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Lean manufacturing has become a paramount production paradigm in many industrial sectors and overall equipment effectiveness (OEE) is a widely accepted and adopted lean metric for any type of machine/equipment. However, the standard OEE formulation presents two important limits: 1) it deals with a single machine; 2) gives a deterministic measure of effectiveness. The objective of this paper is to overcome both of these two shortcomings by proposing a Fuzzy approach for measuring the OEE of a manufacturing line (OEEML). Hence, starting from a possible definition of OEEML, this paper provides a framework to measure its modal value and its variability on an entire manufacturing line. The approach exploits fuzzy triangular numbers instead of burdensome stochastic quantities to measure this variability. Furthermore, it integrates a method which enables us to avoid the annoying 'overestimation effect' of FTNs. The integrated approach has proved to be useful in consistently capturing the OEEML variations, also, both the modal value and the range obtained from its application are good estimators of both the OEEML and its variability. A full case study is finally provided to show the effectiveness of the proposed approach in diagnosing problems and addressing improvement actions.

This paper presents a QFD based methodology allowing the assessment of different plant layout proposals. Facilities layout is a key factor affecting a firm’s profitability and productivity. Such a decision is a strategic one. In literature several works deal with this aspect, and in particular concerning the design task. Meanwhile, along the plant layout life cycle, other ac-tivities have to be carried out that are equally important. This makes the evaluation phase necessarily subsequent. With respect to other assessing techniques, that are often too vague, the one here presented attempts to face the problem in a more rigorous fashion. On the basis of the QFD concept, the most important aspects concerning a plant layout evalua-tion are considered. In particular, customer needs are first derived, technical requirements are identified, and finally a comparison method is presented.

PurposeThe paper proposes a new workforce effectiveness metric that is a sophisticated evolution of a pre-existing overall labour effectiveness (OLE) indicator. The KPI, named revised OLE (ROLE), provides a structured methodology to measure in a holistic way the losses relating to labour, maintaining some formal similitude to the overall equipment effectiveness (OEE)Design/methodology/approachA new structure of losses is proposed to overcome the drawbacks and the difficulties that usually affect the data collection stage, referring to directly measurable quantities or, when this is not the case, suggesting a viable method to quantify the loss. Besides, this approach facilitates the comprehension of labour-related issues, suggesting potential countermeasures. The novel ROLE indicator has been defined, based on this new structure, to evaluate the labour effectiveness in batch process industries. A real case study is provided, which explains the methodology and illustrates the capability of the corresponding KPI.FindingsThe present work analyses the labour performance indexes available in literature, with the aim of evidencing those aspects that can be properly observed and quantified and, at the same time, categorizing them to identify their possible drawbacks. A new structure of losses is derived, with respect to four different categories, which may help to measure the losses themselves more effectivelyOriginality/valueThe paper investigates some important KPIs dealing with labour performance and individuates some significant drawbacks. Then it suggests a new, inclusive structure of losses and a modified KPI that not only measures effectiveness but also allows to identify viable countermeasures.

In the Industry 4.0 era, Preventive Maintenance (PM) is still an attractive solution to prevent breakdowns and failures and to reduce maintenance and failure costs. A PM program is part of both the Total Productive Maintenance (TPM) philosophy and the Reliability Centered Maintenance (RCM) process. A prerequisite to carry out effective PM activities is the availability of a reliable estimate of the equipment failure rate. Assessing it may be a hard task, as it requires analysing a large set of maintenance data, which includes both quantitative and qualitative variables. To this aim, it is possible to exploit advanced data analysis techniques that permit extracting information and knowledge from big datasets. This paper presents an ensemble-learning model to estimate the failure rate of equipment subject to different operating conditions. At the same time, the method permits to identify the most important working parameters affecting the failure rate. An industrial application is considered to show the potentialities and the effectiveness of the proposed method. In particular, a sample of 143 centrifugal pumps installed in an oil refinery plant is analysed.

The I-READ 4.0 project represents an integrated and autonomous Cyber Physical System, for automatic management of very large warehouses with high-stock rotation index. Thanks to an internet of UHF-RFID readers, both fixed and mobile, it is possible to implement an efficient management of assets and forklifts operating in indoor scenario. The proposed solution can be employed independently on the warehouse sizes, the good types and the spatial resolution required for item localization by virtue of its low-cost, reconfigurability and scalability. The I-READ 4.0 system allows also to increase the safety levels of the human operators within the warehouse.

The present paper introduces an integrated approach to support the process engineers during the implementation of the conventional Single Minute Exchange of Dies. In particular, the proposed approach grounds on the new concept of ‘duplication strategy’, and takes advantage of an implementation tool here presented. It follows a top-down functional decomposition of machines, which helps practitioners identifying all those items impacting on the changeover process, and to propose appropriate intervention strategies. It aims at verifying the possibility of replicating entire parts of machines, fixtures and/or equipment, in order to externalise as many tasks as possible, yet carefully evaluating the cost-benefit ratio and any operational issue of the intervention. To show this novel approach, an actual implementation is hereby fully described. Finally, the results of the study are commented and proposals for future possible developments are suggested.

