Crossed products, locally indicable groups and Hughes-free division rings

Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena. There is a lot of debate in the demand planning literature as how to measure and represent historical demand, since the historical demand forms the basis of forecasting. Should we use the history of outbound shipments or customer orders or a combination of the two to proxy for demand.

3.16.8.1 Stock Effects

The effects that inventory levels have on sales. In the extreme case of stock-outs, demand coming into your store is not converted to sales due to a lack of availability.

Demand is also untapped when sales for an item are decreased due to a poor display location, or because the desired sizes are no longer available. For example, when a consumer electronics retailer does not display a particular flat-screen TV, sales for that model are typically lower than the sales for models on display. And in fashion retailing, once the stock level of a particular sweater falls to the point where standard sizes are no longer available, sales of that item are diminished.

3.16.8.2 Market Response Effects

The effect of market events that are within and beyond a retailer’s control. Demand for an item will likely rise if a competitor increases the price or if you promote the item in your weekly circular. The resulting sales increase reflects a change in demand as a result of consumers responding to stimuli that potentially drive additional sales. Regardless of the stimuli, these forces need to be factored into planning and managed within the demand forecast.

In this case demand forecasting uses techniques in causal modeling. Demand forecast modeling considers the size of the market and the dynamics of market share versus competitors and its effect on firm demand over a period of time. In the manufacturer to retailer model, promotional events are an important causal factor in influencing demand. These promotions can be modeled with intervention models or use a consensus process to aggregate intelligence using internal collaboration with the Sales and Marketing functions.

3.16.8.3 Components of a Forecast and Forecasting Methods

A company must be knowledgeable about numerous factors that are related to the demand forecast. Some of these factors are listed next.

1. Past demand

A company must understand such factors before it can select an appropriate forecasting methodology. For example, historically a firm may have experienced low demand for chicken noodle soup in July and high demand in December and January. If the firm decides to discount the product in July, the situation is likely to change, with some of the future demand shifting to the month of July. The firm should make its forecast taking the factor into consideration.

3.16.8.4 Forecasting Methods are Classified According to the Following Four Types:

1. Qualitative: qualitative forecasting methods are primarily subjective and rely on human judgment. They are most appropriate when little historical data is available or when experts have market intelligence that may affect the forecast.

Such methods may also be necessary to forecast demand several years into the future in a new industry.

2. Time series: Time-series forecasting methods use historical demand to make a forecast. They are based on the assumption that past demand history is a good indicator of future demand. These methods are most appropriate when the basic demand pattern does not vary significantly from one year to the next. These are the simplest methods to implement and can serve as a good starting point for a demand forecast.

3. Causal: Causal forecasting methods assume that the demand forecast is highly correlated with certain factors in the environment (the state of the economy, interest rates, etc.). Causal forecasting methods find their correlation between demand and environmental factors and use estimates of what environmental factors will be to forecast future demand. For example, product pricing is

strongly correlated with demand. Companies can thus use causal methods to determine the impact of price promotions on demand.

4. Simulation: simulation forecasting methods imitate the consumer choices that give rise to demand to arrive at a forecast. Using simulation, a firm can combine time-series and causal methods to answer such questions as: what will be the impact of a price promotion? What will be the impact of competitor opening a store nearby? Airlines simulate customer buying behavior to forecast demand for higher-fare seats when there are no seats available at the lower fares.

A company may find it difficult to decide which method is most appropriate for forecasting. In fact, several studies have indicated that using multiple forecasting methods to create a combined forecast is more effective than using any one method alone.

3.16.8.5 Aggregate Planning

Aggregate planning is an operational activity which does an aggregate plan for the production process, in advance of 2 to 18 months, to give an idea to management as to what quantity of materials and other resources are to be procured and when, so that the total cost of operations of the organization is kept to the minimum over that period.

The quantity of outsourcing, subcontracting of items, overtime of labor, numbers to be hired and fired in each period and the amount of inventory to be held in stock and to be backlogged for each period are decided. All of these activities are done within the framework of the company ethics, policies, and long term commitment to the society, community and the country of operation.

Aggregate planning has certain pre-required inputs which are inevitable. They include:

• Information about the resources and the facilities available.

• Demand forecast for the period for which the planning has to be done.

• Cost of various alternatives and resources. This includes cost of holding inventory, ordering cost, cost of production through various production alternatives like subcontracting, backordering and overtime.

• Organizational policies regarding the usage of above alternatives.

"Aggregate Planning is concerned with matching supply and demand of output over the medium time range, up to approximately 12 months into the future. Term aggregate implies that the planning is done for a single overall measure of output or, at the most, a few aggregated product categories. The aim of aggregate planning is to set overall output levels in the near to medium future in the face of fluctuating or uncertain demands. Aggregate planning might seek to influence demand as well as supply."

3.16.8.6 Objectives of Aggregate Planning

The aggregate planner’s main objective is to identify the following operational parameters over the specified time horizon:

• Production Rate: The number of units to be completed per unit time (such as per week or per month)

• Workforce: the number of workers/units of capacity needed for production

• Overtime: the amount of overtime production planned

• Machine Capacity Level: the number of units of machine capacity needed for production

• Subcontracting: the subcontracted capacity required over planning horizon

• Backlog: demand not satisfied in the period in which it arises but carried over to future periods

• Inventory on Hand: the planned inventory carried over the various periods in the planning horizon

3.16.9 Design Options for a Transportation Network