Intro to Supply Chain Chapter 2

Advantages to Sales Force Estimation

-No additional cost to collect data because internal sales people are used -More reliable forecast as it is based on the opinions of salespersons in direct contact with the customer.

Disadvantages to Sales Force Estimation

-Not ideal for long term forecasting -Salespersons may introduce some bias. -Salespersons may not be aware of the economic environment.

Disadvantages to Customer Survey

-Poorly formed questions may lead to unreliable information. -Customers do not always answer the questionnaire. -It is time consuming and costly to survey a large population.

Personal Insight

-The forecast is based on the insight of the most experienced, most knowledgeable, or most senior person available -Sometimes, this approach is the only option, but methods that include more people are generally more reliable.

Advantages of Personal Insight

-fastest and cheapest forecasting technique -can provide a good forecast

The five qualitative models used for qualitative forecasting

1) Seasonal Insight 2) Jury of Executive Opinion 3) Delphi Method 4) Sales Force Estimation 5) Customer Survey

Fundamentals of Forecasting

1) Your forecast is most likely wrong 2) Simple forecast methodologies trump complex ones 3) A correct forecast does not prove your forecast method is correct 4) If you don't use the data regularly, trust it less when forecasting 5) All trends will eventually end 6) It's hard to eliminate bias, so most forecasts are biased 7) Technology is not the solution to better forecasting

two basic forecasting techniques used in most businesses today

1) qualitative 2) quantitative

Two types of quantitative forecasting

1) time series 2) cause and effect

Advantage to Exponential Smoothing

Exponential smoothing will create a forecast more responsive to trends than previous methods.

Disadvantage to Exponential Smoothing

Exponential smoothing will still lag behind trends, especially upward trends since the smoothing factor would need to be greater than 1.0 to approach an accurate forecast.

Disadvantage to Weighted Moving Average

Though better than a simple moving average, this technique will still lag behind actual demand to some degree. -The challenging part of using a weighted moving average is deciding on the weight for each time period.

Reducing Inventory

Through the use of just in time (JIT), vendor managed inventory (VMI), and quick response (QR), all of which will be discussed later in this course.

forecast

an estimate of future demand

The two key building blocks from which all supply chain activities are derived

forecasting and demand planning

Running Sum of Forecast Errors (RSFE)

provides a measure of forecast bias. RSFE indicates the tendency of a forecast to be consistently higher or lower than actual demand. -A positive RSFE indicates that the forecasts were generally too low, underestimating the demand. -A negative RSFE indicates that the forecasts were generally too high, overestimating demand. RSFE = ∑ et Where et = forecast error for period t

Disadvantage to Linear Trend Forecasting

seasonal and cyclical variations are softened, making this method more useful for annual forecasts than for monthly forecasts.

Weighted Moving Average

similar to a simple moving average except that not all historical time periods are valued equally

Generally, the farther out into the future you forecast

the greater the deviation will likely be

quantitative forecasting

uses mathematical models and historical data to make forecasts

Regression

uses the historical relationship between an independent and a dependent variable to predict the future values of the dependent variable, i.e., demand

Tracking Signal

-A simple indicator that forecast bias is present. -If the tracking signal falls outside the pre-set control limits, there is a bias problem with the forecasting method and an evaluation of the way forecasts are generated is warranted. Tracking signal = RSFE/MAD

Sales Force Estimation

-Basically the same as the Jury of Executive Opinion except that it is performed specifically with a group of sales people. -Individuals working in the sales function bring special expertise to forecasting because they maintain the closest contact with customers

Delphi Method

-Basically the same as the Jury of Executive Opinion except that the input of each of the participants is collected separately so that people are not influenced by one another. -This is done in several rounds until a consensus forecast is achieved.

CPFR can significantly reduce the Bullwhip Effect and provide a plethora of benefits including:

-Better customer service -lower inventory costs -improved quality -reduced cycle time -better production methods

Customer Survey

-Customers are directly approached and asked to give their opinions about the particular product. -Customer surveys can be done in person (e.g., one-on-one, focus group), over the phone, by mail, email, or online.

Advantages of Jury of Executive Opinion

-Decisions are enriched by the experience of competent experts. -Companies don't have to spend time and resources collecting data by survey.

