Chapter 5: Managerial Decision-Making and Analytics

1. Introduction

Managers make decisions continuously. They decide which products to offer, how many employees to schedule, which suppliers to use, whether to invest in technology, how to respond to competitors, and how to resolve conflicts. The quality of these decisions strongly influences organisational performance. Good decisions do not always produce successful outcomes because markets, people and external conditions are uncertain. Nevertheless, systematic decision-making improves the probability of achieving desirable results.

Managerial decision-making is the process through which managers identify problems or opportunities, develop and evaluate alternatives, select a course of action, implement it and assess its consequences. In contemporary organisations, this process is increasingly supported by analytics. Analytics involves the systematic use of data, statistical methods, models and visual tools to understand situations and support action.

Analytics does not replace managerial judgement. Data may be incomplete, inaccurate or open to different interpretations. Managers must combine evidence with experience, ethical reasoning, stakeholder understanding and awareness of organisational purpose. The central challenge is therefore not merely to collect more data, but to ask appropriate questions, interpret evidence carefully and make responsible decisions.

2. Learning Objectives

After studying this chapter, students should be able to:

1. Explain the meaning and importance of managerial decision-making and analytics.

2. Distinguish rational, bounded-rationality, intuitive and participative decision models.

3. Apply a systematic decision-making process to managerial problems.

4. Identify common cognitive biases, risks and sources of poor judgement.

5. Use basic quantitative and qualitative tools to compare alternatives.

6. Interpret tables, charts and dashboards for managerial purposes.

7. Explain ethical, governance and privacy issues related to data-informed decisions.

8. Recommend evidence-based actions in practical organisational situations.

3. Meaning and Concept

A decision is a choice among two or more alternatives. A decision may be routine, such as approving a standard purchase, or strategic, such as entering a new market. Decision-making includes both the mental and organisational activities that lead to a choice and the actions that follow it.

Managerial decisions may be classified in several ways. Programmed decisions are repetitive and can often be handled by rules or procedures. For example, an organisation may automatically reorder inventory when stock reaches a specified level. Non-programmed decisions are novel, complex and ambiguous. Acquiring a competitor or responding to a sudden regulatory change usually requires analysis and judgement.

Decisions may also be strategic, tactical or operational. Strategic decisions concern the long-term direction of the organisation. Tactical decisions translate strategy into departmental plans. Operational decisions concern daily activities. Decisions may be made under certainty, risk or uncertainty. Under certainty, relevant outcomes are known. Under risk, possible outcomes can be identified and probabilities can reasonably be estimated. Under uncertainty, outcomes or probabilities are unclear.

Managerial analytics is the use of data and analytical techniques to generate insight and improve decisions. Descriptive analytics asks, “What happened?” Diagnostic analytics asks, “Why did it happen?” Predictive analytics asks, “What is likely to happen?” Prescriptive analytics asks, “What should be done?” For example, a retailer may report falling sales, investigate the reasons, forecast future demand and recommend a revised pricing or promotion strategy.

4. Fundamental Principles

Several principles support effective decision-making.

Purpose and alignment. A decision should be connected to organisational objectives. A low-cost option is not necessarily suitable if the organisation’s objective is superior customer service or quality leadership.

Evidence and relevance. Managers should use reliable information that is relevant to the decision. More data is not always better; irrelevant information can distract attention and delay action.

Alternatives. A decision-maker should avoid treating the first acceptable option as the only option. Developing alternatives encourages creativity and makes comparison possible.

Consequences and trade-offs. Every alternative has benefits, costs, risks and opportunity costs. Choosing one course of action means giving up the benefits of another.

Proportionality. The effort spent analysing a decision should reflect its importance, cost and risk. A major investment deserves more rigorous analysis than a minor office purchase.

Participation and accountability. People affected by a decision may provide valuable knowledge and are more likely to support implementation when they understand the reasoning. However, participation must be organised so that responsibility remains clear.

Flexibility and learning. Because conditions change, managers should monitor results and revise decisions when evidence indicates that revision is necessary. Changing a decision in response to new evidence is not necessarily failure; it may demonstrate responsible learning.

Ethical responsibility. Decisions should respect law, fairness, human dignity, privacy and the legitimate interests of stakeholders. A decision can be profitable and still be unacceptable if it causes avoidable harm.

