Abstract
Companies have to deter many remarkable problems on a daily basis. The existence of excess inventories and stock out events as well the usage of emergency deliveries are three indicative prominent subjects for discussion. Firms try to comprehend the consumer's desires in order to enhance costumer experience. In the current chapter, sales data of one tangible product from a well-known Greek retailing company are used. These data are analyzed in order to forecast the behavior of the demand. The author adapts an alternative approach for managing inventory during cycles with different levels of demand in order to develop a system that minimizes both excessive inventories and stock out events. Additional experiments take place regarding the parameterization of emergency deliveries in order to configure whether their usage is beneficial. Both an error analysis model and a simulation model are developed so as to determine the results of the aforementioned action.
TopBackground
Companies are able to acquire new heritage, face potential threats, and obtain new abilities by using knowledge properly. Shu-hsien Liao (2003) points out that special frameworks, methods, and tools are able to embrace the knowledge management. Quantitative methodologies which explore the notions of classification, acquisition, and decision are indicative methods to obtain knowledge.
Companies ought to unfold new techniques and services in order to retain existing customers and obtain new ones. The knowledge of consumer’s purchasing behavior is an indicative way. Although promotional periods are able to attract new customers, creating new techniques based on data manipulation are the main incentives that a company has to follow. Tapscott et al. (2011) concluded that knowledge support systems are proper tools for the effective reuse of knowledge. Alongside, Marcus (2001) concluded that information technology is the proper way to manage knowledge.
Bevington and Robinson (2003) stipulated in their book that the uncertainty must be confronted through experiments and measurements. In order to achieve this, it is a dire need to focus mainly on the uncertainties which appear due to fluctuations in measurements and the systematic errors that minimize the correctness of the outcome.
Cooper et al. (1997) points out that it is a matter of great importance for a company to belong to the proper supply chain. All the collaborators should cooperate efficiently in order to optimize the procedures within the supply chain. This gives the appropriate opportunity in a company in order to adapt a remarkable performance. Huan (1995) provides an appealing approach to the significance of a marked trade collaboration in enhancing customer satisfaction.
Regarding the uncertainties that have been already mentioned, Towill et al. (2000) suggests that there are four principal categories of uncertainties: supply side; process side; demand side; and control side. In every company there is a marked incentive to decrease uncertainty. In the current chapter (mainly with the forecasting methods) the author tried to minimize the demand side uncertainties. Demand side uncertainties can show a downward trend by receiving feedback through demand data from the consumers. Hence, companies are able to manipulate these data in order to improve their performance, acquire new customers and maintain the old ones.
Key Terms in this Chapter
SKU: A distinct type of tangible or intangible good for purchase.
Emergency Delivery: An infrequent delivery due to excessive demand.
Lost Sales: Company is unable to satisfy customers’ needs.
Stock Out Event: The inventory that is depleted.
Simulation Model: A model that represents an actual procedure over time.
Excess Inventories: The inventory that has never been purchased and remains in the stock.
Advanced Demand Analysis: Analyze demand data properly by using statistical methods.