Quality and Productivity Management

Value of Output
= _________________

Value of Input

Value of the output must exceed the value of the input

Physical or Chemical – Manufacturing, chemical processing, oil refining, etc.
Locational – Airline companies, trucking companies, package delivery services.
Storage – Warehousing operations, banks.
Exchange – Wholesale and retail operations.
Informational – T.V. news departments, newspapers, computer information services
Educational – Schools, colleges, universities.
Attitudinal – Entertainment industry, movie companies, theme parks.
Physiological – Hospitals and healthcare institutions.

Materials
Raw Materials
Purchased Parts
Supplies
Energy
People
Workers
Technicians
Supervisors
Managers
Maintenance
Custodial
Equipment
Land
Buildings
Machines
Tools
Office Equipment
Computers

Products
Services

Physical
Locational
Storage
Exchange
Informational
Educational
Attitudinal
Physiological

Nature of output
Customer contact
Storability of output
Transportability of output
Number of outlets
Location of outlets
Size of outlets
Response time
Use of capital and labor
Measurement of quality
Measurement of productivity

- Broad in scope
- Long-term in nature
- All encompasing

- Narrow in scope
- Short-term in nature
- Concerning a small group of issues

High Volume/Low Variety <---------->Low Volume/High Variety

Line Processes are typified by systems that have discrete units moving through the processing stages. In manufacturing we think of assembly line operations, where examples would include such things as automobiles, pencils, toasters, etc. Even some non-manufacturing systems could have characteristics of a line process (for example, a cafeteria line in a high school).

Continuous Processes differ in that we do not have discrete, individually identifiable items moving through the processing stages. Instead, we tend to have some amorphous matter moving through the processing. Examples here would include an oil refinery, a soft drink bottler, a brewery, or a chemical processing plant.

Job-Shop Processes are typified by systems that handle custom work that requires relatively small amounts of resources and time. Examples cited in class include machine shops that custom manufacture metal parts, fabricators of advertising signs and neon signs, and print shops. In the realm of non-manufacturing, walk-in emergency clinics and insurance claims offices exhibit job shop tendencies.

Project Processes are typified by systems that handle custom work that requires large (or even massive!) amounts of resources and time. There are many examples in the area of construction, such as building bridges, apartment complexes, shopping centers, etc. A non-manufacturing example would be a team of information systems consultants engaged in the design and implementation of a new management information system for a hospital.

Batch Processes are typified by systems that have a moderate number of different outputs and moderate demand for each. These systems will produce a small run of a particular item (on a repetitive basis), then switch to a small run of another item, and so on. Examples include furniture manufacturers and book publishers.

Executive Opinion
Market Research
Delphi Method

These types of forecasting methods are based on judgments or opinions, and are subjective in nature. They do not rely on any mathematical computations
- Executive
- Market research
- Delphi

These types of forecasting methods are based on quantitative models, and are objective in nature. They rely heavily on mathematical computations
-Time Series models
-Causal Models

Naïve
Simple Mean
Simple Moving Avg
Weighted Moving Average
Exponential Smoothing
Trend Adjusted Exponential Smoothing
Seasonal Indexes
Linear Trend Line

Level or horizontal: Data are relatively constant over time, with no growth or decline.

Trend: Data exhibit a steady growth or decline over time.

Seasonality: Data exhibit upward and downward swings in a short to intermediate time frame (most notably during a year).

Cycles: Data exhibit upward and downward swings in over a very long time frame.

Random: Erratic and unpredictable variation in the data over time

Forecasting methods that react very strongly (or quickly) to demand changes

i.e:- If demand has been showing a steady pattern of increase (or decrease), then more responsiveness is desirable, for we would like to react quickly to those demand increases (or decreases) when we make our next forecast

Forecasting methods that do not react quickly to demand changes

if demand has been fluctuating upward and downward, then more stability is desirable, for we do not want to “over react” to those up and down fluctuations in demand

Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data.

Causal models assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those associations

Uses last period’s actual value as a forecast.

A weighted average procedure with weights declining exponentially as data become older