SCM 3301 Exam 1 Miller

Supply Chain Management

the active management of supply chain activities and relationships in order to maximize customer value and achieve a sustainable competitive advantage

Supply Chain

A network of manufacturers and service providers that work together to create products or services needed by end users. These manufacturers are linked together through physical flows, information flows, and monetary flows.

Why Study Supply Chain Management

-Every organization must make a product or provide a service that someone values.

-Most organizations function as part of larger supply chains.

-Organizations must carefully manage their operations and supply chains in order to prosper, and indeed, survive.

Operations Management

the planning, scheduling, and control of the activities that transform inputs into finished goods and services

Operations Management- Inputs

Materials, Information, Intangible Needs

Transformation Process

manufacturing operations, service operations

Outputs

tangible goods, fulfilled needs, satisfied customers

PLAN

Activities that include balance demand requirements against resources & communicate these plans to the various participants

BUY/SOURCE

Activities that include identifying, developing, coordinating suppliers & the delivery of incoming goods & services

MAKE

Activities designed to produce an actual good or service

DELIVER

Activities which store and transport goods to a new destination

Return

Activities for returning and processing defective or excess products and materials (often called "Reverse Logistics")

Focal Firm

The organization with which one identifies when discussing Supply Chain Management

Upstream

: Activities or firms positioned earlier in the Supply Chain (prior to the Focal Firm)

Downstream

Activities or firms positioned later in the Supply Chain (after the Focal Firm)

1st Tier Supplier:

direct supply to the Focal Firm

2nd Tier supplier

direct supplier to the 1st Tier Supplier

1st Tier customer

direct customer to the Focal Firm

2nd tier customer

direct customer of the 1st Tier Customer

The three major trends

Increasing Competition and Globalization

Relationship Management

Electronic Commerce

Electronic Commerce

The use of computer and telecommunication technologies to conduct business via electronic transfer of data and documents.

Relationship Management

Organizations must manage the relationships with their upstream suppliers as well as their downstream customers.

Supply Chain Operations Reference (SCOR) Model

A framework developed and supported by the Supply Chain Council that seeks to provide standard descriptions of the processes, relationships, and metrics that define supply chain management.

Structural element

Large capital investments that are difficult to reverse. Includes tangible resources such as buildings, equipment, and computer systems.

Example-New Battery Plant for Tesla

capacity,-Facilities

-Technology

Infrastructural element

The policies, people, decision rules, and organizational structure choices made by a firm. These affect the culture and operation of the business.

Structural Decision Categories

capacity

,-Facilities

-Technology

Infrastructural Decision Categories

-Organization

-Sourcing/purchasing

-Planning and Control

-Business process and Quality management

-Product and Service development

Operations and Supply Chain strategy

A functional strategy that indicates how structural and infrastructural elements with the operations and supply chain areas will be acquired and developed to support the overall business strategy.

What are the primary objectives of the Operations and Supply Chain strategy

A Long-term right mix, being Strategically aligned, and Core competencies

Three Primary Objectives

of Operations and Supply Chain Strategy

Help management choose the long-term right mix of structural and infrastructural elements based on a clear understanding of the performance dimensions valued by customers and the trade-offs involved.

Ensure that the firm's structural and infrastructural choices are strategically aligned with the firm's business strategy.

Support the development of core competencies in the firm's operations and supply chains.

Quality

Performance quality

Conformance quality

Reliability quality

Cost

Labor costs

Material costs

Engineering costs

Quality-related costs

Overhead allocation

Flexibility

-Volume flexibility

-Changeover flexibility

-Mix flexibility

Time

Delivery Speed

Delivery Reliabiliity

Operations and Supply Chain Strategies- Trade Offs

Trade-offs among Performance Dimensions

Difficult to excel at all four performance dimensions

Decisions to emphasize some dimensions at the expense of others. Nearly all operations and supply chain decisions require such trade offs

Conflicts among supply chain strategies

Some common conflicts

Low cost versus high quality

Low cost versus flexibility

Delivery reliability versus flexibility

Conformance quality versus product flexibility

Straddling

seeking to compete on all performance dimensions. Maybe a very risky strategy.

Order Qualifiers

A performance dimension on which customers expect a minimum level of performance.

