions: Total supply across all origins may equal or differ from total demand across all destinations. Costs per unit are known and nonnegative; capacities (supply/demand) are fixed. We are identifying the correct term when total supply equals total demand, exactly. Concept / Approach: A Balanced Transportation Problem is defined by the equality: sum of supplies = sum of demands. When this holds, the model can be solved directly by methods like Northwest Corner, Least Cost, or Vogel’s Approximation to get a starting solution, and then optimized with Stepping Stone or MODI without introducing dummy rows/columns. If totals differ, the model is Unbalanced and requires a dummy source or destination to reconcile the totals before proceeding. Step-by-Step Solution: Compute total_supply = sum of all origin supplies. Compute total_demand = sum of all destination demands. Compare totals: if total_supply == total_demand, the problem is balanced. Conclude the correct term is Balanced Transportation Problem. Verification / Alternative check: Attempt to set up the linear program. If all constraints can be written without slack from a dummy node and the equality condition holds, the instance is balanced by definition. Why Other Options Are Wrong: Degenerate Solution: Refers to basic feasible solutions with fewer positive allocations than m + n − 1, not the supply-demand equality condition. Unbalanced Transportation Problem: Applies when total supply ≠ total demand. All/None: Incorrect because exactly one term fits the definition. Common Pitfalls: Forgetting to add a dummy node when the problem is unbalanced; misclassifying degeneracy as balance/unbalance. Final Answer: Balanced Transportation Problem

Question 7

Decision analysis and MIS terminology: What is the correct term for limits or restrictions imposed by the firm or its external environment on a decision maker (for example, capacity limits, budgets, or regulations)?

Accepted Answer

Introduction / Context: In decision models and Management Information Systems (MIS), we separate controllable choices from non-negotiable boundaries. These boundaries—such as budgets, machine capacities, regulatory requirements, and deadlines—shape what solutions are feasible. Using the correct term matters for formulating linear programs, dashboards, and what-if analyses. Given Data / Assumptions: The focus is on limits that restrict choices (not just descriptive inputs). Examples include capacity ceilings, minimum service levels, or policy rules. We seek the standard modeling term used across OR/MS and analytics. Concept / Approach: Constraints are mathematical or policy restrictions that candidate solutions must satisfy. In linear programming, they appear as equalities or inequalities; in policy contexts, they are business rules enforced by the system. By contrast, parameters are numerical inputs to a model (e.g., demand, cost coefficients), and constructs are conceptual variables in research design, not operational limits. “Perimeters” are physical boundaries, not modeling restrictions. Step-by-Step Solution: Identify the nature of the item: a limit that narrows feasible choices. Map to modeling language: limits → constraints. Eliminate similar-sounding but incorrect terms (parameters, constructs, perimeters). Choose “constraints.” Verification / Alternative check: Any optimization model shows decision variables bounded by constraints; removing a constraint enlarges the feasible region, confirming the role. Why Other Options Are Wrong: Parameters: Inputs to a model, not restrictions. Constructs: Research/measurement concepts, not operational limits. Perimeters: Physical boundary lines, not decision rules. None: Incorrect because “constraints” is standard. Common Pitfalls: Confusing parameters with constraints; failing to encode a business rule as a formal constraint, leading to infeasible recommendations later. Final Answer: constraints

Question 8

Management reporting practice: Which statements are generally true about how management reports are produced and used across organizational levels?