5. Major Components / Elements

The first component is the decision problem. Managers must distinguish the actual problem from its symptoms. Falling sales may be a symptom of poor product quality, weak distribution, unsuitable pricing or changing customer preferences. A clear problem statement identifies the gap between the current and desired conditions.

The second component is the objective. Objectives describe what the decision should accomplish. Effective objectives are specific enough to guide evaluation. For example, “reduce average customer waiting time by 20 per cent within six months while maintaining service quality” is more useful than “improve service.”

The third component is information. Relevant information may include financial data, customer feedback, employee knowledge, market research, operational records and legal requirements. Managers should assess the source, date, completeness and reliability of information.

The fourth component is the set of alternatives. Alternatives should be feasible and sufficiently different to create a meaningful choice. They may include maintaining the current approach, making a limited change, redesigning the process, outsourcing or postponing action while gathering additional information.

The fifth component is criteria. Criteria are the standards used to judge alternatives. Typical criteria include cost, revenue, quality, speed, risk, employee impact, customer value, sustainability and legal compliance. Criteria may conflict, making trade-offs unavoidable.

The sixth component is stakeholder impact. Stakeholders include employees, customers, owners, suppliers, regulators, communities and society. A decision that benefits one group may impose costs on another. Stakeholder analysis makes these effects visible.

The final components are implementation, monitoring and feedback. A decision has little value if it cannot be translated into action. Managers must assign responsibilities, allocate resources, establish timelines and define performance indicators.

6. Process / Framework / Methodology

A practical decision process consists of the following stages.

Stage 1: Identify the issue. Managers collect initial evidence and determine whether the situation is a problem, an opportunity or both. They should avoid premature conclusions.

Stage 2: Diagnose causes. Techniques such as the “five whys,” process mapping, interviews and cause-and-effect diagrams help identify underlying causes. Diagnosis should separate facts from assumptions.

Stage 3: Define objectives and constraints. Objectives state desired results, while constraints identify limits such as budget, time, staff capacity, regulations or technology.

Stage 4: Gather and analyse information. Data should be organised and examined using appropriate qualitative or quantitative methods. Managers should ask whether the data is representative and whether important variables are missing.

Stage 5: Generate alternatives. Brainstorming, benchmarking, scenario planning and consultation can produce options. At this stage, managers should encourage ideas before judging them.

Stage 6: Evaluate alternatives. Alternatives are compared against agreed criteria. Managers may estimate costs, benefits, probabilities and risks. Sensitivity analysis can show whether a conclusion changes when assumptions change.

Stage 7: Select a course of action. The selected alternative should be justified by evidence, objectives and ethical considerations. The decision-maker should document key assumptions and reasons.

Stage 8: Implement the decision. Implementation requires communication, resources, role allocation, training and change management. Resistance should be treated as information about concerns, not automatically as disloyalty.

Stage 9: Monitor and learn. Managers compare actual performance with targets. Variances should be investigated and corrective action taken. A post-decision review asks what worked, what did not and what can be learned.

7. Important Theories and Models

The rational model

The rational model assumes that decision-makers define a problem clearly, have access to relevant information, identify all important alternatives, evaluate consequences objectively and select the alternative that maximises organisational objectives. It is useful as an ideal standard and is particularly appropriate where objectives are clear and information is relatively available.

In practice, the model’s assumptions are demanding. Managers rarely know every alternative or consequence. Time, cost and uncertainty limit analysis. Therefore, the rational model should be used as a disciplined framework rather than as a claim that perfect optimisation is always possible.

The bounded-rationality model

Herbert Simon’s bounded-rationality perspective recognises that human rationality is limited by incomplete information, limited time, cognitive capacity and organisational pressures. Managers often satisfice: they select an alternative that is good enough to meet important requirements rather than searching indefinitely for the theoretically best alternative.

Bounded rationality is not carelessness. It is a realistic response to complexity. A manager may choose a supplier that meets quality, delivery and budget thresholds after evaluating three credible bids, rather than reviewing every supplier in the market.

The intuitive model

Intuition is a rapid judgement based on experience, pattern recognition and tacit knowledge. An experienced production supervisor may notice unusual sounds from machinery and act before a formal report indicates a problem. Intuition can be valuable when the manager has extensive relevant experience and receives reasonably consistent feedback.

Intuition is less dependable in unfamiliar or highly political situations. Confidence should not be confused with accuracy. Managers should test important intuitive judgements against evidence, alternative explanations and the views of others.

