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Industry 4.0 technologies have attempted to transform current industrial settings to a level that we have never seen before. While at the same time, prevailing applications of Lean tools and techniques over the last 20 years have already dramatically reduced wastes ranging from shop floor production to cross-functional enterprise processes. This paper aims to provide a comprehensive review and report on links between Lean tools and Industry 4.0 technologies, and on how simultaneous implementation of these two paradigms affects the operational performance of factories. The existing and potential enhancements of Lean practices enabled by Industry 4.0 technologies such as wireless networks, big data, cloud computing, and virtual reality (VR) will also be explored. A cloud-based Kanban decision support system is also presented as a real-world demonstrator for integration of an Industry 4.0 technology (cloud computing) and a major Lean tool (Kanban).

The current competitive market demands that manufacturing companies have a survival strategy which should totally focus on providing high-quality products, being consistent in the level of service provided, having lower inventory levels and higher flexibility in operations. In this context, Kanban can be classified as a tool that assists in the proper sizing of inventory levels and production control of the system. However, numerous studies have been developed with the aim of reliably reducing stock levels in Kanban supermarkets, yielding different optimization techniques, but for fixed demands. This work proposes the minimization solution of the supermarket from randomly different demands, using environments with simulated experiments from the ARENA® software, from a response surface methodology (RSM) along with a weighted minimization of the mean standard error and compare with the results provided by the use of optimizer Arena OptQuest®, thus, it is possible to evaluate the robustness question.

This research studies the cellular manufacturing system (CMS) controlled by kanban mechanism which defective items are produced in any production run of each product and rework is carried out to transform them into serviceable items. We consider and compare two different policies for rework where in the first policy rework is completed within the same production cycle and in the second policy rework done after N production cycles. Recently Aghajani et al. (2012) explain policy 2 and proposed a mixed-integer nonlinear programming (MINLP) model for this policy. In order to minimize total cost, MINLP model was developed for policy 1 to find simultaneously the optimal number of kanban, batch size, and number of batches. The cost function includes the cost of setup, holding, and transportation. Due to the high combinatorial structure of the problem, particle swarm optimization (PSO), and simulated annealing (SA) algorithms as meta-heuristic methods are proposed to solve the problem and numerical experiments are conducted to demonstrate the efficiency of the proposed algorithms. It is shown that both PSO and SA result are in a near optimal solution but the PSO algorithm gives a better performance than the SA method. Also, sensitivity analysis is carried out to study the effect of defective rate, holding cost, and setup cost variations on the total system cost is discussed the performance of these policies in different conditions.

In this paper, we consider a hybrid production system with two distinct production lines, where one of them undertakesremanufacturingactivitieswhiletheotherexecutes traditional manufacturing tasks. The output of either produc- tion line can satisfy the demand for the same type of product without any penalties. The realization times for demand oc- currences and service completions are random. In order to control such production systems, we propose a single-stage pull type control mechanism with adaptive kanbans and state- independent routing of demand information. The mechanism is modeled as a stochastic queueing network and performance measures are obtained for stationary behavior. Alternative static control policies are compared with the proposed policy with respect to an expected total cost function of these perfor- mance measures. An experimental design of L(2 1 3 7 )c overing low to high traffic densities is utilized for the numerical analysis. The numerical comparison shows that on the aver- age, the adaptive kanban control mechanism in consideration performs 20 % better than the best static kanban control mechanism.

The main element of just-in-time production system is kanban and the number of kanban influences the product inventory level. This research studies the cellular manufacturing system controlled by kanban mechanism which defective items are produced in any production run of each product and rework is carried out to transform them into serviceable items. In each cycle after the normal production of each product, the machine is setup for the rework process. In this paper, a mixed-integer nonlinear programming (MINLP) model in order to minimize total cost was developed to determine number of kanban, batch size, and number of batches. To avoid the large computational time in optimal solution, a particle swarm optimization (PSO) and simulated annealing (SA) algorithms as metaheuristic methods are used for solving a large MINLP. Some problems are solved by PSO and SA and performance of the PSO and the SA is evaluated by comparing their results with the optimal solution. It is shown that both PSO and SA result in a near-optimal solution but the PSO algorithm gives a better performance than the SA method.

The implementation of a lean manufacturing strategy represents a robust contribution to the phase sequence that leads to operational excellence and the continuous improvement through the elimination of nonvalue-added activities. Therefore, lean practices contribute substantially to plant operational performance. This paper studies the use of value stream mapping (VSM) as a tool in lean manufacturing implementation and a framework of improvement activities, in particular for an efficient introduction of kanban and milkrun techniques. A case study illustrates VSM use, as well as kanban and milkrun systems application on an assembly line. Finally, the results obtained show the path of improvement, measured through the lean rate (LR) and dock-to-dock time (DtD).

In this paper, JIT (Just-In-Time)-KANBAN literature survey was carried out and presented. The introductory section deals with the philosophy of JIT, and the concept involved in the push and pull system. The blocking mechanisms in the kanban system are also discussed elaborately. Besides these sections, the importance of measure of performance (MOP) and the application of the same with respect to JIT-KANBAN are presented. The recent trends in the JIT-KANBAN are discussed under the heading “Special cases”. In this review, 100 state-of-art research papers have been surveyed. The directions for the future works are also presented.

The design of a kanban system addresses the selection of two important parameters, i.e. the number of kanbans and lot sizes of part types. Kanban-based operational planning and control issues have been tackled in a number of studies by means of analytical or simulation modelling. However, the estimation of these parameters becomes complicated because of issues such as variation in demand, variation in processing times, different types of products, etc. The combinatorial property of such problems warrants the development of efficient methodology or heuristics to obtain a good solution. In this paper, an attempt has been made to select the number of production and withdrawal kanbans at each workstation and the lot size for each part type required to achieve the best performance using a simulated annealing algorithm technique. An object-oriented simulation model of a two-card dynamic kanban system capable of handling different types of part with different demand requirements has been developed and used for the analysis. Each part type has its own number of production ordering kanbans and withdrawal kanbans at each workstation. The lot size can also be different for different part types. A bicriteria objective function comprising mean throughput rate and aggregate average kanban queue has been used for evaluation. Different types of problem have been tried out and the performance of the algorithm is studied.