Accepted Answer

Introduction / Context: Management Information Systems tailor information by organizational level and by timing. Operational users require granular data; middle management needs summarized views and exceptions; issuance can be periodic (daily/weekly), event-driven (thresholds, alarms), or on demand. Recognizing these patterns ensures reports add value without overwhelming their audience. Given Data / Assumptions: Organizations have multiple levels: operational, middle, and executive. Information cadence varies by process and decision urgency. Exception and summary reporting are common for mid-level oversight. Concept / Approach: Best practices align report granularity and frequency with user needs. Shop-floor or front-line supervisors act on detailed operational reports. Middle managers rely on summary metrics (KPIs, trends) and exception reports that highlight deviations requiring action. Systems support multiple issuance modes: scheduled (periodic), triggered (event-based), and ad hoc (on demand), covering the full range of decision contexts. Step-by-Step Solution: Map each statement to a known reporting practice. Confirm low-level managers use detail; middle managers use summary and exception. Confirm issuance modes include periodic, event-driven, and on demand. Conclude all statements are true → select “All of the above.” Verification / Alternative check: Standard MIS texts and dashboard design guides show level-based granularity and the use of exceptions to focus attention efficiently at middle levels. Why Other Options Are Wrong: Each single statement is correct yet incomplete alone; the comprehensive choice captures the whole practice. Common Pitfalls: Sending detailed transaction dumps to executives; failing to implement exception thresholds; issuing only periodic reports when event-driven alerts are needed. Final Answer: All of the above

Question 9

EDP/IT auditing competence: An effective EDP auditor should primarily be expert in which domain to properly review controls and evaluate risks in computerized environments?

Accepted Answer

Introduction / Context: EDP (Electronic Data Processing) auditors evaluate control design and operating effectiveness in IT systems. While familiarity with accounting and programming helps, the core competency is understanding how computerized business systems process, store, secure, and report information—across applications, databases, platforms, and networks—so audit objectives can be met. Given Data / Assumptions: Audits cover application controls, IT general controls, interfaces, and data integrity. Auditors must assess risks from authentication, authorization, changes, and operations. Breadth of systems knowledge outweighs deep coding expertise for audit purposes. Concept / Approach: Expertise in computerized business systems enables auditors to map processes to controls (input, processing, output), evaluate segregation of duties, inspect logs, and test configurations. Accounting knowledge supports financial audits, and systems analysis skills help understand requirements, but the audit focus is whether computerized processes reliably achieve business and compliance objectives, which is wider than programming alone. Step-by-Step Solution: Identify the audit scope: applications, databases, OS, network controls. Match required expertise to evaluating these components holistically. Recognize that coding skill is helpful but not central for audits. Select “computerized business systems.” Verification / Alternative check: Audit frameworks encourage IT auditors to understand systems architecture, control catalogs, and risk models rather than serve as developers. Why Other Options Are Wrong: Accounting systems: Important, but a subset of the broader computerized systems domain. Systems analysis: Useful skill, not the primary audit expertise. Computer programming: Depth in coding is not required to assess controls across diverse platforms. None: Incorrect because a clear domain is identified. Common Pitfalls: Overemphasizing code reviews while neglecting access, change, and operations controls; failing to understand data flows across systems. Final Answer: computerized business systems

Question 10

Transaction Processing Systems (TPS): Which characteristic best describes TPS in terms of complexity and computerization of routine, structured activities?

Accepted Answer

Introduction / Context: Transaction Processing Systems (TPS) capture, validate, and record high-volume routine transactions such as orders, receipts, payments, and time punches. Because these processes are standardized and rule-driven, TPS are prime candidates for automation and real-time processing in enterprise systems like ERP and POS platforms. Given Data / Assumptions: TPS handle structured, repetitive tasks with well-defined inputs and outputs. They operate at the operational level, supporting day-to-day activities. Upper management primarily uses summarized dashboards, not TPS screens. Concept / Approach: TPS are fairly easy to computerize because business rules can be codified, data validations are formulaic, and workflows are predictable. While TPS can feed summary information upward, providing summaries is not their defining trait; and they are not built specifically for upper executives. By contrast, DSS/ESS focus on summary and strategic insights, whereas TPS focuses on accurate, timely capture of detailed transactions. Step-by-Step Solution: Identify TPS characteristics: volume, structure, repeatability. Map those traits to ease of automation and rule encoding. Eliminate distractors (executive usage, difficulty to computerize). Choose “fairly easy to computerize.” Verification / Alternative check: Historic adoption of TPS (e.g., bank core systems, inventory control) demonstrates their suitability for early and extensive computerization. Why Other Options Are Wrong: Provides summary information: Possible output, but not defining; TPS focus on detailed transactions. Used by upper management: Upper managers consume summaries from MIS/ESS, not raw TPS screens. Difficult to computerize: Opposite of reality for structured, rule-based tasks. None: Incorrect since one choice is clearly accurate. Common Pitfalls: Mixing roles of TPS and MIS; assuming TPS must produce executive summaries rather than feeding them upstream. Final Answer: fairly easy to computerize

Question 11

MIS development responsibility: Who should develop a Management Information System so that it aligns with business needs and is technically sound?

