Thirty-Plus Practical Tools and Techniques for Making Your Key Financial Decisions

• Chapter 0 - Overview
  • Course Objectives
  • Introduction
  • Strategic Puzzle
    • Solution
  • Practical Tips, Tools, and Techniques for Making Your Key Financial Decisions
    • Chapter 1 – Environmental Forces Affecting Decisions
    • Chapter 2 – Turning Data into Information
    • Chapter 3 – Improvement – Problem Solving Tools
    • Chapter 4 – Analytical Models – Decision Aids
    • Chapter 5 – Forecasting and Budgeting
    • Chapter 6 – Improving Accounting Information and Analysis
    • Chapter 7 – Acquisitions and Divestitures
    • Chapter 8 – Capital and Other Long-Term Investments
• Chapter 1 - Environmental Forces Affecting Decisions
  • Learning Objectives
  • Introduction
  • Historic Focus of Financial Decisions
  • Decision Making – Art and Science
    • Behavioral Finance
  • Structural Blockages to Decisions
    • Perceived Risk
    • Inertia and Bias
    • Organizational Structure
    • Reward Systems
    • Paradigms
  • Summary
  • Questions
• Chapter 2 - Turning Data into Information
  • Learning Objectives
  • Introduction
  • Management’s Role
  • Graph
  • Data Management Tools
    • Average
    • Median
    • Mode
    • Comparison – Average, Mean, and Mode
    • Moving Average
    • Seasonality
    • Uneven Growth/Decline Patterns
    • Exponential Smoothing
  • Time Series
  • Probability
  • Summary
  • Questions
• Chapter 3 - Improvement – Problem Solving Tools
  • Learning Objectives
  • Introduction
  • Benchmarking
    • Competitive Benchmarking – Current Status
    • Functional Benchmarking – Future Opportunities
  • Target Costing
  • Mapping
  • Networking – PERT and CPM
  • Pareto Analysis
  • Cause and Effect Analysis/Fishbone Chart
  • Theory of Constraints
  • Summary
  • Question
• Chapter 4 - Analytical Models – Decision Aids
  • Learning Objectives
  • Introduction
  • Model Building
    • Duplicating the Real World
    • Forecasting Equations
    • Scatter Diagram
  • Regression Analysis
  • Linear Optimization
  • Inventory – Economic Order Quantity
    • Simple Models
  • Simulation – Monte Carlo Analysis
  • Summary
  • Question
• Chapter 5 - Forecasting and Budgeting
  • Learning Objectives
  • Introduction
  • Goals
  • Typical Budgeting Process
  • Introducing Models
    • Balance Sheets Accounts and Costs
    • Sales Forecasting
    • Dow Jones Index vs. Home Runs
    • Regression Model – Example
  • Constructing the Model – Interpreting Results
  • Selected References and Readings
    • Books
    • Articles
• Chapter 6 - Improving Accounting Information and Analysis
  • Learning Objectives
  • Introduction
  • Breakeven Analysis
    • Assumptions
    • Uses
  • Applying the Basics of Lean to the Accounting Function
    • Structure – A Delivery System
    • Project vs. Process
    • Traditional Accounting Practices and ABC
    • Beginning Steps to Lean
  • Establishing Targets
    • Balanced Scorecard
    • Economic Value Added
  • Identifying Future Failures
    • Markets – Competitive Forces
    • Things to Watch
    • Discriminant Analysis – Z-Score
  • Summary
  • Case 6-1 – Measuring the Right Things – We Care Air (WCA)
    • Questions
• Chapter 7 - Acquisitions and Divestitures
  • Learning Objectives
  • Introduction
  • M&A Activity
    • Reasons for M&A
  • Role of Financial Professionals
    • Establishing the Environment
    • Continue to Ask Questions
    • Valuation Approaches
  • Parting Thoughts – Due Diligence
    • Execution Plan
• Chapter 8 - Capital and Other Long-Term Investments
  • Learning Objectives
  • Introduction
  • Budgeting
    • Cash Budget
    • Project List
    • Alternatives
  • Valuation – IRR vs. Present Value
  • Financing
    • Cost of Capital
    • Debt and Equity
  • Hedging
  • Selected References and Readings
    • Books
    • Articles
• Chapter 9 - Latest Developments

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Chapter 1 - Environmental Forces Affecting Decisions

Learning Objectives

• Explore the evolution of financial decisions.

• Introduce the purposes and limits of a scientific approach to decision making.

• Identify the factors that influence decisions.

• Begin to address how environmental factors can be managed to assist in reaching and implementing the best decisions.

Introduction

Decisions are not made in a vacuum. They are the product of an entire organization. Therefore, everything, from its structure and reward systems to its risk profile, plays an important role in selecting and implementing decisions.

A scientific approach to decision making must address these environmental issues, as well as to provide analytical tools and techniques to assist in the decision process. Chapter 1 begins the overall management of decision making by identifying a number of the structural issues/forces, and the influences they can have. In this way, we can also understand how to better manage their impact.

After working through this chapter go back and ask yourself the following question.

Are there policies in my organization which interfere with arriving at and implementing decisions?

For example, do we extend our depreciation period for assets too long, and thereby keep sunk costs on our books which support the status quo? How about our compensation programs? Do they encourage desired behavior?