The participative model

Participative decision-making involves employees or other stakeholders in defining problems, developing alternatives or selecting actions. Participation may improve information quality, creativity, acceptance and commitment. It is particularly useful when employees possess specialised knowledge or must implement the decision directly.

Participation also has costs. It may take time, encourage conflict or create pressure to accept popular rather than effective solutions. Managers should clarify who has decision authority, how input will be used and how disagreements will be resolved.

The incremental model

The incremental model involves making small adjustments rather than attempting a complete solution at once. It is useful where uncertainty is high, resources are limited or learning through action is possible. Pilot projects and phased implementation are examples. Incrementalism reduces exposure but may preserve outdated assumptions or fail to address a fundamental problem.

The evidence-based management approach

Evidence-based management combines the best available research evidence, organisational data, professional expertise and stakeholder values. It discourages decisions based solely on tradition, personal preference or fashionable claims. Evidence must still be interpreted within the organisation’s context.

8. Practical Applications

Managers use decision tools in staffing, operations, finance, marketing, customer service and strategy. In recruitment, analytics can identify the skills associated with successful performance, but managers must ensure that selection criteria do not unfairly exclude groups. In operations, demand forecasts support inventory and staffing decisions. In marketing, customer segmentation and campaign results help allocate promotional budgets.

Qualitative tools are especially valuable when issues involve attitudes, meanings or future possibilities. Interviews can reveal why employees resist a new system. Focus groups can explore customer reactions to a proposed service. A SWOT analysis examines strengths, weaknesses, opportunities and threats, although it should be supported by evidence rather than completed as a list of opinions. Scenario planning considers how the organisation might respond to different future conditions.

Quantitative tools support comparison and estimation. A cost-benefit analysis compares expected benefits with relevant costs. A weighted scoring model assigns weights to criteria and scores each alternative. Break-even analysis identifies the sales volume at which total revenue equals total cost. Forecasting uses historical or external information to estimate future demand. Descriptive statistics such as averages, percentages, ranges and trends summarise data.

Data visualisation converts data into charts, graphs, maps or dashboards. Line charts are appropriate for trends over time; bar charts compare categories; scatter plots show relationships between two variables; and stacked charts show composition. A visualisation should have a clear purpose, accurate scales, readable labels and limited clutter. A visually attractive chart can still mislead if it uses distorted axes or omits important context.

9. Examples

Suppose a café is deciding whether to extend opening hours. The manager identifies the objective as increasing contribution without creating excessive labour cost. Relevant data includes sales by hour, wages, electricity costs, customer demand and competitor hours. Alternatives include retaining current hours, extending by one hour on weekdays, extending on weekends or conducting a four-week pilot. A weighted score may compare expected contribution, customer convenience, staffing difficulty and operational risk.

Consider a small manufacturer experiencing late deliveries. The manager should not immediately blame employees. Process data may show that delays begin with unreliable supplier deliveries or excessive changeover time. A cause-and-effect analysis, interviews and delivery records may identify the main causes. Options could include changing suppliers, holding safety stock, improving scheduling or redesigning the production process.

A service organisation may use a dashboard showing response time, customer satisfaction, complaints and repeat contacts. A decline in satisfaction alongside rising response time may indicate capacity problems. However, managers must examine whether the survey sample changed or whether a major event affected results. The dashboard supports investigation; it does not automatically explain causation.

10. Technology and Digital Applications

Spreadsheet software remains useful for budgets, break-even calculations, scenario analysis and weighted scoring. Business intelligence platforms can combine data from sales, finance and operations into interactive dashboards. Customer relationship management systems support analysis of customer histories and service outcomes. Enterprise resource planning systems integrate purchasing, inventory, production and accounting data.

Predictive tools use statistical or machine-learning methods to estimate events such as demand, customer churn or equipment failure. These tools may improve planning but require appropriate data, validation and human oversight. Managers should understand the difference between correlation and causation. A model may predict a result accurately without explaining why it occurs.

Cloud platforms make data available across locations, while mobile applications support real-time reporting. Digital tools also introduce cybersecurity, access-control and privacy risks. Technology should be selected because it improves a decision or process, not merely because it is fashionable. Data definitions, ownership and quality standards must be established before dashboards are trusted.

11. Strategic / Managerial Importance

Decision quality affects profitability, productivity, innovation, reputation and resilience. Strategic decisions determine the allocation of scarce resources and shape the organisation’s future capabilities. Analytics can reveal emerging customer needs, inefficient processes and changing market conditions.