Accepted Answer

Introduction / Context: Successful MIS implementations blend business insight with technical execution. Managers understand goals, KPIs, and processes; information specialists (analysts, engineers) translate those needs into data models, pipelines, and applications. Collaboration ensures usability, adoption, and maintainability of the resulting system and reports. Given Data / Assumptions: Managers own business outcomes and define requirements. Information specialists design and implement technical solutions. Both perspectives are necessary for a fit-for-purpose MIS. Concept / Approach: MIS development should be a joint effort. Managers articulate decision needs, exceptions, and cadence; information specialists propose architectures, integrate data, and enforce governance. This partnership avoids systems that are technically elegant yet irrelevant, or business-friendly yet technically fragile. It also accelerates iterative improvements via feedback loops and prototypes. Step-by-Step Solution: Elicit requirements from managers (metrics, drill-down, alerts). Translate into data models, ETL/ELT, and dashboards by specialists. Validate usability and correctness together via pilots. Conclude both groups must co-develop the MIS. Verification / Alternative check: Methodologies like Agile BI and product analytics emphasize product owners (business) partnering with engineering to deliver value incrementally. Why Other Options Are Wrong: Either group alone yields gaps—business-only lacks technical rigor; tech-only may miss business relevance. Common Pitfalls: Throwing requirements over the wall; inadequate user testing; failing to embed governance early. Final Answer: both (a) and (b)

Question 12

Transportation model optimality testing: After you build an initial feasible solution, which methods are widely used to test and improve a transportation solution toward optimality?

Accepted Answer

Introduction / Context: Transportation Problems are often approached in two phases: first, find an initial basic feasible solution; second, test for optimality and iterate if improvements exist. Two classic improvement tests—Stepping Stone and MODI (Modified Distribution)—provide systematic ways to evaluate reduced costs and adjust allocations to reduce total cost. Given Data / Assumptions: An initial solution exists (e.g., by Northwest Corner, Least Cost, or Vogel’s Approximation). Costs and supplies/demands are fixed and nonnegative. Goal is minimum total transportation cost. Concept / Approach: The Stepping Stone Method evaluates the opportunity cost of introducing a shipment into an unoccupied cell by tracing a closed path (alternating plus/minus) and computing net cost change. The MODI Method calculates row and column potentials (ui, vj) to find reduced costs efficiently for all empty cells. If any reduced cost is negative, you can pivot along a loop to improve the solution until no further improvement is possible. Step-by-Step Solution: Compute potentials (MODI) or evaluate loops (Stepping Stone). Identify any cell with negative reduced cost (opportunity for improvement). Adjust allocations along the closed path to decrease total cost. Repeat until no negative reduced cost exists; solution is optimal. Verification / Alternative check: Compare total costs after each iteration; they must monotonically decrease or remain unchanged once optimal. Why Other Options Are Wrong: Slack variable method: General LP concept; not the standard named test for transportation optimality. None: Incorrect because both listed methods are standard. Common Pitfalls: Forgetting degeneracy handling (m + n − 1 basic variables); mis-tracing loops; stopping before all reduced costs are nonnegative. Final Answer: Both of the above

Question 13

Public-sector information systems: At which level of government are computer-based urban traffic control systems (signal timing, congestion management, incident response) typically implemented and operated?

