Chapter 2

Time Series - exponential smoothing

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)

Example of multiple linear regression

For example, the demand might be dependent on how much money is spent on advertising and promotion and also on the selling price charged for the product: -if advertising is increased and the price is lowered, it is likely that demand will go up -if advertising is increased and the price is also increased, the impact on demand is not as obvious. the mathematics of multiple regression can help predict the impact on demand

Forecast Bias

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 sum of forecast error = sum of actual demand = sum of forecast demand In the formula above, if the sum of the forecast error is not zero, there is bias in the forecast. -A negative result shows that actual demand was consistently lessthanthe forecast A positive result shows that actual demand was greaterthanforecast demand A forecast process with bias will eventually create significant problems in the supply chain if left unchecked. Good supply chain planners are aware of these biases. A best practice is to measure for forecast bias _routinely_andthen make corrections accordingly.

Forecast Error - Calculations and Examples

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

Forecasting and Demand Planning

Forecasting and Demand Planning are the key building blocks from which all supply chain planning activities are derived

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)

Qualitative Forecasting - Jury og Executive Opion

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 -generally, the panel conducts a series of forecasting meetings to discuss the forecast until the panel reaches a consensus agreement

Time Series - Linear Trend Forecasting

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

MAD calculation

MAD = sigma( | A - F |)/n where: A= actual demand F = forecast demand n = number of time periods

MAPE formula

MAPE = sigma (|A - F|)/A))/n where: A=actual demand F=forecast demand n=number of time periods

MSE formula

MSE = sigma (A-F)^2 /n where: A=actual demand F=forecast demand n=number of time periods

Measures of Forecasting Accuracy - MAD

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 -absolute errors for a series of time periods are added and then divided by the number of time periods. the resulting value is the MAD measure of forecast inaccuracy -whether the forecast is over or under the actual demand is irrelevant; only the magnitude of the deviation matters in the MAD calculation

Measures of Forecasting Accuracy - MAPE

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 is a useful variant of the MAD calculation because it shows the ratio, or percentage, of the absolute errors to the actual demand for a given number of periods.

Cause and Effect - Multiple Linear Regression

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 - personal insight

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

RSFE Formula

RSFE = sigma et where: et = forecast error for period t

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)

Qualitative Forecasting - Sales Force Estimation

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 the sales function bring special expertise to forecasting because they maintain the closest contact with customers

Forecasting and Demand Planning Challenge

Supply chain organizations routinely rank demand planning immaturity as a major obstacle in meeting their supply chain goals -accurate forecasts are the foundation for profitable business growth -optimal demand planning and forecasting requires comprehensive modeling capabilities plus the flexibility to shift methods as product life cycles progress and market conditions change -a best in class forecasting system provides flexibility for users to weight elements and override parameters in the forecast calculation based on their knowledge and market expertise

synchronizing the supply chain

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

Times Series - weighted moving average forecasting

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

5. all trends will eventually end

(that's why they are called trends) -many factors will affect the pattern youre tying to forecast -it doesn't matter how accurately you predict the trend, in the future the variables will change and the forecast will be wrong

Forecast accuracy - tracking signal

a simple indicator that forecast bias is present determines if the forecast is within acceptable control limits and provides a warning when there are significant unexpected departures from the forecast -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 -think of a smoke detector. it is present to allow for a certain range of smoke, but beyond that range the alarm (tracking signal) goes off and warns you

cyclical variations

a 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)

Evaluate the health of a brand

an understanding of how your target market feels about your company, products and services

moving average

as time moves forward, and each month becomes actual, the oldest month is dropped and the newest month is added

Cause and Effect

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

Qualitative Forecasting Techniques

based on opinion and intuition -generally used when data are limited, unavailable, or not currently relevant. examples: new product, new market segment -forecast depends on skill and experience of forecaster(s) and available information -the five qualitative models used are: 1. personal insight 2. jury of executive opinion 3. Delphi method 4. sales force estimation 5. customer survey