Advantages of Delphi Method

-Decisions are enriched by the experience of competent experts. -Decisions are not likely a product of groupthink . -Very useful for new products

Disadvantage of Jury of Executive Opinion

-Experts may introduce some bias -Experts may become biased by their colleagues or a strongly opinionated leader.

Disadvantages of Delphi Method

-Experts may introduce some bias. -Companies must spend time & resources collecting data by survey. -If external experts are used there is a risk of loss of confidential information. -The Delphi Method can be time-consuming and is therefore best for long-term forecasts.

Advantages to Customer Survey

-It is a direct method of assessing information from the primary sources -Simple to administer and comprehend. -It does not introduce any bias or value judgment particularly in the census method if the questions are constructed carefully.

Disadvantaged of Personal Insight

-It relies on one person's judgement and opinions, but also on their prejudices and ignorance. -The major weakness is unreliability ; someone who is familiar with the situation often provides a worse forecast than someone who knows nothing.

Simple Linear Regression

-attempts to model the relationship between a single independent variable and a dependent variable (demand) by fitting a linear equation to the observed data. -The equation describes the relationship between the independent variable and dependent variable as a straight line.

Multiple Linear Regression

-attempts to model the relationship between two or more independent variables and a dependent variable (demand) by fitting a linear equation to the observed data -Depending on the data and the number of independent variables, the mathematics involved can be complex.

qualitative forecasting

-based on opinion and intuition -generally used when data are limited, unavailable, or not currently relevant -forecast depends on skill and experience of forecaster(s) and available information

Time Series

-based on the assumption that the future is an extension of the past. Historical data is used to predict future demand . -The most frequently used among all the forecasting models.

Main purpose of a time series model

-to collect and study the past data of a given time series in order to generate probable future values for the series. -In other words, forecasts for future demand rely on understanding past demand -Accordingly, time series forecasting can be characterized as the act of predicting the future by understanding the past

The two basic cause and effect models

1) simple linear regression 2) multiple linear regression

Two important considerations about a forecast

1) statistically speaking, the forecast will be inaccurate, and although it may be inaccurate, it is still useful 2) The forecast is the basis for most "downstream" supply chain planning decisions, so it is critical to be as accurate as possible

When creating a quantitative forecast, data should be evaluated to detect for the following components

1) trend variations 2) random variations 3) seasonal variations 4) cyclical variations

Advantage and disadvantage to Naïve Forecasting

Advantage: Works well for mature products and is very easy to determine. Disadvantage: Works for mature products only. Any variations in demand will create inventory issues.

Disadvantage to Simple Moving Average

Fails to identify trends or seasonal effects. It will also create shortages when demand is increasing because it lags behind actual demand

safety stock

Forecasts are based on statistics, and they are rarely 100% accurate, therefore, companies often carry an inventory buffer called safety stock -moving from the end-consumer(s) backward across the supply chain to raw material supplier(s), each supply chain participant is farther removed from the end demand and may have less information about what is happening with demand, creating a greater need to maintain higher levels of safety stock. -In the absence of any other information or visibility, individual supply chain participants are second-guessing what is happening with ordering patterns, and potentially over-reacting, creating the bullwhip effect. -In periods of rising demand, down-stream participants increase orders. -In periods of falling demand, orders decrease or stop, and inventory accumulates.

Random Variations

Instability in the data caused by random occurrences. These random changes are generally very short-term, and can be caused by unexpected or unpredictable events such as weather emergencies, natural disasters, etc. (e.g., hurricane = wood for roof repair, tree clean up, water damage)

Advantage to Weighted Moving Average

More accurate than a simple moving average if actual demand is increasing or decreasing.

Trend Variations

Movement of a variable over time. Might be more easily observed by plotting actual demand on a graph over time to see whether there is an increase or decrease. (e.g., laptops, cell phones, fashion products, toys)

Jury of Executive Opinion

People who know the most about the product and the marketplace would likely form a jury (i.e., management panel) to discuss and determine the forecast. -The panel conducts a series of forecasting meetings to discuss the forecast until the panel reaches a consensus agreement

Advantage to Simple Moving Average

Provides a very consistent demand over long periods of time and smooths out random variations.