Accepted Answer

Introduction / Context: Traffic signal control, adaptive timing, and incident response systems are core urban infrastructure. While standards and funding may involve regional or national bodies, day-to-day operation and integration with city roads and intersections are typically responsibilities of local governments or municipalities (city traffic management centers). Given Data / Assumptions: Systems include sensors, controllers, communication networks, and central software. Road rights-of-way and intersections in cities are under local jurisdiction. Operational decisions respond to local conditions and events. Concept / Approach: Because the assets (signals, detectors, cameras) are local and the required responsiveness is immediate, local governments manage these systems. They coordinate with regional transportation authorities for arterials and with state/provincial agencies for highways, but urban signal control is centered at the municipal level for maximum responsiveness and alignment with city policies (transit priority, pedestrian safety, emergency preemption). Step-by-Step Solution: Identify asset ownership and jurisdiction (city streets). Recognize need for responsive, localized control. Conclude that operational control is at the local level. Select “Local.” Verification / Alternative check: City traffic management centers commonly publish performance dashboards and operate signal timing plans, confirming local responsibility. Why Other Options Are Wrong: “Top,” “bottom,” and “Top-to-bottom” are not governmental levels; responsibility is not primarily national/federal for city streets. Common Pitfalls: Confusing highway management (state/national) with city intersection control; assuming centralized national operation of local assets. Final Answer: Local

Question 14

Decision-Support Systems (DSS): DSS are primarily intended to produce information that supports which class of managerial decisions?

Accepted Answer

Introduction / Context: Decision-Support Systems assist managers with complex problems where procedures are not fully specified in advance. These decisions require judgment, exploration of scenarios, and incorporation of qualitative factors alongside quantitative data—features that distinguish DSS from routine transaction processing and fixed, preprogrammed reports. Given Data / Assumptions: DSS provide interactive models, what-if analysis, and ad hoc querying. Problems addressed lack fully defined algorithms or procedures. Users are typically middle to senior managers and analysts. Concept / Approach: Unstructured (and often semi-structured) decisions involve ambiguous objectives, uncertain data, and multiple criteria. DSS integrate model bases and data with user-friendly interfaces to explore alternatives. By contrast, well-structured, routine, and strictly anticipated decisions are often handled by TPS or MIS with standard rules and scheduled reports instead of flexible, exploratory tools. Step-by-Step Solution: Classify decision types by structure: routine vs. judgment-intensive. Map DSS capabilities (what-if, simulation, optimization) to unstructured needs. Eliminate structured/routine categories handled by TPS/MIS. Select “unstructured.” Verification / Alternative check: Classic frameworks (Gorry & Scott Morton) place DSS toward the less-structured end of decision problems, confirming the classification. Why Other Options Are Wrong: Structured, anticipated, or routine decisions typically use predefined procedures and standard reports, not DSS exploration. Common Pitfalls: Expecting DSS to replace TPS/MIS; neglecting data quality and governance when feeding DSS models. Final Answer: unstructured

Question 15

DSS architecture components: Which item below is NOT typically listed as one of the major components of a decision-support system (compared with the classic triad of language system, knowledge system, and problem-processing system)?

Accepted Answer

Introduction / Context: A Decision-Support System (DSS) typically comprises user interaction mechanisms, data/model knowledge resources, and processing control that coordinates analyses. Classic descriptions reference a language system (interface), a knowledge system (data/model base), and a problem-processing system (control/solver orchestration). Distinguishing these from separate AI technologies prevents architectural confusion. Given Data / Assumptions: DSS emphasize interactive analysis, modeling, and what-if exploration. Core subsystems: language (dialog), knowledge (data and models), and problem-processing (control). Expert systems are AI tools that can be embedded, but are not required DSS components. Concept / Approach: An expert system (rule-based inference) may complement a DSS, yet it is not a canonical DSS component. The language system provides the user interface for queries and visualization. The knowledge system houses data and models. The problem-processing system coordinates model execution, scenario management, and integration. While DSS can integrate AI, the baseline architecture does not mandate an expert system module. Step-by-Step Solution: List classic DSS subsystems: language, knowledge, problem-processing. Compare each option against the canonical triad. Identify “expert system” as outside the core DSS components (optional add-on). Select “expert system.” Verification / Alternative check: DSS literature consistently presents the triad without requiring rule-based AI; many effective DSS have no inference engine at all. Why Other Options Are Wrong: Language, knowledge, and problem-processing systems are widely recognized DSS components. Common Pitfalls: Equating DSS with AI; overengineering DSS by insisting on expert-system features where simple models suffice. Final Answer: expert system

Question 16

In managerial decision-making, which type of decision involves establishing organization-wide objectives and creating long-range plans to achieve those objectives?

