Forecasting - Operation Management - Lecture Slides, Slides of Production and Operations Management

Operation Management are some of points from this lecture. Keywords are: Forecasting, Quality Management, Strategic Decisions, Operations Strategy, Competitiveness, Process Selection, Design of Products, Operational Decisions, Next Number, Types of Forecasts

Typology: Slides

2011/2012

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Forecasting
August 29, Wednesday
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Forecasting

August 29, Wednesday

Introduction

Operations Strategy & Competitiveness

Quality Management Strategic Decisions (some) Design of Products and Services

Process Selection and Design

Capacity and Facility Decisions

Tactical & Operational Decisions

Course Structure

Forecasting

Forecasting

  • Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7,

b) 2.5, 4.5, 6.5, 8.5, 10.5,

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5,

Outline

  • What is forecasting?
  • Types of forecasts
  • Time-Series forecasting
    • NaĆÆve
    • Moving Average
    • Exponential Smoothing
    • Regression
  • Good forecasts

In general, forecasts are almost always wrong. So,

Why do we need to forecast?

Throughout the day we forecast very different things such as weather, traffic, stock market, state of our company from different perspectives.

Virtually every business attempt is based on forecasting. Not all of them are derived from sophisticated methods. However, ā€œBest" educated guesses about future are more valuable for purpose of Planning than no forecasts and hence no planning.

Departments throughout the organization depend on forecasts to formulate and execute their plans.

Finance needs forecasts to project cash flows and capital requirements.

Human resources need forecasts to anticipate hiring needs.

Production needs forecasts to plan production levels, workforce, material requirements, inventories, etc.

Importance of Forecasting in OM

Types of Forecasts by Time

Horizon

  • Short-range forecast
    • Usually < 3 months
      • Job scheduling, worker assignments
  • Medium-range forecast
    • 3 months to 2 years
      • Sales/production planning
  • Long-range forecast
    • 2 years - New product planning

Design of system

Detailed use of system

Quantitative methods

Qualitative Methods docsity.com

Forecasting During the Life

Cycle

Introduction Growth Maturity Decline

Sales

Time

Quantitative models

**- Time series analysis

  • Regression analysis**

Qualitative models

**- Executive judgment

  • Market research**
  • Survey of sales force
  • Delphi method

Briefly, the qualitative methods are:

Executive Judgment: Opinion of a group of high level experts or managers is pooled

Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast.

Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services.

Qualitative Methods

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Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization.

Typically, the procedure consists of the following steps: Each expert in the group makes his/her own forecasts in form of statements  The coordinator collects all group statements and summarizes them  The coordinator provides this summary and gives another set of questions to each group member including feedback as to the input of other experts.  The above steps are repeated until a consensus is reached.

.

Qualitative Methods

Quantitative Forecasting Methods

Quantitative Forecasting

Regression Models

**2. Moving

  1. Naive Average**

Time Series Models

3. Exponential Smoothing a) simple b) weighted

a) level b) trend c) seasonality

Time Series Models

  • Try to predict the future based on past

data

  • Assume that factors influencing the past will continue to influence the future

Product Demand over Time

Year 1 Year 2 Year 3 Year 4

Demand for product or service

Product Demand over Time

Year 1 Year 2 Year 3 Year 4

Demand for product or service

Trend component

Actual demand line

Seasonal peaks

Random variation

Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e^ Now let’s look at some time series approaches to forecasting… docsity.com