Supply Chain Managment Exam #3

Forecast

• A statement about the future value of a variable of interest, such as demand. Equivalently, a prediction about the future.

Four Features of Forecasting

1. Assumption

2. Actual results differ

3. Forecast accuracy decreases

4. Forecast of groups

Forecasting assumption

Casual systems existed in the past will continue to exist in the future

Actual forecast results vary

• Real results can vary from the predicted values

Forecasting accuracy decreases

• as time covered by the forecast increases the accuracy of the forecast decreases

Forecasting for groups

• Forecasting groups of items tends to be more accurate than forecasts for individual items

Casual System

• competitors to the company product

• wheather conditions

• other factors sffecting demand

Grouping items: concepts of aggregation

• to identify a meaningful measure of output

• Examples: Steel producer, paint producer, auto manufactures, in service orgs, health care facillities

Elemants of a good forecast

• timely

• reliable

• accurate

• meaningful

• written

• easy to use

• cost effective

Steps in the forecasting process

Step 1: determine the purpose of forecast

Step 2: establish a time horizon

Step 3: select a forecast technique

Step 4: gather and analyze data

Step 5: prepare the forecast

Step 6: monitor the forecast

Types of Forecasts

Qualitative (judgmental)

Quantitative ( time series analysis, casual relationship)

Compnents of Demand

• trend

• average demand for period of time

• seasonal elemant

• cyclical elemant

• random variation

• autocorrelation

Trend Variation

• long term movement in data

Seasonality variation

• short-term regular variation in data

Cycles variation

• Wavelike variation over long-term

Irregular variations

• Caused by unusual circumstances

Random variations

• caused by chance

The forecast for any period is equal to

The previous period’s actual value

Simple Moving average Formula

Assumes an average is a good estimator of future behavior

Moving Average Pros and Cons

Pros: easy to compute and easy to understand

Cons: all values in the average are weighted equally

Weighted Moving Average

• Assigns more weight to recent observed values

• More responsive to changes

• Selection of weights is arbitrary, but weights must add to one. The values for the weights are always given

weights place more emphasis on…

recent data

Exponential Smoothing Average

The most recent observations might have the highest predictave value so we should give more weight tp the more recent time periods

Smoothing Constant can be

greater than or equal to zero and less than or equal to one

Smaller smoothing constant results in

smoother line

Techniques for seasonallity

• Regularly repeating upward and downward monements in time series values

• examples: rush hour traffic twice a day

Additive to seasonality

• seasonality is expressed as a quanity which is added or subtracted from the average to incorporate seasonality

Multiplicative Seasonality

• expressed as a percentage of the average amount which is used to multiply the value of a series

Seasonal Percentage can aslo be called

• seasonal relatives

• seasonal indexes

Additive model equals

Demand = Trend + Seasonality

Multiplicative model equals

Demand = trend x seasonality

Seasonality relatives/Index equation

Demand/Average

Mean Absolute Deviation

• Used to determine forecast error

• larger MAD implies a less accurate model

Ideal MAD equals

zero meaning no forecasting error

1 MAD equals

0.8 SD

1 SD equals

1.25 MAD

Mean Squared Error

Ideal MSE is zero

Monitoring the forecast

• find the standard deviations use sqrt of MSE

Inventory Turns

COGS/ Cost of Inventory

Inventory

• Stock of any item or resource used in an organization and can indlude ( raw materials, component parts, supplies)

Inventory System

Set of polocies and controls the monitor levels of inventory and determine, what inventory level should be maintaines, when stock should replenish, how large orders should be

Purposes of Inventory

Maintain idependence of operations, safeguard from delivery times, take advantage of purchase order size, hedge against price increases, allow flexibillity in scheduling, meet variation in demand

Independent Demand

Demand for final end product or demand not related to other items

Dependent Demand

Derived demand items for component parts, subassemblies, and raw materials

Objective of Inventory control

• achieve satisfactory levels of customer service while keeping inventory costs within reasonable boundaries

Understocking

• dissatisfied customers, lost sales

Overstocking

• tied up funds

Two fundemental parts of inventory control

• timing of the order

• size of the order

Inventory Counting Systems

• periodic review system

• continous review system

Cost Information

Holding carrying cost: physically holding items in storage

interest

taxes

breakage

oppurtunity costs

Ordering Cost Examples

• cost of ordering inventory

• typing invoices

• inspecting goods

• moving to storage

• cost of machine setup

Shortage Costs

• Oppurtunity cost for not making a sale

shortage cost effects

• loss of customer goodwill

• lateness charges

• cost of loss production

Classification System

A-B-C: classifying inventory according to some measure of importance and allocating controls

Asuumptions of Basic Economic order quanity Model

• Only one product is involved

• Annual demand requirments are known

• Lead time doesnt vary

• no quanity discounts

• single delivery

Economic Order Quanity

• The order size that minimizes total cost

Multi-Period Models

• Fixed Order quanity Model

• Fixeed Time Period model

Fixed-order quanity models

• falls below a reorder point

• continous review system

• event-triggered

• an order of a fixed quanity q is placed every time the inventory turns

Fixed time period models

• periodic review system

• time triggered ( inventory counted at particular time)

• A quanity that depends on the current inventory level is ordered