Supply chain management midterm ch 1 - 3

supply chain

The network of all entities involved in producing, processing, and delivering a product or service, from suppliers to manufacturers to consumers. It encompasses the flow of materials, information, and finances.

supply chain management

the integration of key business processes regarding the flow of materials from raw material suppliers to the final customer

SCOR model

Plan, source, make, deliver, return

plan

The first step in the SCOR model that involves setting objectives, determining resource requirements, and establishing schedules.

source

The second step in the SCOR model focusing on procuring goods and services to meet planned demand.

make

The process in the SCOR model that involves converting raw materials into finished products, including production scheduling and management of the production environment.

deliver

The activities necessary to deliver the finished product to customers, including order management and transportation.

return

The process in the SCOR model that addresses the return of defective or unwanted products from customers back to the manufacturer, including reverse logistics and disposition of returned items.

The bullwhip effect

small fluctuations in demand at one end can cause huge distortions in production

forecast

an estimate of future demand; provides the basis for planning and sound business decisions

quantitative forecasting

uses mathematical models and data

qualitative forecasting

based on opinion and intuition; generally used when data are limited, unavailable, or not currently relevant

jury of executive opinion

group of high-level management come together to develop the forecast

Delphi method

panel of internal + external experts who respond to a series of questionnaires in multiple rounds

sales force composite

company relies on its sales force to estimate future demand

customer surveys

companies survey customers about future purchasing needs, new product ideas, and opinions about new/existing products.

time-series forecasting

based on assumption that the future is an extension of the past; historical data is used to predict future demand; most frequently used among all forecasting models

cause + effect forecasting

assumes that one or more factors (independent variables) predict future demand

trend variations

increasing or decreasing over many years

cyclical variations

wavelike movements that are longer than a year

seasonal variations

peaks and valleys that repeat over a consistent interval (hours, days, weeks, months, seasons, or years)

random variations

due to unexpected or unpredictable events

naive forecasting

the estimate of the next period is equal to the demand in the previous period

simple - moving average forecasting

uses historical data averages to generate a forecast

weighted moving average

based on an n-period weighted moving average

linear trend forecasting

simple linear regression is used to fit a line to a series of data occurring over time

simple regression

one explanatory variable is used, similar to the previous trend model

multiple regression

several explanatory variables are used to make the forecast

forecast error

the difference between the forecast (predicted demand) and the actual demand

MAPE ( mean absolute percentage error)

shows magnitude of error ( always positive)

bias

shows tendency to over or under forecast