Decision-making also influences organisational culture. When managers explain evidence, invite appropriate challenge and learn from outcomes, employees are more likely to develop a culture of inquiry. When leaders hide assumptions or punish honest reporting, employees may provide information that confirms existing beliefs.

Analytics can create competitive advantage when an organisation develops unique data, strong analytical capabilities and the managerial discipline to act on insight. The advantage does not come from data alone. Competitors may access similar technologies; the difference may lie in better questions, faster learning and more effective implementation.

12. Detailed Case Study

The following is a fictional illustrative case.

Background

BrightMart is a medium-sized grocery retailer with six urban stores. It has experienced rising online orders but inconsistent delivery performance. Customer complaints have increased, while managers disagree about whether the problem is insufficient staffing, poor route planning or inaccurate promised delivery times.

Problem

The regional manager must recommend an action within one month. The budget is limited, and the organisation wants to improve on-time delivery without increasing delivery prices. Some managers favour hiring drivers; others favour purchasing routing software.

Analysis

The manager forms a small team involving operations, customer service, finance and two delivery employees. The team examines eight weeks of orders, promised and actual delivery times, route distances, driver schedules, order volume by hour and complaint records. Interviews reveal that dispatchers often change routes manually and that orders are sometimes accepted after the capacity for a time slot has been reached.

The team calculates that 62 per cent of late deliveries occur during three peak periods. A simple comparison shows that routes with excessive last-minute additions have the highest delay rate. Customer feedback indicates that customers prefer accurate delivery windows to unrealistic same-day promises. The team recognises that hiring drivers immediately may treat the symptom rather than the cause.

Three alternatives are evaluated: hire two drivers, purchase routing software, or introduce capacity controls and pilot routing software in two stores. Criteria are weighted as follows: improvement in on-time delivery, cost, implementation speed, employee impact and scalability. The third option receives the highest overall score because it combines a low-cost operational change with a controlled technology test.

Strategy / Solution

BrightMart introduces maximum order limits for peak time slots, revises promised delivery windows and pilots routing software in two stores. Dispatchers receive training, and drivers participate in testing the routes. A four-week pilot is established with indicators for on-time delivery, complaint rate, cost per delivery, driver overtime and customer satisfaction.

The manager communicates that the pilot is an experiment, not a final judgement on employees. Weekly reviews identify problems, and drivers can report routes that appear unsafe or impractical. The team also compares pilot stores with stores using the previous process.

Outcome

After four weeks, on-time delivery in the pilot stores improves from 74 per cent to 89 per cent. Complaints fall by 31 per cent, and overtime decreases modestly. Some customers initially object to narrower order capacity, but satisfaction improves when delivery promises become more accurate. The organisation decides to refine and extend the system gradually rather than hire drivers immediately.

Learning Points

The case demonstrates the value of defining the real problem, combining quantitative and qualitative evidence, involving affected employees and using a pilot to manage uncertainty. It also shows that a decision should be assessed against several criteria rather than a single cost measure. Finally, technology was useful only when combined with process redesign, communication and monitoring.

13. Advantages / Benefits

Effective decision-making can improve resource allocation, reduce avoidable costs, increase service quality and strengthen accountability. Analytical methods make assumptions more visible and enable managers to compare alternatives consistently. Participation can increase commitment and reveal operational knowledge that senior managers lack. Visualisation can communicate complex patterns quickly. Pilots and feedback systems reduce the risk of making large irreversible commitments without learning.

14. Limitations / Challenges

Data may be incomplete, outdated, biased or collected for a different purpose. Small samples may not represent all customers or employees. Averages can conceal important differences between groups. Forecasts are uncertain, and models may fail when conditions change. Analytics can also create false precision: a numerical result may appear objective even when its assumptions are weak.

Decision processes can be affected by time pressure, organisational politics, groupthink, confirmation bias and overconfidence. Participation may become symbolic if leaders have already decided. Technology may be expensive and difficult to integrate. Employees may resist monitoring if they believe data will be used unfairly. Finally, ethical and privacy concerns can limit the use of personally identifiable information.

15. Ethical and Governance Considerations

Managers must determine whether data collection has a legitimate purpose and whether individuals understand how their data will be used. Personal information should be collected minimally, stored securely and accessed only by authorised users. Organisations should comply with applicable privacy and data-protection laws.

