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Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the operations that do not add value to the product or processes to increase the value of each performed activity. Here the main motivation is to study how quantitative modelling approaches can support LM tools even under system and environment uncertainties. The main contributions of the article are: (i) providing a systematic literature review of 99 works related to the modelling of uncertainty in LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifying LM works under uncertainty; and (iv) identify quantitative models and their solution to deal with uncertainty in LM environments by identifying the main variables involved. Hence this article provides a conceptual framework for future LM quantitative modelling under uncertainty as a guide for academics, researchers and industrial practitioners. The main findings identify that LM under uncertainty has been empirically investigated mainly in the US, India and the UK in the automotive and aerospace manufacturing sectors using analytical and simulation models to minimise time and cost. Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce waste in a context of system uncertainty. [ABSTRACT FROM AUTHOR]

Operations management researchers and practitioners have shown increasing interest in incorporating sustainability into supply chain (SC) design models. This means that sustainability must be considered in all aspects of the SC, including location, inventory and transportation (LIT) decisions. Hence the aim of this article is to propose an optimisation model that incorporates: (i) LIT decisions in an integrated manner; (ii) the three sustainability (3S) aspects, i.e. economic, environmental and social, into each named decisions; and (iii) a closed-loop supply chain (CLSC) structure. The proposed formulation is a multi-objective mixed integer non-linear programming (MO-MINLP) model whose objectives consider minimisation of economic and social costs (economic aspect) and carbon emissions (environmental aspect), and maximisation of the social impact of SC operations (social aspect). A transformation technique is applied to one of the objective functions, which results in an MO-MILP model solved by the lexicographic method. This article focuses on commodity industries where only one finished product is manufactured. Hence the 3S-LIT model is validated with a randomly generated dataset and against a recently published alternative model applied to the copper mining industry. [ABSTRACT FROM AUTHOR]

Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation. [ABSTRACT FROM AUTHOR]

This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform. [ABSTRACT FROM AUTHOR]

The upcoming logistic environment is about to modify deeply the way we supply products. In fact, some new trends are going to require more and more agility between a large number of stakeholders in open and dynamic networks. This should be possible to achieve thanks to new data collection and treatment abilities. Considering this moving technological and logistic environment, it appears necessary to define and categorize more specifically the main disruptive events that can affect a supply chain. In fact, amount of data are collected on the field and must be helpful to make relevant decisions in case of disruption. In order to understand automatically what these data mean, it is necessary to detect and classify the disruptive events in order to find the best adaptation. This paper focuses on the sensitive products' supply chains, that are facing with agility high requirements, based on their ability to detect disruptive events. We take as an example the blood supply chain. [ABSTRACT FROM AUTHOR]

The goal of C2NET European H2020 Funded Project is the creation of cloud-enabled tools for supporting the SMEs supply network optimization of manufacturing and logistic assets based on collaborative demand, production and delivery plans. In the scope of C2NET Project, and particularly in the Optimisation module (C2NET OPT), this paper proposes a novel holistic mixed integer linear programing (MILP) model to optimise the injection sequencing in a multi-machine case. The results of the MILP will support the production planner decision-making process in the calculation of (i) moulds setup in certain machines, and (ii) the amount of products to produce in order to minimise the setup, inventory, and backorders costs. The designed MILP takes part of the algorithms repository created in C2NET European Funded Project to solve realistic industry planning problems. The MILP is verified in realistic data considering three data sets with different sizes, in order to test its the computation efficiency. [ABSTRACT FROM AUTHOR]

Researchers in the area of collaborative networks are more and more aware of proposing collaborative approaches to address planning processes, due to the advantages associated when enterprises perform integrated planning models. Collaborative production-distribution planning, among the supply network actors, is considered a proper mechanism to support enterprises on dealing with uncertainties and dynamicity associated to the current markets. Enterprises, and especially SMEs, should be able to overcome the continuous changes of the market by increasing their agility. Carrying out collaborative planning allows enterprises to enhance their readiness and agility for facing the market turbulences. However, SMEs have limited access when incorporating optimization tools to deal with collaborative planning, reducing their ability to respond to the competition. The problem to solve is to provide SMEs affordable solutions to support collaborative planning. In this regard, new optimisation algorithms are required in order to improve the collaboration within the supply network partners. As part of the H2020 Cloud Collaborative Manufacturing Networks (C2NET) research project, this paper presents a study on integrated production and distribution plans. The main objective of the research is to identify gaps in current optimization models, proposed to address integrated planning, taking into account the requirements and needs of the industry. Thus, the needs of the companies belonging to the industrial pilots, defined in the C2NET project, are identified; analysing how these needs are covered by the optimization models proposed in the literature, to deal with the integrated production-distribution planning. [ABSTRACT FROM AUTHOR]

Collaboration is a trend in supply networks management, based on the jointly planning, coordination and integration of processes, participating all network entities. Due to the current characteristics of uncertainty in the markets and economic crisis, there is a need to encourage collaboration tools to reduce costs and increase trust and accountability to market requirements. This study presents an overview of the research carried out in the H2020 European Project: Cloud Collaborative Manufacturing Networks (C2NET), which is directed towards the development a cloud platform that consist of, optimization tools, collaboration tools to support and agile management of the network. The collaborative cloud platform allows to collect real time information coming from real-world resources and considering all the actors involved in the process. The collaborative cloud provides real time data gathered from the entire network partners in order to improve their decision-making processes. [ABSTRACT FROM AUTHOR]