Accepted Answer

Introduction / Context: Managers make decisions at different levels. Some choices are day-to-day and highly structured, while others determine the organization’s overall direction for years to come. This question checks whether you can correctly identify the type of decision associated with defining objectives and crafting long-range plans. Given Data / Assumptions: The focus is organization-wide direction, not a single department. The time horizon is long term (multi-year), not weekly or monthly. The activity includes setting objectives and choosing high-level policies. Concept / Approach: Decisions are commonly grouped into three levels: operational (short-term execution), tactical/management control (resource allocation and medium-term coordination), and strategic (long-term goals and policies). Strategic decisions are broad in scope, future oriented, and made under uncertainty. They commit major resources and shape markets, capabilities, and competitive positioning. Step-by-Step Solution: Identify keywords: “establishing objectives” and “long-range plans.”Map these to decision levels: long-range objectives → strategic.Eliminate options that describe mid-term resource allocation (tactical) or vague attributes (“relevant”, “prompt”).Select “strategic.” Verification / Alternative check: Classic frameworks (e.g., Anthony’s) define strategic planning as setting objectives and policies for the enterprise, while tactical/management control ensures resources are used effectively to meet those objectives. Why Other Options Are Wrong: Tactical: Medium-term allocation and coordination, not enterprise-wide objective setting. Prompt: Describes timeliness, not a decision class. Relevant: A quality of information, not a decision type. None of the above: Incorrect because “strategic” is the standard term. Common Pitfalls: Confusing tactical budgeting or scheduling with long-range strategic positioning; assuming any significant decision is “strategic” even when it is about mid-term resource allocation. Final Answer: strategic

Question 17

Operations management: labour (labor) standards can be established using which sources or methods?

Accepted Answer

Introduction / Context: Labour standards specify the normal time or effort required to perform a task. They are essential for capacity planning, costing, incentive systems, and scheduling. Understanding where these standards come from helps ensure that targets are both achievable and fair. Given Data / Assumptions: Industrial engineering (IE) techniques such as time and motion studies are available. Historical performance data from the shop floor is also available. We seek the valid sources for setting standards. Concept / Approach: Two common approaches exist. Analytical methods (e.g., time and motion studies, predetermined motion-time systems) directly measure or model the work to derive a standard time. Empirical methods analyze historical data—adjusting for outliers, learning effects, and abnormal conditions—to infer a realistic standard. Many organizations triangulate both methods to improve reliability. Step-by-Step Solution: Identify recognized IE methods: time and motion → valid.Consider historical data analysis: valid if cleaned and normalized.Conclude that both are acceptable sources.Select “both (a) and (b).” Verification / Alternative check: Operations texts describe setting standards through engineered standards (stopwatch, PMTS) and through analysis of historical records corrected for allowances and performance ratings. Why Other Options Are Wrong: Only time and motion: Ignores valid empirical alternatives. Only historical data: Ignores engineered standards that may better reflect best practice methods. Neither/None: Incorrect because both are recognized. Common Pitfalls: Using raw historical data without cleaning; setting standards from time studies but ignoring allowances (fatigue, delays) or method variation. Final Answer: both (a) and (b)

Question 18

Decision levels: which of the following is a tactical (management control) decision rather than a strategic or purely operational one?

