Logistics

Logistics
Name
Institutional Affiliation
Basic types of forecasts; their strengths and weaknesses
Simple moving average
In this forecast, one averagely takes value performances in the last few months to predict or determine the performance in the next trading month. For instance, if there are four months of moving the average model, one takes the average of income or sales from the four months so that the average value gives the predicated sale for the next month. The strength of this model is that one can easily predict future operations, and sales because the method is easy and straightforward. However, Patil and Shirke (2017) argue that the weakness of the forecast is that it may not be accurate due to different prevailing factors within a month.
Weighted moving average
The forecast allows one to put weight on recent performance data rather than previous data. By using the previous data, a weighting factor is multiplied by prices in every bar. The strength of this forecast is that it allows one to emphasize recent demands because it is from these demands that one can predict the future. The weakness, however, is that it does not predict or account for patterns in seasonal demands.
Exponential smoothing
The forecast is the weighted average that considers the last number price (n-value) but with an exponential decrease in weighting. The major strength is that it is simple to operate besides having limited needs for data. Nevertheless, when dealing with seasonal demands from the market, the forecast may give inaccurate information about the demands (Patil & Shirke, 2017).

The nature of the primary interface between logistics and marketing and how it might be changing
With the changing business environment, customer service helps focus business performances, so that market penetration functions as a design model. That is, customer service should act as the linkage between marketing and logistics. For instance, ineffective outbound logistics will create performance gap thus, compromising on service delivery to customers or end users. The gap in customer service implies that the company or organization will not meet its current and future revenue objectives. Gansterer (2015) acknowledged the need to satisfy customer needs in logistics. The study further observed that most organizations focus and place emphasis on quality production, balance in the marketing mix, and cost or price determination rather than customer satisfaction. The nature of the interface is that if logistics do not deliver the products at the right time the other variables, including quality production may not help in creating a brand reputation. Nonetheless, the interface is changing as logistics now focus on affecting levels of customer service rather than production delivery. Currently, these changes have affected business approach where suppliers are now also concerned with their financial position, cost reduction, and product availability when designing logistics model to use.
References
Gansterer, M. (2015). Aggregate planning and forecasting in make-to-order production systems. International Journal of Production Economics, 170, 521-528.
Patil, S. H., & Shirke, D. T. (2017). Economic design of moving average control chart for non-normally distributed data: A comparative study. International Journal of Operational Research, 28(1), 98-120.