Historic Focus of Financial Decisions

The development of financial decision tools and techniques has moved from external financing (1920s) and survival (1930s) to encompass all aspects of a modern organization. The acceptance of new ideas concerning economic theory in the 1950s, probably did more, than any other event, to increase the use of analytical techniques. This evolutionary process has resulted in a significantly expanded role for financial professionals, and the development and use of decision tools to address an ever-widening range of issues.

The period of growth and innovation in the 1920s created a need for additional capital. This resulted in the predictable focus on liquidity and financing. The events of the 1930s directed the study of finance to defensive/survival issues. The corporate collapses and fraudulent actions by some management resulted in government regulations. Thus, significantly increasing the amount of financial data provided externally. This led to increasing the role of financial analysts, as they were better able to compare the financial condition and performance of companies.

Throughout the next two decades, financial analysis looked at issues within companies from an outsider’s viewpoint. The experiences of the 1930s, educated financial professionals to review issues as a third-party lender or investor.

During the 1950s to the early 1960s, budgeting (operating and capital) and cash flow analysis and control became the vehicles which expanded the role of analytical tools. Over the next 1-2 decades, models were developed to value firms from a lender’s and/or investor’s viewpoint (valuation models). This led to calculating the impact of internal decisions on valuations and linking the previous external focus to an internal awareness. The formulation of portfolio and efficient market theories helped to complete the basic “tool box.”

Increases in inflation and an expanding global marketplace (both first became noticeable during the 1970s) drove the next stage of analysis. Fluctuations in inflation resulted in significant increases in interest rates and capital replacement costs, as well as increasing reported profits from the sale of older assets, including inventory. While inflation has been considerably tamer in recent years, the CPI in 1980 was in the mid-teens.

The development of a global marketplace with competition from nontraditional sources has and continues to increase the number and frequency of decisions. In addition, as technology changed at an increasing rate, financial decision making tools had to be adapted to an ever-widening array of situations. The singular focus of the 1920s on financing has been replaced by a growing and changing list of issues ranging from new product development to production and capital programs. The risk of making a poor decision has never been higher, nor has the number of required applications for analytical tools.

Decision Making – Art and Science

Making decisions and taking action is fundamental for all management. Therefore, the outcome of analysis must have a direct implication for management’s actions. Hopefully, it is understood that analytical tools and techniques cannot provide the entire basis for every decision. Managers are constantly called upon to make decisions. At any given instance, the use of an analytical decision tool may result in the same decision an experienced executive would make. However, would you recommend that the future direction of an enterprise be dependent upon a single manager’s, or small group of managers’, experiences? Using the right tool provides the discipline required when faced with complex problems.

The advent and expanded use of computers virtually assures the existence of volumes of data at any time. At times, the sheer volume of data can confuse and delay decisions. Selecting and managing the significant data (evidence) and transforming it into information is key to making good/accurate decisions.

The use of the correct tool can

• Provide a full range of alternatives and their system-wide impact.

• Enable people to debate the merits of a decision, with less emotion and relying more on facts.

The purpose of this program is to provide the tools to significantly increase your long-term potential for making the right decisions. This is the Science part of decision making. The Art comes from interpreting the information and linking it in a unique way, thus providing insights missed by others.

Behavioral Finance

The study of behavioral finance has exploded in the past decade. Behavioral finance attempts to explain how emotions and psychology influence our investment decisions. The famed investor, Warren Buffet, often comments that a successful investor needs a temperament to control urges that lead to trouble in investing. The toughest part of investing is not the intellectual analysis, but the emotional aspects.

Some of the factors which significantly influence our interpretation of data and therefore decision making, fall into the following areas:

• Anchor Effect – Often a disproportionate weight is given to the initial information we received. This is particularly true if it supports our position. The situation can be further complicated if the initial data is shared with others and used to form a “preliminary” view. At this point inertia can take over.

• Overreaction to Random Occurrences – We tend to look for systematic patterns in data. While often underlying patterns do exist, sometimes so-called random events are actually random. Constructing a relationship which does not exist among data points results in our decisions being systematically wrong.

• Overconfidence – It appears that there is a human tendency to be over confident in our abilities and knowledge. Overconfidence will ultimately result in mistakes, often of a significant size.

• Optimism – Another human trait. Optimists underestimate the potential of bad outcomes. A series of modestly, but consistently core optimistic assumptions can, when combined, make a forecast overly and dangerously optimistic.

• Follow the Herd – Following widely known actions by others. This provides a false sense of comfort to people that they are not alone. In addition, if their actions fail, they can make comments such as, “No one knew.” While this behavior may provide comfort, it rarely leads to success or changes to the status quo. Remember, the old saying, “The fool does what the wise man did first.” Based on a study by Money magazine (July 2006), just before the 2000 crash, Wall Street analyst “buy” recommendations outnumbered “sells” by 37 to 1. The same article noted, only 5 out of 40 economists surveyed in June 1990 forecasted that year’s recession.

• Loss Aversion – People feel the pain of loss considerably more than the pleasure of an equal gain. This can be magnified by a misdirected reward system, which rewards even small short-run gains, and severely punishes reasonable and promising, long-term actions which fail. In such a case, innovation will suffer.

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