A rational use of production resources has been catalysing the attention of researchers and practitioners for several years. A number of indicators have been proposed to assess the effective and efficient use of the various production factors. A widely known example of these metrics is the overall equipment effectiveness (OEE), which applies to machines and equipment. This paper proposes a novel methodology that seizes upon OEE’s straight forward and easy-to-use structure to address the problem of measuring the effective use of materials within a factory. An inclusive structure of material loss and a methodology to calculate a novel performance indicator called overall material usage effectiveness (OME) is given. The proposed set of KPIs not only permits the comprehension of material-related issues but also the identification of viable countermeasures. A real-life application to demonstrate the practical usefulness of the methodology and illustrate the capability of its KPIs is described.

The present work is meant to show the effective capability of optimizing an unbalanced Paired-Cell Overlapping Loops of Cards with Authorization (POLCA)-controlled production system by means of a heuristic algorithm. This objective is suggested by the fact that one of the most significant issues when using card-driven production control systems is represented by the optimized setting of the large number of cards within the control loops. This is particularly true in the case of unbalanced systems, where the number of cards may vary significantly among the different loops. Little law is usually adopted in literature to infer this number from historical data, but the obtained number is usually far from the optimum. Indeed, in real-world applications, the systems to be controlled are designed to process units with very different routings, each with different probability to occur. In all these situations, they result particularly difficult to set correctly. To this aim, in the present work a Genetic Algorithm is used. The objective is that of finding the correct number of cards and to reduce the overall Total Throughput Time and the average Work In Process. The proposed approach may provide a valid support tool to overcome these limitations, making the most of POLCA capabilities in many manufacturing configurations.

In this paper, we study the periodic-review stochastic Joint-replenishment Problem (JRP), with backorders-lost sales mixtures, controllable lead times, and investment to reduce the major ordering cost. The purpose is to determine a strict cyclic replenishment policy, the length of lead times, and the major ordering cost that minimize the total system cost. We first present an effective heuristic algorithm to approach the problem. However, results illustrate how computationally expensive the algorithm would be for a practical application. Hence, we then propose an efficient and more practically applicable solution procedure. In particular, approximating part of the cost function with its second-order Taylor series expansion, we obtain an expression that resembles the deterministic cost structure. Therefore, the problem can be approached exploiting a standard algorithm suitable for the deterministic JRP. Numerical tests compare the performances of the algorithms developed and show that the approximated approach is actually promising for a practical application.

This paper considers the management of safety stock in a coordinated single-vendor single-buyer supply chain under continuous review and Gaussian lead-time demand. The lead time is supposed controllable, and shortages are not allowed. We follow the present value criterion by considering both inflation and time value of money. Our aim is to present a novel approach to optimizing the safety stock in such system. Under the conditions considered, the safety stock is typically determined according to the value assigned to the safety factor, which is thus treated as a parameter of the model, and not as a decision variable. In this paper, we take a different perspective by putting the order quantity and the safety factor in functional dependence through the adoption of a specific parameter. More precisely, we express the service level as a function of the number of admissible stockouts per time unit and the order quantity. This allows optimizing the safety stock taking into account the constraint on the number of admissible stockouts per time unit. We present both exact and approximated minimization algorithms. Numerical examples are finally shown to illustrate the effectiveness of the approximation algorithm, and to investigate the sensitivity of the model with respect to variations in some fundamental parameters. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we consider the continuous review (S − 1, S) policy with complete backordering, deterministic and constant lead-time, and with a Poissonian lead-time demand. Moreover, we take into account two different expressions of the total expected cost depending on the customer-service criterion adopted. First, we approximate the Poissonian lead-time demand with a well-suited Gaussian random variable. Although this system can be easily solved with a simple algorithmic approach, it is interesting to be aware of the direct analytical functional relationship between the optimal value of the decision variable and the parameters of the model. Hence, the second step consists in providing some approximated closed-form minimum-cost solutions for both cost models considered under a Gaussian lead-time demand. An extensive numerical study is finally shown to characterise the precision achieved by the approximations developed.

The present paper is aimed at investigating, through simulation, the behaviour of the POLCA method when a Make-to-Order (MTO) production system is highly unbalanced, in terms of both routings and times. In particular, the study is addressed to verify (i) the effective capability of the POLCA method to improve the uncontrolled system and (ii) the impact of how orders are processed at each workstation, to show that POLCA performance can be further improved in such circumstances by adopting an appropriate selection rule. Germs and Riezebos (2010) proved that POLCA is very effective in reducing the Total Throughput Time (TTT) with respect to the corresponding unconstrained production systems. Owing to this, it appears evident that POLCA represents a valuable and effective Make-to-Order (MTO) production control method. However, they only considered balanced systems, hence some observations must be carefully addressed. In fact, often, in real world applications, the systems to be controlled are designed to process units with very different routings, each with significantly different probability to occur.

