Management Planning: Nature, Process, Responsibility

• Planning is the first function of management.

The Nature of Planning

• A plan serves as a blueprint or framework that describes how an organization aims to achieve its objectives.
• Planning involves the process of developing comprehensive plans.
• Planning is determining the specific path an organization intends to follow among various options to realize its goals.

The Planning Process Stages

• Setting goals and objectives.
• Developing strategic plans to achieve the primary goals of an organization
• Creating tactical plans, which involves creating shorter-term plans that allow you to achieve strategic goals

Responsibility for Planning

• Planning is the responsibility of top-level management.
• The board of directors establishes the broad goals and strategies of the firm.
• Planning staff offers support to line managers with expertise and resources to aid the planning process.
• Planning staff coordinates and integrates the planning activities across different levels.
• Middle managers work together to support strategic planning
• Middle managers perform tactical planning
• Middle managers develop and implement planning activities within their departments individually.

Kinds of Plans

• Strategic plans are developed by top managers to guide the general direction of the organization.
• Tactical plans focus on implementing the strategic plans that have already been developed.
• Operational plans have the narrowest focus and the shortest time frame.

Planning Time Frames

• Long-range planning spans a period of 5-10 years.
• Intermediate planning generally covers a time frame of one to five years.
• Short-range planning covers time periods of one year or less.

Contingency Planning

• Contingency planning involves identifying alternative courses of action that the organization might take if various conditions arise.
• Managers engaging in contingency planning must specify contingency events (possible changes/outcomes) and analyze how they affect the initial plan.
• Steps for contingency planning:
  • Develop initial plans and specify contingency events
  • Assess relevant contingencies
  • Adopt contingency plan B, continue with initial plans, or adopt contingency plan C depending on the assessment

Tools and Techniques for Planning

• Forecasting.
• Linear programming.
• Break-even analysis.

Forecasting

• A forecast is a statement of what is likely to happen in the future.
• It is a future estimate to allow organizations and individuals to plan ahead.
• Forecasts make use of historical data.
• Types of Forecasting:
• Demand/Sales Forecasting: For predicting sales of products
• Technological Forecasting: For predicting the rate of technological progress or change
• Economic Forecasting: For predicting e.g. inflation rates & money supplies

Forecasting Time Horizon

• Short-range forecasts have a time span of up to 1 year, but is generally less than 3 months, used for purchasing & job scheduling.
• Intermediate forecasts generally span from 3 months up to 3 years and is useful for sales planning, production planning, and budgeting.
• Long-range forecasts are generally 3 years or more and are useful for planning new products, facility location, or expansion.

Forecasting Approaches

• Quantitative and Qualitative
• Quantitative Forecasting: use mathematical models, historical data/causal variables
• Qualitative Forecasting: incorporates the subjective judgements, intuition, experience and expert knowledge in reaching a forecast

Qualitative Forecasting Techniques

• Jury of Executive Opinion: Uses the opinions of a small group of high-level managers, often in combination with statistical models.
• Sales Force Composite: Each salesperson estimates future sales in the region; forecasts are combined at the district and national levels.
• Delphi Method: Uses opinions of experts who may be located at different places (iterative process).
• Consumer Market Surveys: Solicits input from customers or potential customers regarding future purchasing plans.

Quantitative Forecasting Techniques

• Times Series: Naïve approach, moving averages, exponential smoothing, and trend projection.
• Causal Model: Linear Regression considers factors influencing the quantity being forecast.
• The future is a function of the past; historical data is therefore used, particularly for time series techniques.

Decomposition of Time Series

• Depends on a series of spaced data points and historical data such as weekly or monthly data.
• Any other variable not showing up in past data is ignored.
• Analyzing time series breaks down past data into components and projects them into the future.
• Times series has components: Trend (T), Seasonality (S), Cycles (C), and Random variations (R).
• Trend (T) gradual upward or downward movement of the data over time.
• Seasonality (S) a data cycle that repeats itself after a period.
• Cycles (C) patterns in the data that occur every several years.
• Random variations (R) blips in the data caused by chance and unusual
• An example of random variation is caused by unusual situations

Quantitative Forecasting Techniques Cont.

