Chapter 11 – Demand Management & Forecasting – Flashcards

Medium & Long term forecasts that are used to make decisions related to design and plans for meeting demand. How demand will be met strategically.

Short-term (few weeks or month) forecasts used as input for making day-to-day decisions related to meeting demand

1. Perfect forecast is virtually impossible! 2. Rather than searching for the perfect forecast, it is far more important to establish the practice of continual review of forecast and to learn to live with inaccurate forecast 3. When forecasting, a good strategy is to use 2 or 3 methods and look a them for the commonsense view.

1. Qualitative - use managerial judgement, used when situation is vague and little data exists (new products, new technologies) 2. Quantitative - rely on mathematical methods, used when situation is "stable" and historical data exists (existing products, current technology)

The purposes is to coordinate and control all sources of demand so the supply chain can be run efficiently and the product delivered on time.

1. Dependent demand - demand for products or services caused by the demand for other products or services. Not much the firm can do, it must be met. 2. Independent demand - demand that cannot be directly derived from the demand for other products. Firm can: a) Take an active role to influence demand - apply pressure on your sales force b) Take a passive role to influence demand - if a firm is running on a full capacity, it may not want to do anything about demand. Other reasons are competitive, legal, environmental, ethical, and moral.

1. Qualitative - subjective or judgmental and are based on estimates and opinions 2. Time series analysis (focus of this chapter) - based on the idea that data relating to past demand can be used to predict future demand 3. Casual relationships - linear regression techniques, assumes that demand is related to some underlying factor or factors in the environment. 4. Simulations - allows the forecaster to run through a range of assumptions about the condition of the forecast.

1. Average demand for a period of time 2. Trend lines - usually the starting point in developing a forecast. Adjusted for seasonal effects, cyclical elements and any other expected events that may influence the final forecast. A linear trend is a straight continuous relationship. 3. Seasonal elements 4. Cyclical elements - more difficult to determine, cyclical influence comes from political elections, war, economic conditions, or sociological pressures. 5. Random variation - caused by chance events. If we can't identify the cause of the reminder portion of demand, it is assumed to be purely random chance. 6. Auto-correlation - the value expected at any point is highly correlated with its own past values. When demand is random, it may vary widely form one week to another. Where high auto-correlation exists, demand is not expected to change very much from one week to the next.

1. Linear - is a straight continuous relationship. 2. S-Curve - typical of product growth and maturity cycle 3. Asymptotic - starts with the highest demand growth at the beginning then tappers off. 4. Exponential - explosive growth. Sales will continue to increase - assumption that may not be safe to make.

Try to predict the future based on a past data. 1. Short term - under 3 months - tactical decisions such as replenishing inventory or scheduling EEs in the near term 2. Medium term - 3 M-2Y - capturing seasonal effects such as customer's respond to a new product 3. Long term - more than 2 years. To identify major turning points and detect general trends.

1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel.

is a functional relationship between 2 or more correlated variables. It is used to predict one variable given the other.

Linear regression is a special type of regression where the relations between variable forms a straight line Y = a+bX. Y - dependent variable a - Y intercept b - slope X - independent variable It is used for long-term forecasting of major occurrences and aggregate planning. It is used for both, time series forecasting and casual relationship forecasting.

Chronologically ordered data that may contain one or more components of demand. Decomposition of time series - identifying and separating the time series data into these components. Trend - easy to identify Cycle, Autocorrelation & Random Composition - hard to identify.

Is the amount of correction needed in ta time series to adjust for the season of the year.

1. Additive Seasonal variations (the seasonal amount is constant) - Forecast = Trend + Seasonal variations 2. Multiplicative Seasonal variations (seasonal variations is a % of demand) - Forecast = Trend X Seasonal factor

1. Determine the seasonal factors 2. Deseasonalize the original data 3. Develop a least sq. regression line for the deseasonalized datat 4. Project the regression time through the period of the forecast 5. Create the final forecast by adjusting the regression line by the seasonal factors.