Algorithms and scoring systems should be examined for discriminatory effects. If a recruitment model consistently disadvantages a protected group, managers cannot defend it simply by saying that “the computer decided.” Data quality, model assumptions and outcomes require human review. Important decisions should be explainable to affected people where feasible.

Governance requires clear ownership of data, access controls, documentation, retention rules and audit processes. Managers should distinguish facts, estimates and opinions in reports. They should disclose material uncertainty and avoid manipulating charts to support a preferred conclusion. Ethical decision-making also considers employees, customers, communities and future generations, not only immediate financial returns.

16. Current and Emerging Trends

Organisations are increasingly using real-time dashboards, predictive analytics, automation and generative artificial intelligence. These technologies can summarise information, identify patterns and support scenario analysis. However, generated outputs may contain errors, fabricated sources or hidden bias. Human verification remains essential.

Augmented decision-making combines managerial judgement with machine recommendations. Explainable artificial intelligence seeks to show the factors influencing a model’s output. Process mining analyses digital records to reveal how work actually flows. Organisations are also developing data-literacy programmes so that non-specialist managers can question and interpret analytical outputs.

A further trend is responsible analytics, which integrates fairness, privacy, sustainability and accountability into the design of data systems. Organisations are moving from isolated reporting to integrated decision platforms, but integration increases the importance of governance and cybersecurity.

17. Best Practices

Managers should begin with a clear decision question rather than collect data without purpose. They should define success measures, identify affected stakeholders and establish decision rights. Data sources and assumptions should be documented. At least two or three credible alternatives should be considered, including the option of doing nothing.

Important decisions should use more than one perspective. Managers can combine performance data, customer or employee feedback, professional expertise and relevant research. They should actively seek disconfirming evidence and ask what would make the chosen alternative fail. Sensitivity analysis, pilot testing and contingency planning are valuable where uncertainty is high.

Decisions should be communicated in plain language, with reasons, responsibilities and expected outcomes. Managers should monitor leading indicators as well as final results. A post-decision review should focus on learning rather than blame, while still addressing negligence or misconduct. Finally, analytical systems should be reviewed regularly for accuracy, fairness, security and continued relevance.

18. Future Scope

The future of managerial decision-making will involve closer integration between human judgement, advanced analytics and automated systems. Managers will need stronger data literacy, statistical awareness and ethical reasoning. Routine decisions may become increasingly automated, allowing managers to focus on exceptions, relationships, innovation and judgement under ambiguity.

At the same time, organisations will need safeguards against excessive automation. Human oversight, transparent models and meaningful opportunities for appeal will be important in decisions affecting employment, credit, access to services and customer rights. Sustainable decision-making will also incorporate environmental and social data alongside financial performance.

19. Chapter Summary

Managerial decision-making is the process of identifying problems and opportunities, evaluating alternatives, selecting action, implementing it and learning from results. Analytics strengthens this process by using data, models and visualisation to generate insight. The rational model provides a disciplined ideal, while bounded rationality recognises limits on information and human capacity. Intuition can be valuable when supported by relevant experience, and participation improves knowledge and commitment when properly managed.

Effective decisions require clear objectives, reliable information, alternatives, criteria, stakeholder analysis, implementation and feedback. Quantitative tools include weighted scoring, cost-benefit analysis, break-even analysis and forecasting. Qualitative tools include interviews, scenario planning, SWOT analysis and process mapping. Technology can improve speed and visibility but introduces privacy, bias, security and governance challenges. Evidence-based management is therefore both an analytical and ethical responsibility.