In this technical note, an alternative solution procedure to the model that Glock (2011) developed is presented. In particular, we take into account the same assumptions, but the total cost function is reformulated, i.e., the step-type discontinuity is replaced by a logistic approximation and the total costs of the system are computed on the whole set of preselected suppliers. Subsequently, we show that the total cost function possesses some properties in terms of convexity and continuity that allow the exploitation of standard constrained nonlinear minimization algorithms. Finally, tests conducted on the same set of problems that Glock (2011) originally considered illustrate the good performances of the solution procedure developed in this note.

The present paper provides the schema for an innovative and modular computer-based approach to the planning of activities in large-scale projects. Such projects are characterized by tens of thousands of tasks, which are consequently burdensome and difficult to plan manually. This is true to the point that in many shipyards only a low level of detail is used and poor planning is generally performed. The proposed approach is called computer-aided activity planning (CAAP), and an application in the yachting industry is shown to demonstrate its effectiveness. In particular, the so-called outfitting planning problem is faced. The CAAP system, taking into account the available shipyard resources and the knowledge on the building rules is able to automatically define, sequence, and schedule the activities of the whole outfitting process acting as a “planning configurator”. Moreover, it allows the industry-specific knowledge to be stored, maintained and shared within the (extended) organization. Owing to these “building blocks”, plans can be defined accurately and in a shorter time starting from pre-defined templates, with particular impact on lead times whenever variations to complex projects are needed. Finally, to verify the actual capabilities of the approach, the CAAP was implemented within a prototypical software called NautiCAAP.

In this paper a new analytical approach to safety stock management, within single buyer–single vendor framework under VMI with consignment agreement, is presented. In particular, the cost of the safety stock is evaluated adopting a logistic approximation of the standard normal cumulative distribution. The service level is put in relation to the dimension of the single shipment, to the average demand on the buyer and to the number of admissible stockouts. Once the mean joint total cost function is defined, on the basis of the model of Braglia and Zavanella, (2003. Int. J. Prod. Res. 41, 3793–3808) modified according to Braglia et al. (2014. Int. J. Logist. Syst. Manage. forthcoming 2014), a minimum cost solution (within a predefined domain) is derived. Moreover, a numerical study is carried out. First, a sensitivity analysis is shown. Then, the model of Braglia and Zavanella, (2003. Int. J. Prod. Res. 41, 3793–3808), in its original expression, is compared with both Hill (1997. Eur. J. Oper. Res. 97, 493–499), Hill (1999. Int. J. Prod. Res. 37, 2463–2475) and Hill and Omar (2006. Int. J. Prod. Res. 44, 791–800). In particular, the comparison considers the equal-sized shipments case without delayed deliveries taking into account different reciprocal values for the stockholding costs.

Purpose – The purpose of this paper is to present a reliability centered maintenance (RCM) embedded integer linear programming approach (suited to the budget monetary resources allocation task) to the maintenance strategies mix selection for an industrial plant equipment. Design/methodology/approach – The developed approach allows to determine the optimal maintenance strategies mix for a set of equipment in a more quantitative way than the classic RCM approach. The proposed model takes into account, for each potential failure determined using the FMECA and for each admissible strategy, the costs and the potential risk priority number (RPN) reduction. Finally, an industrial case concerning an Italian paper-mill plant is reported to demonstrate the effectiveness of the approach presented. Findings – The paper finds that the application of the proposed approach allows to optimally allocate the budget monetary resources, determining which suitable maintenance practice apply to each failure, taking into account the costs of each strategy and the potential reduction of the RPN. Practical implications – The proposed model permits to assign (during the budget monetary resources allocation task) to each failure the optimal strategy, among a set of suitable maintenance practices, considering the costs and the estimated RPN reduction. Originality/value – The paper proposes a completely new RCM embedded approach to the maintenance strategies selection, in order to optimally allocate the budget monetary resources. This model overcomes the limits of the traditional RCM approach, taking into account quantitative aspects, i.e. the compatibility constraint between failures and policies, the maintenance strategies costs, and the RPN estimated reduction.

This paper aims at presenting a revisited and extended version of the injury theory, originally developed by Ramalingam and Watson (1977) and Ramalingam (1977). This theory allows deriving tool-life distributions when either a single catastrophic event or a gradual and cumulative wear process terminates the tool useful life. Since these models cover great importance in tool-life modelling, this paper would contribute in this direction with further observations and results, giving special attention to the mathematical formulation. In particular, besides deriving known results following a different approach, we extend the theory to the case of multiple machines with both single- and multi-tool configurations. Moreover, as examples we derive different expressions of the tool-life distribution in the cases studied related to particular formulations of the injury rate.

A new analytic formulation of the Vendor Managed Inventory with consignment agreement is proposed. It represents a modified and more realistic version of the model proposed in Braglia and Zavanella (2003). The evaluation of the stockholding related costs is herewith substantially revisited to consider the requirements of a standard VMI relationship with consignment agreement. In particular, the physical space occupation cost held by the buyer is introduced as a component of the overall holding cost and is referred to the maximum stock level, rather than to the average value, as proposed in the past literature. Finally, an implicit analytical solution is given and compared to the original one obtained by Braglia and Zavanella (2003).