Errors come from 2 sources: 1. usual errors similar to the standard deviation of any set of data 2. errors because the line is wrong

Frequently centered, it is more convenient to use past data to predict the following period directly. if you want to forecast June with a 5-month moving average, we can take the average of Jan, Feb, March, Apr, and May. The longer the moving average period, the more the random elements are smoothed, but lags the trend. Main disadvantage is that all individual elements must be carried as data because a new forecast period involves adding new and dropping the earliest data. In a forecasting model using simple moving average the shorter the time span used for calculating the moving average, the closer the average follows volatile trends

Allows any weights to be placed on each element, providing, of course, that the sum of all weights equals 1. A company wants to forecast demand using the weighted moving average.

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Is the most used forecasting technique. The most recent occurrences are more indicative of the future (highest predictable value) than those in the more distant past. We should give more weight to the ore recent time periods when forecasting. Each increment in the past is decreased by (1- alpha). The higher the alpha, the more closely the forecast follows the actual. Most recent weighting = alpha(1-alpha) na 0 Data one time period older = alpha(1-alpha) na 1 Data two time period older = alpha(1-alpha) na 2

Delphi Method

True

1. Exp. models are surprising accurate 2. Formulating an exp. model is relatively easy 3. The user can understand how the model works. 4. Little computation is required to use the model 5. Computer storage requirements are small because of the limited use of historical data. 6. Tests for accuracy as to how well the model is performing are easy to compute.

Adjusting the value of alpha.

To correct the trend, we need two smoothing constants - Smoothing constant alpha () - Trend smoothing constant delta (δ) Delta reduces the impact of the error that occurs between the actual and forecast. If both alpha and delta are not included, the trend overreacts to errors.

Value must be between 0 and 1 1. 2 or more predetermined values of Alpha - depending on the degree of error, different values of Alpha are used. If the error is large, Alpha is 0.8, if error is small, Alpha is 0.2 2. Computed values of Alpha - exponentially smoothed actual error divided by the exponentially smothered absolute error.

1. Bias Errors - occur when a consistent mistake is made 2. Random Errors - those that cannot be explained by the forecast model being used.

1. Standard Error - linear regression 2. Mean Square Error (or variance) - standard error is a square root of a function. Average of Square error. 3. Mean Absolute Deviation - the average forecast error using absolute value of the error of each past forecast. Average absolute error. The ideal MAD is zero which would mean there is no forecasting error. The larger the MAD, the less the accurate the resulting model. 4. Mean Absolute % Error - Average absolute % Error

- is a measurement that indicates whether the forecast average is keeping pace with the genuine upward or downward changes in demand. - is the # of mean absolute deviations that the forecast value is above or below the actual occurrence. - +- 5 limits are acceptable

- When one occurrence causes another. - Uses independent variable other than time to predict future demand. - Any independent variable must be a leading indicator.

is appropriate when a number of factors influence a variable of interest.

Knowledge of experts and require much judgement (new products or regions) 1. Market Research - looking for a new products and ideas, likes and dislikes about existing products. Primarily SURVEYS & INTERVIEWS 2. Panel Consensus - the idea that 2 heads are better than one. Panel of people from a variety of positions can develop a more reliable forecast than a narrower group. Problem is that lower EE levels are intimidated by higher levels of management. Executive judgement is used (higher level of management is involved). 3. Historical Analogy - a firm that already produces toasters and wants to produce coffee pots could use the toaster history as a likely growth model. 4. Delphi Method - very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast. Everyone has the same weight (more fair). Satisfactory results are usually achieved in 3 rounds.

To exchange selected internal information on a shared Web server in order to provide for reliable, longer-term future views of demand in the supply chain.

1. Creation of a front-end partnership agreement 2. Joint business planning 3. Development of demand forecasts 4. Sharing forecasts 5. Inventory replenishment Largest hurtle is lack of trust over complete information sharing btw supply chain partners. Front-end partnership agreements nondisclosure agreements, and limited information access may help to overcome these fears.