20. Key Terms

1. Analytics: The systematic use of data and methods to generate insight for decision-making.

2. Bounded rationality: The view that decisions are limited by information, time and human cognitive capacity.

3. Cognitive bias: A systematic pattern of judgement that can produce errors.

4. Decision criteria: Standards used to evaluate alternatives.

5. Decision risk: The possibility that an outcome will differ from what was expected.

6. Descriptive analytics: Analysis explaining what has happened.

7. Diagnostic analytics: Analysis investigating why something happened.

8. Evidence-based management: Management based on research, organisational data, expertise and stakeholder values.

9. Forecasting: Estimating future conditions or outcomes using available information.

10. Intuition: Rapid judgement based on experience and pattern recognition.

11. Participative decision-making: Involving relevant people in the decision process.

12. Predictive analytics: Analysis estimating what is likely to happen.

13. Prescriptive analytics: Analysis recommending what should be done.

14. Programmed decision: A repetitive decision handled through rules or procedures.

15. Satisficing: Selecting an alternative that is good enough rather than perfectly optimal.

16. Sensitivity analysis: Testing how results change when assumptions change.

17. Stakeholder: A person or group affected by, or able to affect, an organisation.

18. Weighted scoring model: A method that scores alternatives against weighted criteria.

21. Review Questions

Short Answer Questions

1. What is managerial decision-making?

2. Distinguish between descriptive, diagnostic, predictive and prescriptive analytics.

3. What is meant by bounded rationality?

4. State two advantages of participative decision-making.

5. Why should managers monitor decisions after implementation?

Long Answer Questions

1. Explain the stages of a systematic managerial decision-making process.

2. Compare the rational, bounded-rationality, intuitive and participative decision models.

3. Discuss how quantitative and qualitative tools can be combined in management decisions.

4. Explain the ethical and governance issues associated with data-informed decisions.

5. Evaluate the benefits and limitations of dashboards and predictive analytics.

Analytical / Application Questions

1. A retailer has declining profits but increasing sales. Design a decision process that would help the manager diagnose the problem and compare possible solutions.

2. A company uses an algorithm to shortlist job applicants, but the results show that applicants from one group are rarely selected. What investigations and actions should management undertake?

3. A small business must choose between purchasing new equipment, outsourcing production or improving its existing process. Develop suitable criteria and describe how a weighted scoring model could support the decision.

22. Multiple Choice Questions with Answers

Question 1

Which statement best describes bounded rationality?

A. Managers always have complete information and select the optimal alternative

B. Managers make decisions within limits of time, information and cognitive capacity

C. Managers rely exclusively on group voting

D. Managers avoid using evidence and depend only on instinct

Correct Answer: B. Managers make decisions within limits of time, information and cognitive capacity

Question 2

A line chart is generally most suitable for showing which type of information?

A. Changes in a measure over time

B. The legal rights of stakeholders

C. The sequence of interview questions

D. The personal opinions of employees

Correct Answer: A. Changes in a measure over time

Question 3

Which activity belongs to the diagnostic stage of analytics?

A. Reporting last month’s sales total

B. Estimating next year’s demand

C. Investigating why customer complaints increased

D. Recommending a new pricing decision

Correct Answer: C. Investigating why customer complaints increased

Question 4

A manager selects a supplier that meets all minimum quality, delivery and budget requirements instead of examining every supplier. This illustrates:

A. Satisficing

B. Groupthink

C. Scenario planning

D. Prescriptive analytics

Correct Answer: A. Satisficing

Question 5

What is the main purpose of sensitivity analysis?

A. To remove all uncertainty from a decision

B. To test how changes in assumptions affect results

C. To replace stakeholder consultation

D. To guarantee that a forecast is correct

Correct Answer: B. To test how changes in assumptions affect results

Question 6

Which practice best reduces the risk of misleading data visualisation?

A. Using decorative effects and multiple colours

B. Removing labels to simplify the chart

C. Using accurate scales, clear labels and relevant context

D. Displaying only the data that supports management’s preference

Correct Answer: C. Using accurate scales, clear labels and relevant context

Question 7

A pilot project is particularly useful when:

A. The decision is completely routine and risk-free

B. Managers want to learn while limiting exposure to uncertainty

C. Stakeholders must not know about the decision

D. No performance measures can be established

Correct Answer: B. Managers want to learn while limiting exposure to uncertainty

Question 8

Which is an ethical concern in algorithmic employee selection?

A. The algorithm processes information quickly

B. The system may produce discriminatory outcomes

C. The organisation can document its criteria

D. Managers can compare applicants consistently

Correct Answer: B. The system may produce discriminatory outcomes

Question 9

Which alternative is an example of a programmed decision?

A. Entering an unfamiliar international market

B. Acquiring a major competitor

C. Reordering inventory when stock reaches a set level

D. Responding to a sudden industry crisis

Correct Answer: C. Reordering inventory when stock reaches a set level

Question 10

Why should managers combine data with professional judgement?

A. Data is always irrelevant to managerial decisions

B. Judgement guarantees that data will be accurate

C. Data may be incomplete or ambiguous and requires contextual interpretation

D. Professional judgement eliminates the need for monitoring

Correct Answer: C. Data may be incomplete or ambiguous and requires contextual interpretation