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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 5, April 2012, Pages 5251–5261
Quality control of food inventories in the warehouse is complex as well as challenging due to the fact that food can easily deteriorate. Currently, this difficult storage problem is managed mostly by using a human dependent quality assurance and decision making process. This has however, occasionally led to unimaginative, arduous and inconsistent decisions due to the injection of subjective human intervention into the process. Therefore, it could be said that current practice is not powerful enough to support high-quality inventory management. In this paper, the development of an integrative prototype decision support system, namely, Intelligent Food Quality Assurance System (IFQAS) is described which will assist the process by automating the human based decision making process in the quality control of food storage. The system, which is composed of a Case-based Reasoning (CBR) engine and a Fuzzy rule-based Reasoning (FBR) engine, starts with the receipt of incoming food inventory. With the CBR engine, certain quality assurance operations can be suggested based on the attributes of the food received. Further of this, the FBR engine can make suggestions on the optimal storage conditions of inventory by systematically evaluating the food conditions when the food is receiving. With the assistance of the system, a holistic monitoring in quality control of the receiving operations and the storage conditions of the food in the warehouse can be performed. It provides consistent and systematic Quality Assurance Guidelines for quality control which leads to improvement in the level of customer satisfaction and minimization of the defective rate.
Food quality is an important issue in the food industry. Lots of quality checking and assurance duties are required throughout the whole food chain. Poor quality control decisions may lead to a high level of defective goods and a poor level of customer satisfaction (Nilsson et al., 2001 and Youngdahl and Kellogg, 1997). Currently, food safety assuring systems like Hazard Analysis and the Critical Control Point (HACCP) system are widely promoted for demonstrating the commitment to food safety in the food industry (Orriss & Whitehead, 2000). The warehouse, where food is stored and where value adding activities are performed, is no exception to this commitment. In order to ensure the food is of acceptable quality, appropriate quality control actions need to be performed (Getinet et al., 2008 and Yan et al., 2008). However, the operations and duties that need to be undertaken for the sake of safety assurance are complex and difficult to apply. In fact, the current quality assurance process adopted in warehouses has several serious problems. Traditionally, the decision making process for selecting the necessitate quality control operation relies mainly on the skills and experience of operators. This means that errors can easily occur. Nevertheless, the increasing niche requirements of diversified value added activities and the great variance in Stock Keeping Unit (SKU) which require entirely different handling operations in the warehouse, further increase the complexity of the quality control. On top of this, numerous researchers have undertaken different studies in promoting and evaluating the importance of adopting various quality assurance systems concerned with food handling. However, the actual practical automation of a quality control assistance system in warehouses, as a research field, has not yet been much explored. An investigation into the adoption of a decision support system (DSS) for automating the process may help to improve the situation. The purpose of this paper is to outline and illustrate a decision support approach to automate the existing human based decision making practice for determining the appropriate quality assurance operations for food inventory management. An integrative prototype system, namely, the Intelligent Food Quality Assurance System (IFQAS), has been developed, not only for facilitating the selection of the most appropriate quality control operations, but also for suggesting the best storage environment for the goods after the quality has been checked. In the system, a Case-based Reasoning (CBR) engine which solves new cases by reusing the previous handling experience in decision making is proposed to replace the manual decision making patterns in deciding the quality assurance operations. Because of the special nature of the food items it is not possible for humans to judge accurately the condition of the food inventory or express the conditions in precise numerical values, so often vague, fuzzy logic techniques are applied in order to extract the critical quality assurance information in terms of fuzzy rules. Therefore, a Fuzzy rule-based Reasoning (FBR) engine is constructed for suggesting the appropriate storage environment. The rest of this paper is organized as follows. In Section 2, the current quality assurance method used in the food industry with the application of various kinds of technology is reviewed. In Section 3, the system architecture of an Intelligent Food Quality Assurance System (IFQAS) and the mechanism of its two modules are described and explained in detail. In Section 4, a case study for validating the feasibility of the adoption of IFQAS is presented. Section 5 contains a detailed discussion of the system’s performance. The final section, Section 6, concludes the paper.
نتیجه گیری انگلیسی
In a warehouse, quality control of inbound inventory is critical. Receiving operations act as the first gate for quality assurance especially with food inventory. Burdensome procedures and regulations make it difficult for operators to carry out their duties efficiently. Hence, the traditional methods mainly considered the quality control activities at the operations level, they rarely focused on improvement at the parameter level. This paper provides a holistic monitoring approach from the receiving docks to the inventory storage stage. It demonstrated a decision support approach to automate the human based decision making process in the determination of quality assurance operations and storage conditions suggestion of inventory at the parameter level. With the support of the decision support technologies like CBR and FBR, a system, namely IFQAS, is designed to assist and automate the safety control decision making process. The aim of the system is to provide consistent and systematic quality assurance, with improvement in customer satisfaction level and in the defective rate. Despite the contributions of the proposed system in the food quality control perspective, the system is not without constraints. The tacit knowledge and operation practices of human experts are computerized for the preservation of past cases and to help in the construction of rules. In order to collect information for those formations, investigation or analytical tasks need to be executed. Such requirements may not be easy for small or immature companies to fulfill, which increases the difficulty of successful adoption. Thus, the generalization of the storage condition suggestions to other warehouse with different food inventories may be limited, as different parameters may need to be analyzed when some special food items are involved. Further investigation on the appropriateness of integrating the Radio Frequency Identification (RFID) technique for automating the process is suggested. RFID can help improve the visibility of the location and status of the inventory and this can further develop a holistic monitoring of the inventory.