# A Multi-Criteria Assessment of Manufacturing Cell Performance Using the AHP Method
## 1 Introduction and Problem Description  
The smart manufacturing concept, as an important part of the Industry 4.0 strategy, opens new opportunities for producers to implement new platform-based business models by embracing cutting-edge technologies. Cellular manufacturing (CM) systems belong among six fundamental manufacturing systems, for which Industry 4.0 has been conceived [1]. Their goal is to complete jobs as swiftly as possible, make a wide variety of similar products, and produce as little waste as possible. An important objective of CM systems is to be as easily reconfigurable as possible [2; 3; 4]. If the number of potential configuration design solutions is generated, the role of the user is to define constraints on design and performance to obtain solutions meeting their requirements. Subsequently, it is needful to explore design options and configurations to select an optimal solution. Operation management research often reflects CM problems in order to make manufacturing operations more efficient and productive. The cellular manufacturing method brings scattered processes together with compact cells usually arranged in U-shapes and can significantly improve the operation of batch production [5]. Batch production with a wide range of product types presents a crucial problem for layout designers since the parts move in batches from one process to another, and ready parts must wait for the remaining parts to complete processing before they move to the next stage. Because operation times are distinct one from another, it causes unbalanced machine utilization, scheduling problems, and possible late deliveries. Unbalanced machine utilization is frequently considered one of the main criteria in CM design optimization since ineffective utilization of machines can result in unprofitable production [6; 7]. Another approach to optimize CM design is based on using appropriate scheduling techniques that aim to determine the actual assignmentof products or jobs to available operations in order to complete manufacturing orders on time [8]. These techniques are mostly developed using the minimum makespan criterion for CM design optimization [9]. Moreover, makespan minimization positively influences work-in-process inventories [10; 11]. Taking into consideration the fact that CM systems have to be adaptable to changing market demands, their structural and operational complexity adequately increases as a consequence [12; 13]. In this context, the cost of operational complexity is becoming a topical problem, and therefore, the trade-offs between the level of complexity and its added value are often discussed (see, e.g., [14; 15; 16; 17]). It is also worth mentioning that CM is not always the appropriate approach to take for certain scenarios [18]. This problem has been studied, e.g., by [PERSON] and [PERSON] [19], who indicated that, through a well-organized job shop, it is possible to achieve at least as good a performance as the cellular layout with respect to several criteria such as work-in-process inventory levels and average flow times. For this reason, it is assumed in this study that a given manufacturing process satisfies basic criteria for a cellular layout, which include a high ratio of setup to process time, stable demand, unidirectional workflow within a cell, and a considerable level of material movement times between process departments [20]. The problem that is treated in the present work can be described as follows. The time required to complete all jobs, namely makespan, is one of the most frequent performance indicators for the job shop problem [21]. Considering that one-piece flow cellular manufacturing is the ultimate in lean production [22], CM is primarily aimed at reducing times within the production system. On the other hand, competitiveness through cost reduction in the design and implementation of production systems is an important and permanent task for process designers.