The paper 'Demand Forecasting Using Time-Series Forecasting " is an outstanding example of business coursework. Quantitative techniques are used in decision making of logistics management to analyze data and uncover important information and hence make decisions on material acquisitions, production quantities, production capacity and the number of inventories and resources required. Demand forecasting is an important aspect in logistics management and one of the quantitative techniques that can be used in demand forecasting is time-series forecasting and index numbers where the historical data is analyzed and this provides the basis of decision making regarding the demand.
Through forecasting time series, the seasonality, cyclic patterns and trends of demand can be identified because forecasting using time series, four key elements in the past demand are used and they include average, trend, seasonal, as well as the cyclical element. Some time-series forecasting techniques include moving average, exponential smoothing, double exponential smoothing, and extended exponential smoothing. Some of the limitations with time series forecasting include that the quantitative technique can lead to erroneous forecasting because whereas the methods used in time-series forecasting can link historical patterns into future forecasts, the methods are not effective in integrating the input of projected future events.
Therefore, to ensure accuracy during forecasting it is important to integrate the forecast techniques with suitable support and administrative systems. Another limitation is that some of the time-time series forecasting techniques can be significantly unresponsive to change and they also need updating and maintenance of huge historical data when calculating forecasts. This can be solved by combining the techniques during data analysis as this can allow detection of deviations and thus tracking errors that may arise by using one technique. Assignment 2: Demand Forecasting using Time-series forecasting The focus of this assignment is on the relationship between data analysis and the decision making of logistics management.
A tremendous amount of data is generated during logistics management and thus it is necessary to analyze the data sets to unearth important information thus data forms the first step in logistics management by providing the required information to provide the basis for making decisions on components of logistics management such as acquisition of production materials, organization production capacity, the number of inventories as well as making decisions regarding inventories (Krager, 2010). In particular, the paper will center on the use of time series forecasting in forecasting demand in order to predict the future demand and therefore make supply and logistics management decisions that can adequately meet the current demand.
Demand forecasting serves as the foundation for planning decisions with regard to supply chain and logistics management. Basically, demand forecasting is very important for organizations that carry out pull or push strategies where organizations utilizing push strategy produce materials/products in anticipation prior to getting customer requests, while organizations that use pull strategy base their production on the real customer demand (Chan & Spedding, 2000).
Consequently, demand forecast is required in a pull strategy where a company basis its production on the real customer demand to establish the availability of production capacity as well as inventory. Similarly, in a push strategy where the companies produce goods in anticipation before receiving customer requests, demand forecasting is done in order to determine the production quantities. Therefore, demand forecasting is required to ascertain the efficacy and profitability of the organization’ s future operations (Donald, 2010).
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