Order Winners

A performance dimension highly valued by customers that differentiates a company's products and services from its competitors. It tends to drive market share within a targeted market segment.

Designing Strategic Operating Models Framework for Order Winners/Qualifiers

Divide market into segments based on order winners/qualifiers

Cost, Flexibility, Service, Quality, etc.

Identify current market segment or segment(s) to enter

Translate order winners/qualifiers into process requirements

Design processes to meet requirements

Equipment, facility, labor, etc.

Design infrastructure to support processes

Information and accounting systems, human resources, etc.

Stages of Alignment Between Supply Chain and Operations Strategies- Stage 1

Stage 1 - Internally neutral: not linked to business strategy

Stages of Alignment Between Supply Chain and Operations Strategies- Stage 2

Stage 2 - Externally neutral: follow industry best practices

Stages of Alignment Between Supply Chain and Operations Strategies- Stage 3

Stage 3 - Internally supportive: SC strategy aligned with business strategy

Stages of Alignment Between Supply Chain and Operations Strategies- Stage 4

Stage 4 - Externally supportive: develop/exploit SC core competencies

Designing Strategic Operating ModelsFramework for Order Winners/Qualifiers

Divide market into segments based on order winners/qualifiers

Cost, Flexibility, Service, Quality, etc.

Identify current market segment or segment(s) to enter

Translate order winners/qualifiers into process requirements

Design processes to meet requirements

Equipment, facility, labor, etc.

Design infrastructure to support processes

Information and accounting systems, human resources, etc.

Customer Value Index

A measure that uses performance and importance scores for the various performance dimensions for a product or a service to calculate a score that indicates the overall value of an item or service to a customer.

Forecast

An estimate of the future level or some variable

Underlying basis of all business decisions.

Marketing: promotions and sales

Supply Chain: purchasing, capacity, production, inventory

Finance: Cash flow projections, profits

Human Resources: hiring/firing

MIS: user base size, technology development

Forecasting Challenge

Challenge: The ACTUAL future level of the variable is likely to be either higher or lower than the prediction, often by a significant amount

Demand forecast

Considering overall market demand and firm-level demand

Supply forecast

Predict material availability based upon suppliers, trends, risk

Price Forecasts

Forward buying, futures contracts, buying frequency

Economic forecasts

Inflation rates, borrowing rates

Law 1 of Forecasting :

Forecasts are almost always wrong (but still useful)

Law 2 of Forecasting

Short term forecasts tend to be more accurate than longer term forecasts.

Law 3 of Forecasting

Forecasts for Groups (categories) of Products or Services tend to be more accurate than forecasts for specific products or services.

Law 4 of Forecasting

Forecasts are not a substitute for Calculated Values. Only use forecasting when a more reliable method is not available.

Steps in forecasting

1- Determine how the forecast will be used

2-Select the values to forecast

3-Determine the planning time horizon of the forecast

4-Select potential forecasting model

5-Gather historical data from which to forecast

6-Calculate forecasts using forecasting model

7-Evaluate forecast accuracy& choose a forecasting model

8-Make future predictions based upon the forecasting model

Long-range forecast

(Asset Acquisition)

Yearly planning bucket

3-10 years planning horizon

New product planning, facility construction, technology

Medium-range forecast

(Asset Utilization)

Monthly/Quarterly planning bucket

3 months to 2 years planning horizon

Seasonal production, inventory, employment, budgeting

Short-range forecast

(Asset Execution)

Weekly/Monthly planning bucket

1-26 week planning horizon

Job scheduling, worker assignments, inventory stocking

Forecasting approaches

-Qualitative

-Quantitive

Qualitative Methods

Subjective- opinion based

-Used when there is limited quantitive data available

-Used when the relationship between the past and the future is uncertain New products and technology

-Involves intuition, experience e.g. forecasting sales sales of a new product

Quantitative Methods

-Objective- Calculation based

-Used when quantitative historical data is available

Used when the relationship between the past and the future is predictable.

Existing or stable products

Current technology

Involves mathematical techniques

e.g., forecasting sales of products within a stable market

Market Surveys

Structured questionnaires or market research panels

Qualitative Forecasting Methods-Panel consensus forecasting

Experts meet together to develop forecasts

Qualitative Forecasting Methods-Delphi method

Experts develop forecasts separately & then revise

Qualitative Forecasting Methods-Life-cycle analogy method

Modeling growth and decline based upon similar products

Qualitative Forecasting Methods-Build-up forecasts

Market Segment experts develop forecasts that are added together

Level or constant

-Average value is relatively constant over time

-Demand is not changing overtime

Trend

Long-term movement up or down in a time series.