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

good forecasting can

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

advantage of linear trend forecasting

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

Impact of social media on forecasting

companies are using social media insights to drive improvements in forecasting analysis of social sentiment can be used to: -evaluate the health of a brand -improve demand prediction -address a crisis -research the competition

improve demand prediction

companies can use the voice of the customer (VOC) to drive improvements in forecasting and inventory positioning

Companies and their forecasts

companies spend a lot of time and effort trying to figure out how they can best forecast because they will make everything else downstream flow more smoothly

Forecasting and Demand Planning are crucial components of

customer satisfaction

Qualitative Forecasting - Customer Survey

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

advantage to exponential smoothing

exponential smoothing will create a forecast more responsive to trends than pervious methods

disadvantage to exponential smoothing

exponential smoothing will still lag behind trends, especially upwards trends since the smoothing factor would need to be greater than 1.0 to approach an accurate forecast. the smoothing constant is not a given. it has to be determined based on the best judgement of a company's experts

disadvantages of simple moving average forecasting

fails to identify trends or seasonal effects -it will also create shortages when demand is increasing because it lags behind actual demand

example of simple linear regression

for example, the demand might be dependent on how much money is spent on advertising and promotion: the more money spent, the higher the demand -the line that represents this relationship can be used to forecast demand with consideration of future values of the independent variable -in other words, if a company plans on investing more in advertising, it might be necessary to increase the forecast, or vice versa

Forecast

is an estimate of future demand

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)

independent demand

is 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 is forecasted)

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

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

forecasting definition

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

demand

is 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

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

Measures of Forecast Accuracy - MSE

mean squared error (MSE) magnifies the errors by squaring each one before adding them up and dividing by the number of forecast periods -squaring errors effectively makes them absolute since multiplying two negative numbers results in a positive number

Therefore, the goal of the forecasting and demand planning process is to

minimize forecast error

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)

The factors that influence demand

must also be considered when forecasting, e.g., market changes, seasonality, competitive activity, pricing, changing consumer preferences, etc

Time Series - naive forecasting

naive forecasting - sets the demand for the next time period to be exactly the same as the demand in the last time period

error measurement

plays a critical role in tracking forecast accuracy, monitoring for exceptions, and benchmarking the forecasting process -interpretation of these statistics can be tricky, particularly when working with low volume Fata or when trying to assess accuracy across multiple items

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 d -in this situation, stock outs are likely to occur as companies are unable to meet customers' actual demand -a negative RSFE indicates that the forecasts were generally too high, overestimating demand -in this situation, excess inventory and higher carrying costs are likely to occur

advantage to simple moving average forecasting

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

it is generally recommended to use a combination of

quantitative and qualitative techniques

Quantitative forecasting techniques

quantitative forecasting uses mathematical models and historical data to make forecasts -time series -cause and effect

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 chain) based on that forecast will also be bad

disadvantages to linear trend forecasting

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

Collaboration

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

Cause and Effect - Simple Linear regression

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

Times Series - Simple Moving Average Forecasting

simple moving average - uses a calculated average of historical demand during a specified number of the most recent time periods to generate the forecast

Forecast error

since forecasts are almost always inaccurate, companies need to track the forecast against actual demand and measure the size and type of forecast error -the size of the forecast error can be measured in units or percentage

research the competition

social sentiment analysis can help you understand how to position against the competition

address a crisis

social sentiment analysis might reveal a spike in negative posts and provide an early warning to a potential product or service issue

Generally, the farther out into the future you forecast,

the greater the deviation will likely be

Other forecasting models

there are numerous other forecasting models available and in use beyond what is covered in this course. the following are a few of those models: -drift method -holt's linear trend method -holt-winters seasonal method -autoregressive integrated moving average (ARIMA) -box jenkins -x-11 -econometric model -input-output model

Cause and Effect Forecasting

there are two basic cause and effect models: -simple linear regression -multiple linear regression regression uses the historical relationship between an independent and a dependent variable to predict the future values of the dependent variable, i.e., demand

How can the Bullwhip Effect be Alleviated?