• Naïve approach assumes that demand in the next period is equal to demand in the most recent period.
• Moving average assumes that demand in the next period is the average demand over a specified number of periods and this is useful if market demand will be fairly stable over time.
  • Moving Average formula: Σ Demand in previous n period; Where n is the number of periods.
• Weighted moving average weights can emphasize more recent values when there is a trend or pattern, making the technique more responsive to changes. Wighted Moving Average formula: ∑ (weight for period n) x (Demand in previous n months) / Sum of weights
• Exponential smoothing: A type of moving average that requires little historical data.
  • The new forecast value is calculated by the formulation: New forecast = last period's forecast + a (last period's actual demand – last period's forecast)
  • Where:
  • Ft = Ft-1 + a (At-1 -Ft-1)
  • a is a weight/smoothing constant (values between 0 and 1 inclusive)
• Trend Projection fits a line to historical data and projects it into the future for medium-to-long-range forecasts.
  • Example formula: ŷ =a + bx
    • Where:
    • ŷ is the value to be computed.
    • a is the y-axis intercept
    • b is the slope of the regression line or rate of change in y
    • X is the independent variable, which is time in this case.

Causal Forecasting Methods

• Forecasts several variables related to the variable being predicted (dependent variable).
• Once variables are determined, a statistical model is developed to forecast the variable of interest.
• Methods are more powerful than time series methods that only use historical values.
• The most common quantitative causal forecasting model is linear regression analysis.
• The same mathematical model used in the least squares method of trend projection can perform linear regression analysis with the formula ŷ =a + bx
• The difference is that x is no longer only time but the independent variable.

Forecast Errors formulas

• Cumulative sum of Forecast Errors equation:
• Σ(i=1 to n) ei
• Mean Square Error equation: MSE = Σ(i=1 to n) ei2 / n
• Mean Absolute Deviation equation: MAD = (Σ(i=1 to n) |ei|) / n
• Mean Absolute Percentage Error equation: MAPE = ( Σ(i=1 to n) ei / Dt) / n * 100
• Tracking Signal equation: TS= Σ(i=1 to n) ei / MAD
• Mean Error euqation: ME = Σ(i=1 to n) ei / n

Break-Even Analaysis

• Used to find the point, in dollars and units, at which costs equal to revenue.
• Estimates fixed costs that continue even if no units are produced.
• Estimates variable costs which change based on the quantity of units produced.
• Estimates money to be realized from the sale of goods.
• Total Revenue (TR) is equivalent to Total Costs (TC).
• Formulas:
  • TR= TC
  • TC = F+Vx
  • Px = F+Vx
  • BEP (x) = F/ (P-V)
  • BEP ($) = F/ (1-(V/P))

Linear Programming

• LP (Linear Programming) is a planning and decision-making tool used to help make decisions about the trade-offs an organization makes for optimized benefits.
• Involves maximizing or minimizing some quantity; a known objective function.
• Restrictions/constraints which limit the degree the company can pursue its objective.
• Presents alternative courses of action to choose from, that must be expressed in terms of linear equations or inequalities.

Linear Programming Problems

• The graphical approach uses the Iso-Profit solution method.
• The corner Point Solution method offers a simplified process that involves looking at the profit from every corner point of the feasible solution
• Assumption: The optimal solution to any problem (i.e. the X1 & X2) will lie at the corner point or extreme point of the feasible solution.

Solution

• Walkmans =X₁ and Watch-TV = X₂
  • Profit = 7X₁ + 5X₂ → The objective function
    • 1st constraints = 240 hrs for electronics department
    • 2nd constraints =100 hrs for assembly department
• Production constraint equations: 4X₁ + 3X₂ ≤ 240 Assembly constraint equation: 2X₁ + X₂ ≤ 100

Feasible Region

• Do plotting for both equations to find the area of the feasible region.
• The intersection of the two constraints lines gives you're the points of the option profit
• Equations:
  • 4X₁ + 3X₂ = 240
  • 2X₁ + X₂ = 100 and (can multiply equation by -2 and add the two equations)
• Can set values of X₁ and X₂ into the objective function to decide on what gets the business the optimal profit.

Minimization Problem

• Cohen Chemicals, Inc., produces two types if photo-developing fluids.
• The first, a black-and-while picture chemical, costs Cohen $ 2500 per ton to produce.
• The second, a colour photo chemical, costs $ 3000 per ton.
• Cohen is asked to produce an 60 tons of his chemicals.

Solition

• X₁ = Black-and-white picture chemical and X2 = Colour photo chemical
• It cost $ 2500 to produce a black-and-white picture chemical and to produce a colour photo chemical costs $ 3000
• 2500 X₁+3000 X2 = minimum cost ……….Objective function. Constraints or requirements include the following:
  • X₁ ≥ 30
  • X2 ≥ 20
  • X1+X2 ≥ 60*
• Set the inequalities to equalities.
• Example:
  • Set X₁ = 0; X2 = 0 into equation *: When X₁ = 0, X = 60, When X2 = 0, X₁ = 60

Explore management planning: its nature as a blueprint for organizational goal achievement, the stages involved in the planning process, and the responsibilities of different levels of management. Learn about strategic and tactical plans and the role of planning staff.