Seasonal Variations

Repeating pattern of demand from year to year, or over some other time interval, with some periods of considerably higher demand than others (e.g., holiday shopping, restaurant customers, swim suits sales by region, building construction slowing in winter by region)

Naïve Forecasting

Sets the demand for the next time period to be exactly the same as the demand in the last

Collaboration

Sharing information through the use of electronic data interchange (EDI), point of sale (POS) data, and web-based systems can facilitate collaboration.

Synchronizing the supply chain

Supply chain participants coordinate planning and inventory management to minimize the need for reactionary corrections.

Simple Moving Average

Uses a calculated average of historical demand during a specified number of the most recent time periods to generate the forecast.

Cyclical Variations

Wavelike pattern that can extend over multiple years, and therefore, cannot be easily predicted. (e.g., business cycle, China growth, GDP, bull or bear markets)

Collaborative Planning, Forecasting, and Replenishment (CPFR)

a business practice that combines the intelligence of multiple trading partners who share their plans, forecasts, and delivery schedules with one another in an effort to ensure a smooth flow of goods and services across a supply chain

Cause and Effect

assumes that one or more factors (independent variables) predict future demand (e.g., seasonality in retail markets)

Bad forecasting can

be the root cause for creating just the opposite. There is a familiar adage that applies to forecasting: "garbage in = garbage out." If a forecast is bad, everything else (i.e., the supply plan) based on that forecast will also be bad.

Good forecasting can

benefit a company, by facilitating more effective planning, which can lead to reduced inventories, reduced costs, reduced stockouts, and improved customer service,

Advantage to Linear Trend Forecasting

can provide an accurate forecast into the future even if there is random variation.

Independent Demand

demand for an item that is unrelated to the demand for other items, such as a finished product, a spare part, or a service part. Demand for these items are Forecasted

Forecast Bias

is a consistent deviation from the mean in one direction; either high or low. -In other words, bias exists when the demand is consistently over- or under-forecast. A good forecast is not biased.

Exponential Smoothing

is a more sophisticated version of the weighted moving average. Requires 3 basic elements: last period's forecast, last period's actual demand, and a smoothing factor, which is a number greater than 0 and less than 1 (used as a weighting percentage).

Dependent Demand

is demand for an item that is directly related to other items or finished products, such as a component or material used in making a finished product. Demand for these items is Calculated.

Linear Trend Forecasting

is imposing a best fit line across the demand data of an entire time series. Used as the basis for forecasting future values by extending the line past the existing data and out into the future while maintaining the slope of the line.

Since we know that only thing that we can say for sure about a forecast...

is that the forecast will likely be wrong, the best that we can hope for is to be as consistently accurate as possible.

Mean Squared Error (MSE)

magnifies the errors by squaring each one before adding them up and dividing by the number of forecast periods. MSE = ∑ (A-F) ² / n Where A =Actual demand, F = forecast demand, n = number of time periods

factors that influence demand

market changes, seasonality, competitive activity, pricing, changing consumer preferences, etc.

Mean Absolute Percent Error (MAPE)

measures the size of the error in percentage terms. It is calculated as the average of the unsigned percentage error. -Many companies use the MAPE as it is easier for most people to understand forecast error and forecast accuracy in percentage terms rather than in actual units. MAPE = ∑ ((|A - F|)/ A) / n Where A =Actual demand, F = forecast demand, n = number of time periods

Mean Absolute Deviation (MAD)

measures the size of the forecast error in units. It is calculated as the average of the unsigned, i.e., absolute, errors over a specified period of time. MAD = ∑(|A - F|) / n Where A = actual Demand, F = forecast demand, n = number of time periods

the goal of the forecasting and demand planning process is to

minimize forecast error

Error measurement

plays a critical role in tracking forecast accuracy, monitoring for exceptions, and benchmarking the forecasting process.

forecasting

the business function that estimates future demand for products so that they can be purchased or manufactured in appropriate quantities in advance of need

Forecast Error

the difference between the actual demand and the forecast demand. The error can be quantified as an absolute value or as a percentage.

demand

the need for a particular product or component. The demand could come from various sources such as a customer order, a forecast, the manufacturing of another product, etc.

demand planning

the process of combining statistical forecasting techniques and judgment to construct demand estimates for products or services

The real value of CPFR comes from

the sharing of forecasts among firms, rather than firms relying on sophisticated algorithms and forecasting models to estimate demand.