there is no single remedy, but there are some actions that supply chain participants can take collectively: -collaboration -synchronizing the supply chain -reducing inventory if the various participants work together to get closer to customers through collaborative planning, forecasting, and replenishment (CPFR), then the bullwhip effect can be greatly reduced

Fundamentals of Forecasting

these fundamentals of forecasting in business can be easily forgotten at times, to the detriment of the quality and accuracy of your forecasts 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 dont use the data regularly, trust it less when forecasting 5. all trends will eventually end 6. its hard to eliminate bias, so most forecasts are biased 7. technology is not the solution to better forecasting

disadvantage of 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

tracking signal formula

tracking signal = RSFE/MAD

The Bullwhip Effect (2/2)

when a small demand ripple in the market place is felt by the retailer at the end of the supply chain, the retailer will then start adjusting their orders to the wholesalers, and the wholesaler in turn will adjust its orders to the distributor, the distributor to the factory, and so on back up to the supply chain when the new demand reaches the material or components supplier at the other end of the supply chain, the magnitude of fluctuation becomes unrecognizable an overreaction due to uncertainty occurs throughout the entire supply chain

variations in quantitative forecasting

when creating a quantitative forecast, data should be evaluated to detect for the following components: patterns in the data -trend variations -random variations -seasonal variations -cyclical variations

The second step is demand planning

where management and other experts within the company review the forecast to ensure that it is aligned with the company's strategy, business policies, and business knowledge, and make adjustments if necessary

The first step is Forecasting,

where the forecast is developed through data analysis and judgement. -organizations must have a formal forecasting process to develop an agreed upon set of numbers that becomes the driver for demand planning

disadvantages of naive forecasting

works for mature products only. any variation in demand will create inventory issues

Advantages of Naive Forecasting

works well for mature products and is very easy to determine

The Bullwhip Effect (1/2)

-because customer demand is not perfectly stable, businesses must forecast demand in order to properly position inventory and other resources. -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

advantages to 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

disadvantages of Delphi method

-experts may introduce some bias -companies must spend time and 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

Disadvantages of jury of executive opinion

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

3. a correct forecast does not prove your forecast method is correct

-it could have been chance -if you only question your methods when there is a large variance in the data, you'll miss all those times your forecast was just lucky

advantages of 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

Advantages to personal insight

-it is the fastest and cheapest forecasting technique -it can provide a good forecast

disadvantages to personal insight

-it relies on one person's judgement and options, 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

advantages of 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 of sales force estimation

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

disadvantages of 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

7. technology is not the solution to better forecasting

-robust forecasting comes from sound logic in your methodology -create an appropriate strategy and then use technology to make it more successful -technology is not the answer; its a tool to help you make the forecast better

1. your forecast is most likely wrong

-the question you should be asking is "How wrong is the forecast?" -forecasting is difficult mainly because people know it is likely to be wrong and nobody likes to be publicly and visibly wrong -you must be willing to recognize and adapt to chaining conditions - dont fall in love with your forecast and ignore evidence that it may be wrong -just be one to the first signs of change and be prepared to react quickly and decisively

2. simple forecast methodologies trump complex ones

-there is danger in complexity. complicated forecast methods often hide key assumptions built into the model -on the other hand, simple forecast methods are easy to understand, analyze and work out why it went wrong

4. if you dont use the data regularly, trust it less when forecasting

-when information is not regularly used, errors often remain undetected -regualr use of data helps identify mistakes and smooths out inconsistencies over time

6. its hard to eliminate bias, so most forecasts are biased

-when you have to make a range of assumptions (which factors to include, how strongly to weight them etc.), its likely that you will be adding some bias to the forecast

Qualitative Forecasting - Delphi Method

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 doen in several rounds until a consensus forecast is achieved

Times Series Forecasting

The main purpose of a time series model is 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

Forecasting Techniques

There are two basic forecasting techniques used in most businesses today 1. Qualitative forecasting which is based on opinion and intuition 2. Quantitative forecasting which uses mathematical models and historical data to make forecasts

Forecasting

There are 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