Bringing the design perspective to coworking-spaces: Constitutive entanglement of actors and artifacts

Different forms of coworking spaces have spread worldwide that use various artifacts (e.g., office settings, décor, furniture, equipment) to strengthen autonomy, flexibility, spontaneous interactions, and intrinsic motivation. However, the various manifestations of coworking spaces limit clearly defined boundary conditions for their successful implementation and demand a profound understanding of the interactions that take place among actors and artifacts. Using a flexible pattern matching approach, our study explains that artifacts can contour and create the possibilities for actors and define the borders, e.g., when, where, and with whom to communicate and work in coworking spaces. Our findings indicate that actors and artifacts are constitutively entangled in coworking spaces and play a key role in the community development process by fostering a shared understanding and identity, mutual interdependency, and common practices.     

An Approximate Solution to Deterministic Kanban Systems

This paper presents a simple heuristic for computing the number of kanbans in a kanban system. The production systems under consideration have a multistage, uncapacitated, assembly tree structure, with every stage producing only one item at a time. The experimental results show that this method generates very good solutions. In more realistic problems where the master production schedule incorporates a smoothed demand, the solution found under the proposed method is identical to that of the linear programming approximation. This simple heuristic can be used in the real world to quickly determine the number of kanbans for daily just-in-time operations.     

Structuring Conflict in Individual,Face-to-Face,and Computer-Mediated Group Decision Making: Carping Versus Objective Devil's Advocacy*

Researchers and practitioners have long been interested in the effects of cognitive conflict techniques on individual and group decision making. One widely used and studied technique, devil's advocacy (DA), has been found to enhance decision-making performance for both individuals and groups. Devil's advocacy begins with a recommended decision, followed by a critique of the decision that questions its assumptions. Researchers have not yet examined the effects of the objectivity of the devil's advocacy comments in computer-mediated environments. This paper reports the results of a laboratory experiment that focused on this question by comparing the effects of an objective, nonemotional DA to an emotional, “carping” DA within individuals and groups using either computer-mediated or face-to-face communication. In a manner consistent with prior research, both DA treatments were operationalized through the use of paper-based consulting reports. The results suggest that individuals and computer-mediated groups develop and consider more solution alternatives than face-to-face groups, and that subjects given the objective DA treatment produce higher quality decisions than those given the carping DA treatment. Face-to-face groups in the carping DA treatment considered the fewest alternative solutions in their decision-making process, reached the lowest solution quality, yet reached decision consensus in the fewest voting rounds. The practical implications of the results suggest that questioning statements made by a devil's advocate should be objective, regardless of group communication condition. Carping devil's advocacy appears to stifle group decision outcomes when groups are using face-to-face communication.     

Performance of Selected Part-Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection

This study addresses the part-machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array-based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non-hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p-median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture-model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry-size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array-based methods of ROC2 and DCA.