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Solution Manual for Quantitative Analysis for Management, 14th Edition by Barry Render, Ralph Stair Jr, Michael Hanna| Updated Version 1 Jun| Newest Version£12.22
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Solution Manual for Quantitative Analysis for Management, 14th Edition by Barry Render, Ralph Stair Jr, Michael Hanna| Updated Version 1 Jun| Newest Version
Solution Manual for Quantitative Analysis for Management, 14th Edition by Barry Render, Ralph Stair Jr, Michael Hanna| Updated Version 1 Jun| Newest Version
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Solution Manual for Quantitative Analysis for
Management, 14th Edition by Barry Render, Ralph Stair
Jr, Michael Hanna| Updated Version 1 Jun| Newest
Version
,SOLUTION MANUAL FOR
QUANTITATIVE ANALYSIS FOR MANAGEMENT, 14TH EDITIONRENDER
CHAPTER 1-15
CHAPTER 1
Introduction to Quantitative Analysis
TEACHING SUGGESTIONS
Teaching Recommendation 1.1: Emphasizing the Significance of Qualitative Factors.
Section 1.1 provides students with an introduction to quantitative analysis, exploring various qualitative
factors such as federal legislation and emerging technology. As part of the discussion, students can be
encouraged to brainstorm additional qualitative factors that might influence quantitative analysis. Examples
such as waiting lines and project planning could be utilized to illustrate this concept.
Teaching Recommendation 1.2: Exploring Diverse Quantitative Analysis Applications.
Section 1.2 delves into practical applications of quantitative analysis. Students can be prompted to identify
other problems or domains that could benefit from the application of quantitative analysis techniques.
Teaching Recommendation 1.3: Delving into Conflicting Perspectives.
This section highlights potential challenges within the quantitative analysis approach, including conflicting
viewpoints within an organization. Students can engage in discussions around scenarios like staffing a
university registration desk, where differing perspectives exist between students and administrators. These
discussions foster a deeper understanding of the complexities inherent in quantitative analysis.
Teaching Recommendation 1.4: Addressing Data Acquisition Challenges.
A significant hurdle in quantitative analysis is acquiring accurate input data. Students can be tasked with
devising strategies to obtain necessary information for scenarios like inventory ordering or carrying cost
determination. Role-playing exercises, with students assuming analyst roles and instructors portraying
experienced inventory managers, can provide insight into the complexities of data acquisition.
Teaching Recommendation 1.5: Managing Resistance to Change.
This section examines the phenomenon of resistance to change within organizations. Students can be
challenged to formulate strategies for introducing new systems or changes effectively. Understanding and
addressing resistance is crucial for the successful implementation of quantitative analysis initiatives. Students
can explore reasons why individuals may resist new approaches, such as fear of change in inventory control
or forecasting systems.
1-1. Quantitative analysis involves employing mathematical equations or relationships to analyze specific
problems, yielding numerical outcomes useful for decision-making. On the other hand, qualitative analysis
investigates non-quantifiable factors in decision-making. While both approaches are typically utilized in
decision-making, this book focuses predominantly on quantitative techniques.
1-2. Quantitative analysis represents a systematic and rational approach to managerial decision-making,
devoid of emotions or arbitrary judgments. Organizations such as INFORMS, Decision Sciences Institute,
and Academy of Management advocate for this scientific approach.
1-3. Business analytics encompasses descriptive, predictive, and prescriptive analytics, providing insights
into past performance, future forecasts, and optimal operational strategies, respectively.
1-4. The quantitative analysis process involves defining the problem, developing a model, acquiring input
data, formulating a solution, testing and analyzing results, and finally implementing the solution. Accurate
data acquisition is crucial as it directly influences the effectiveness of the analysis.
1-5. Quantitative decision-making techniques have historical roots dating back to figures like Frederick W.
Taylor and gained prominence during World War II, continuing to evolve and influence managerial
practices.
1-6. Models in quantitative analysis can take various forms, including mathematical representations used to
solve specific problems across diverse fields.
1-7. Input data for quantitative analysis can be sourced from company reports, interviews, measurements,
and sampling methods. Ensuring data accuracy is paramount to avoid misleading outcomes, adhering to the
principle of "garbage in, garbage out."
1-8. Implementation is the final stage of the quantitative analysis process, involving the integration of the
solution into the organizational framework. Poor implementation can undermine the entire analytical effort.
1-9. Sensitivity analysis and post-optimality analysis enable decision-makers to gauge the impact of changes
in input data or models on the final solution. Sensitivity to variations necessitates thorough testing and
validation of both data and models.
1-10. Various quantitative terms like PERT, CPM, simulation, and mathematical programming may require
clarification for managers unfamiliar with these concepts, emphasizing the importance of clear
communication in quantitative analysis.
1-11. Quantitative analysts may find fulfillment in building models, data analysis, programming, or
, navigating the implementation process. Effective implementation often hinges on interpersonal skills and
understanding organizational dynamics, presenting challenges beyond analytical prowess.
1-12. Users need not become involved in technical aspects of the QA technique, but they should have an
understanding of what the limitations of the model are, how it works (in a general sense), the jargon
involved, and the ability to question the validity and sensitivity of an answer handed to them by an analyst.
1-13. Churchman meant that sophisticated mathematical solutions and proofs can be dangerous because
people may be afraid to question them. Many people do not want to appear ignorant and question an elaborate
mathematical model; yet the entire model, its assumptions and its approach, may be incorrect.
1-14. The break-even point is the number of units that must be sold to make zero profits. To compute this, we
must know the selling price, the fixed cost, and the variable cost per unit.
1-15. f = 350 s = 15 v=8
a) Total revenue = 20(15) = $300 Total variable cost = 20(8) = $160
b) BEP = f/(s − v) = 350/(15 − 8) = 50 unitsTotal revenue = 50(15) = $750
1-16. f = 150 s = 50 v = 20
BEP = f/(s − v) = 150/(50 − 20) = 5 units
1-17. f = 150 s = 50 v = 15
BEP = f/(s − v) = 150/(50 − 15) = 4.29 units
1-18. f = 400 + 1,000 = 1,400 s=5 v=3
BEP = f/(s − v) = 1,400/(5 − 3) = 700 units
1-19. BEP = f/(s − v)
500 = 1,400/(s − 3)
500(s − 3) = 1,400
s − 3 = 1,400/500
s = 2.8 + 3
s = $5.80
1-20. f = 2,400 s = 40 v = 25
BEP = f/(s − v) = 2,400/(40 − 25) = 160 per week
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