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This article presents an overview of the models applied to sustainable inventory management in supply chains and a roadmap for new research. It aims to address the lack of understanding of how sustainability is being incorporated into quantitative inventory management models in the supply chain context. The study is based on a classification of the reviewed literature according to the following criteria: supply chain structure, environmental approach, problem type, modeling, and solution approach. As a result, 36 articles were analyzed and classified. The main findings show that studies that incorporate social sustainability into inventory management along supply chains are lacking, while environmental studies are a growing research area. Uncertainty issues also need to be incorporated into sustainable inventory management models. Another important result of this study is the definition of a roadmap with trends and future research guidelines. The identified future research guidelines include incorporating decisions that can help to improve economic, environmental, and social sustainability. Thus, future studies should focus on both following quantitative models that incorporate inventory decisions integrally with transportation and location decisions, and more complex models, and employing new algorithms and heuristics to solve them. [ABSTRACT FROM AUTHOR]

Disposition Decision (DD) consists of deciding how to treat a recovered product, and it is one of the most important decisions in reverse logistics. Any of the selected disposition alternatives will have a significant impact on the enterprise sustainability. However, the most sustainable alternative may not be an alternative to make circular economy (CE) possible. In these cases, if the company wishes to adopt a CE strategy, it will have to switch from the most sustainable alternative to a less sustainable one that CE allows. Then, how much should be sacrificed for each sustainability dimension to make CE possible? This paper proposes a methodology for quantitatively comparing the most sustainable disposition alternative and the most sustainable CE alternative. This comparison allows small and medium enterprises (SMEs) to know how exactly all dimensions increase or decrease when selecting the most sustainable CE disposition alternative and to, therefore, assess the interest of adopting a CE policy. The proposed methodology is applied to a used tire recovery company. The results of this example show that the CE alternative offers a better environmental result but presents worst economic and social results. This example can be used as a guide for future applications other SMEs. [ABSTRACT FROM AUTHOR]

In today's volatile business arena, companies need to be resilient to deal with the unexpected. One of the main pillars of enterprise resilience is the capacity to anticipate, prevent and prepare in advance for disruptions. From this perspective, the paper proposes a mixed-integer linear programming (MILP) model for optimising preparedness capacity. Based on the proposed reference framework for enterprise resilience enhancement, the MILP optimises the activation of preventive actions to reduce proneness to disruption. To do so, the objective function minimizes the sum of the annual expected cost of disruptive events after implementing preventive actions and the annual cost of such actions. Moreover, the algorithm includes a constraint capping the investment in preventive actions and an attenuation formula to deal with the joint savings produced by the activation of two or more preventive actions on the same disruptive event. The management and business rationale for proposing the MILP approach is to keep it as simple and comprehensible as possible so that it does not require highly mathematically skilled personnel, thus allowing top managers at enterprises of any size to apply it effortlessly. Finally, a real pilot case study was performed to validate the mathematical formulation. [ABSTRACT FROM AUTHOR]

Enterprise resilience is a key capacity to guarantee enterprises' long-term continuity. This paper proposes the enterprise resilience Conceptual Reference Framework to characterize enterprise resilience capacity. The framework is composed of 71 disruptive events that enterprises consider as endangerments to their continuity. The framework also comprises constituent capabilities of enterprise resilience in terms of preparedness and recovery capabilities and elements that support the transition from the AS IS situation to the TO BE one, which are preventive actions (for preparedness capability) and knowledge registration actions (for recovery capability). From the preparedness perspective, 403 preventive actions are currently defined. Each preventive action is specific for every disruptive event. However, it is worth noting that a preventive action can also be applied to different disruptive events. From the recovery perspective, the proposed framework indicates knowledge registration related to (i) the occurrence of disruptive events; (ii) the recovery actions performed to re-establish the normal enterprise operation level. Further research lines are addressed to develop quantitative methods and tools to assess the extent of enterprises' resilience following the foundations of the proposed conceptual framework. [ABSTRACT FROM AUTHOR]

Nowadays, collaborative knowledge management (CKM) is well accepted as a decisive asset in the field of networked enterprises and supply chains. However, few knowledge management initiatives have been performed successfully because, in most cases, the barriers that hinder the CKM process are unknown and misunderstood. Currently, the research reveals different uni- and bi-dimensional barriers' classifications, however multi-dimensional approaches provide a better view of the complexity in the area of CKM. Therefore, this paper proposes the three-dimensional matrix of collaborative knowledge barriers taking into account: (i) perspectives; (ii) levels and (iii) barriers blocks to provide a reference way to audit the CKM barriers, and thus, in further research, focus on the corrections and adjustments to guarantee the success while implementing a CKM project. [ABSTRACT FROM AUTHOR]

Currently, the management of water networks is key to increase their sustainability. This fact implies that water managers have to develop tools that ease the decision-making process in order to improve the efficiency of irrigation networks, as well as their exploitation costs. The present research proposes a mathematical programming model to optimize the selection of the water sources and the volume over time in water networks, minimizing the operation costs as a function of the water demand and the reservoir capacity. The model, which is based on fuzzy methods, improves the evaluation performed by water managers when they have to decide about the acquisition of the water resources under uncertain costs. Different fuzzy solution approaches have been applied and assessed in terms of model complexity and computational efficiency, showing the solution accomplished for each one. A comparison between different methods was applied in a real water network, reaching a 20% total cost reduction for the best solution. [ABSTRACT FROM AUTHOR]

Enterprise resilience is a key capacity to guarantee enterprises' long-term continuity. This paper proposes a quantitative approach to enhance enterprise resilience by selecting optimal preventive actions to be activated to cushion the impact of disruptive events and to improve preparedness capability, one of the pillars of the enterprise resilience capacity. The proposed algorithms combine the dynamic programming approach with attenuation formulas to model real improvements when a combined set of preventive actions is activated for the same disruptive event. A numerical example is presented that shows remarkable reductions in the expected annual cost due to potential disruptive events. [ABSTRACT FROM AUTHOR]