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The objective of this assignment is to investigate different business forecasting methods, and demonstrate the benefits of their use for a specific organization. We have learned that demand forecasting invokes the processes of determining exactly what service/products are needed, in what quantity, and in what amount of time. Organizations that are able to implement effective forecasting will be better equipped to find the balance between managing demand for a product/service and the capacity to meet this demand. The ability of optimizing this unique balance enables an organization to use this as a competitive advantage over their competitors
There are a variety of forecasting models to choose from and organizations should first decide which type of business decision is being made.
This initial determination will allow managers to decide which forecasting methods are appropriate or not given the period of time allotted. There are four basic families that describe distinct demand forecasting techniques which include the qualitative, time series analysis, causal, and simulation models.
We have learned that it is important to keep options open to apply different models – the one most readily available or commonly used may not be the most appropriate, and choosing the wrong one can cost a larger organization millions.
The qualitative forecasting technique is highly subjective in that it is readily influenced by opinions and estimates primarily utilized for long-range corporate strategies. The unique distinction between this method and the other three forecasting families is that those tend to be more quantitative in nature, relying heavily on the process of gathering and analyzing hard data.
There are no formal mathematical models utilized in this forecasting family. For example, market research normally consists of a third party firm that primarily conducts surveys and interviews to determine product or service interest on various demographics. This information is then provided en masse without much analysis taking place. This method is highly judgmental, subject to individual bias, and used when historical data cannot be gathered.
This forecasting technique is assumption-based in that it compares existing past demand data in order to predict future demand. An example of this technique is the simple moving average which allows the casual observer to visually witness trends and determine trends by changing period length which in turn affects visual trend lines. One of the primary attractions of this technique is that data is quickly compiled and organized, but a negative exists in that all the data collected during this process must be maintained until replaced by newer data. Best suited for short-term forecasts, other models within this family can more accurate forecast demand by using different techniques such as applying modifiers to desired ranges, but this comes at a higher cost due to increased amount of data and computation required. This forecasting family of models should not be utilized for long-term projections.
The causal method predicts demand by assuming events are related to one another through some independent variable as its leading indicator, and relies upon fitting a mathematical trend line to acquired data points on this causal relationship to predict future demand. The multiple regression analysis is a subset of causal demand forecasting technique because it links two or more independent variables to demand of a product or service. This forecasting model is useful in calculating several factors to determine a single point of interest, and is most useful in determining long-term forecasts; a task in which it excels.
The use of computers enables simulation-based forecasting models to make assumptions of multiple variables within the model, and quickly allow theoretical events to measure change. The computing power enables families such as causal demand techniques to be more accurately predicted as they use multiple variables. This model isn’t useful for shorter term, single-variable forecasts.
In discussions threads in class, I noted that my organization has had difficulty in effectively balancing the workload of it’s full-time employees (me) and contractors, and have performed quite poorly in developing a method to determine (and schedule) service load. For this and many other reasons, I have become quite despondent working with my current company, so I opted to perform research on another company I am familiar with, the Dell Computer Corporation.
Dell has positioned itself as the direct to consumer producer of quality computers with top notch service, but in order to meet growth objectives they must rely on the ability to accurately forecast demand and provide this information through its supply chain. This is done in order to minimize costs, maintain market growth, and maximize profits. Dell maintains close relationships with its customer base to improve the accuracy of its demand forecasts. These are developed through the customer databases, based on products purchased, in an attempt to understand future computing needs of large accounts by jointly planning the company infrastructure and discussing their needs (Viswanadham, 2002).
Since 1984, Dell Computer Corporation has pioneered the direct marketing channel of PCs and their marketing objectives were based on a basic premise that PCs should be available to the general public. In direct marketing, the manufacturer sells directly to the consumer, rather than through “middle-man” retail stores. This direct business model eliminates retailers that add unnecessary time and cost, or can diminish Dell’s understanding of customer expectations. (Dell.com), and partially eliminates the need for extensive inventory in storage. Therefore, demand forecasting techniques are less critical for this business model than the more traditional models of their competitors (notably HP and IBM).
In order to maintain efficiency through the use of demand forecasting, Dell actively recruits individuals familiar with this knowledge coupled with their core beliefs of customer service and cutting edge technology. For example, the following is an active job posting listing the following requirements:
“Analyzes business needs and develops support solutions/processes to improve customer experience. Drives efficiencies to optimize IT resources. Participates in roadmap planning and project staffing/forecasting activities. … Has strong track record of effectively managing people as measured by Tell Dell results and peer feedback … Develops support forecasts and standards. … Strategic planning and tactical operational skills. Comprehensive understanding of IT disciplines. ” (Dell.com)
Dell uses a “Build-to-Order” (BTO) manufacturing process which distinguishes itself from Build-to-Forecast methods of more traditional computer manufacturers. This enables the delivery of a customized, lower-cost product quickly to the customer (Viswanadham, 2002). To further take advantage of their manufacturing model, Dell actually built a distribution center near a FedEx hub in Greensboro, NC to facilitate delivery to their customers.
Dell maintains a distinct competitive edge with an integrated supply chain process that refreshes their inventory every 3-6 days. This allows Dell a “first-to-market” advantage on newly developed technology and is important because over short periods of time, the price of technology drops and a competitor that is unable to move inventory as rapidly loses valuable profit. A benefit of this framework that Dell has established for its supply chain is that distributors carry the onus of storing and delivering large quantities of different products … [c]ompanies such as Dell using the direct model normally have around 20% cost advantage over companies using the direct one (Viswanadham, 2002). This price advantage results in lower prices for consumer products and a much larger market share as a result.
Demand forecasting is important to any business unit because it can minimize resources lost due to errors of forecasting vs. the actual realized demand. The process of forecasting is to examine recently-gathered data and develop a method to anticipate future events based on this earlier representation. Examining various forecasting models is important in order to determine which method best fits process requirements, and what amount of data needs to be collected over the desired timeframe. At this point, the appropriate forecasting model can be utilized to generate and forecast future events. Dell’s direct model greatly diminishes the need for highly accurate demand forecasts as they are quickly able to adjust, through their supply chain, product availability based on actual product demand.
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