Accepted Answer

Introduction / Context: Decisions are often categorized into strategic (long-term, high uncertainty), tactical/management control (medium-term, resource allocation), and operational (short-term execution). Distinguishing between these levels helps align information needs and authority structures. Given Data / Assumptions: Strategic: capital structure, product portfolio, plant location. Tactical/management control: budgets, department targets, resource allocation for the coming period. Operational: daily scheduling and transactions. Concept / Approach: Budget allocation fits the tactical level: management translates strategy into departmental resources and spending limits for the fiscal period. By contrast, capital-source, product, and plant-location decisions shape the firm’s long-term structure and are therefore strategic. Step-by-Step Solution: Classify each option by time horizon and scope.Identify “budget allocation” as medium-term resource distribution.Identify remaining options as strategic (long-term, high impact).Choose “budget allocation.” Verification / Alternative check: Management control literature places budgeting squarely in the management control (tactical) domain, connecting strategy to operations. Why Other Options Are Wrong: Capital-source: Strategic financing decision. Product decision: Strategic portfolio/positioning. Plant-location: Strategic footprint decision. None: Incorrect because one tactical example exists. Common Pitfalls: Assuming anything involving money is strategic; ignoring the time horizon and scope that define the decision level. Final Answer: budget allocation

Question 19

Marketing information systems: which authority most notably formalized the structure and definition of a Marketing Information System (MkIS)?

Accepted Answer

Introduction / Context: A Marketing Information System (MkIS) integrates people, procedures, and technology to collect, analyze, and distribute timely marketing information. The concept’s mainstream, textbook framing is strongly associated with one widely cited marketing scholar. Given Data / Assumptions: The question asks for the prominent authority who formalized MkIS structure in widely used texts. Options include plausible but incorrect names alongside the correct one. We focus on mainstream academic attribution. Concept / Approach: Philip Kotler’s marketing texts popularized and structured the MkIS concept—covering internal records, marketing intelligence, marketing research, and analytical models—making his name the most closely associated with defining MkIS in education and practice. Step-by-Step Solution: Recall marketing text authorship and MkIS chapters.Associate MkIS components (internal records, intelligence, research, models) with Kotler’s framework.Select “Philip Kotler.”Eliminate distractors not recognized for MkIS structure. Verification / Alternative check: Standard marketing curricula cite Kotler’s definitions for MkIS components and data flows supporting decision making. Why Other Options Are Wrong: Other listed names are not widely credited with the core MkIS formalization in mainstream teaching materials. Common Pitfalls: Confusing marketing research pioneers or econometricians with MkIS textbook authors; overlooking MkIS as more than research—it includes intelligence and analytics. Final Answer: Philip Kotler

Question 20

Order processing and fulfilment: what is the key coordinating procedure that aligns customer orders with available inventory across warehouses and channels?

Accepted Answer

Introduction / Context: In order processing, coordination is needed to assign stock to open orders based on priorities, availability, and service levels. This step sits between order entry and physical fulfilment and is central to promising accurate delivery dates (available-to-promise/ATP). Given Data / Assumptions: Orders may be fulfilled from multiple locations. Inventory is finite and may be reserved or backordered. Coordination aims to match demand with supply fairly and efficiently. Concept / Approach: Product allocation is the coordinating procedure that assigns available stock (and expected receipts) to specific orders according to rules (priority customers, channels, regions) and constraints (lot, shelf life, transportation). Warehouse is a facility, and custom production planning is a make-to-order scheduling activity that may not apply in pure distribution scenarios. Step-by-Step Solution: Identify the need: align orders with available items.Recognize the procedure that reserves stock: product allocation.Eliminate facility or production-specific distractors.Choose “product allocation.” Verification / Alternative check: ERP order management modules include allocation/allocation runs (ATP/CTP) that perform this coordination before picking and shipping. Why Other Options Are Wrong: Warehouse: A location, not a coordinating procedure. Custom production planning: Applicable to make-to-order manufacturing, not core to distribution order allocation. All/None: Incorrect given the precise coordinating step is allocation. Common Pitfalls: Skipping allocation and overselling; failing to consider reserved stock; ignoring priority rules leading to stockouts for key accounts. Final Answer: product allocation

Management Information Systems Questions