Seasonality

A repeated pattern of spikes or drops in a time series associated with certain times of the year.

Cyclical

Long-term cycles of demand- often over several years

Example-For instance: Product Life Cycles, Presidential Election impact on markets

Randomness

Unpredictable movement from one time period to the next. This component often serves to hide the underlying demand pattern. Random variation is what makes forecasts especially difficult

Example-Measured statistically by "variance" or "standard deviation"

.

Time series model definition

Demand follows a trend and/or pattern over time

Time series model

-Last period or Naive forecast

-Moving average

-Weighted moving average

-Exponential smoothing

-Adjusted Exponential smoothing

-Linear regression

Casual models

Demand is predicted by observing environmental factors such as economic indicators

Casual model

-Linear regression

-Multiple regression

(Forecasting models) Time series

-A set of periodic observations arranged in chronological order

-A "period" is the regular frequency with which measurements are plotted

Assumption

The past is a good predictor of the future

Risk

The underlying demand pattern may change over time

Trade-Off

Forecast Responsiveness

Forecast Stability

Last period or naive approach

-Current demand becomes the next period periods forecast

-This is the simplest time series model

-Very responsive to demand changes in the short-term, but unstable for long-range planning

-Major weakness is that it does not take into consideration historical trends and patterns over time

Moving average model

-Forecast averages the "n" most recent demand values

-Smooths data randomness to illuminate data pattern

-can be adjusted for the type of data being measured

-Does not consider trend or seasonality

-NOTE: n=1 is the SAME as the last period forecast

Weighted Moving Average Method

A form of the moving average that applies varying weights to past observations.

Weights are decimal values between 0 and 1

Weights are listed, the sum of the weights must equal 1

The first weight listed is applied to the most recent historical demand

The last weight listed is applied to the n-th most recent historical demand

Highest weights usually placed on more recent past demand

Cyclical patterns can be modeled by adjusting weight values

Responsiveness determined by weight values.

Exponential Smoothing Model

-Weighted smoothing model with greater weight on most recent data.

-Requires very little stored data (only the past period forecast and demand) and easy to automate.

Adjusted Exponential Smoothing Model

-Exponential Smoothing Calculation that considers trending

-Contains an "Un-adjusted Forecast" component (alpha)

-Contains a "Trend Adjustment" component (beta)

Dependent Variable

The variable that is assumed to be "caused"

Independent Variable

The variable that is assumed to be "causing"

Positive Bias

Positive RSFE indicates that demand exceeded the forecast over time. Forecasts with positive bias will eventually cause stock outs.

Negative Bias

Negative RSFE indicates that demand was less than the forecast over time. Forecasts with negative bias will eventually cause excessive inventory.

Mean Forecast Error (MFE)

Indicative Average forecasts bias

Mean Squared Error (MSE)

Provides a measure of error variance. Penalizes larger errors more than smaller errors.

Mean Absolute Deviation (MAD)

Provides a measure for error disruption. Limited use when comparing forecast accuracy across data with different average demand.

Mean Absolute Percentage Error (MAPE)

Useful for comparing relative forecast accuracy across data with different demand.

Tracking Signal

Provides a measure of the severity of forecast model bias.

Computer-Based forecast packages

Are used to develop, evaluate and change forecasting models as needed.

Collaborative Planning, Forecasting and Replenishment (CPFR)

A set of business processes, backed up by information technology, in which supply chain partners agree to mutual business objectives and measures, develop joint sales and operational plans, and collaborate to generate and update sales forecasts and replenishment plans.

Sales and Operations Planning (S&OP)

A process to develop tactical plans by integrating marketing plans for new and existing products with the management of the supply chain. Brings together all the plans for the business into one integrated set of plans.

Also called Aggregate Planning.

Detailed Planning and Control

Limited ability to adjust capacity, lowest risk. (day to day, hour to hour)

Tactical Planning

Workforce, inventory, subcontracting and logistics decisions. Planning numbers somewhat "aggregated" . Moderate risk. (month to month)