Giovanni Mummolo

Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry

Abstract The paper deals with lot sizing and scheduling problem (LSSP) of a multi-site manufacturing system with capacity constraints and uncertain multi-product and multi-period demand. LSSP is solved by an hybrid model resulting from the integration of a mixed-integer linear programming model and a simulation model. The hybrid modelling approach is adopted to test a local as well as a global production strategy in solving the LSSP concerned. The model proposed is applied to a supply chain of a multi-site manufacturing system of braking equipments for the automotive industry. Solutions obtained by the hybrid model under the local or the global production strategy are compared with an actual reference production plan. The approach could help decision making in adopting a cooperative, rather than competitive, production strategy.

A loss factor based approach for sustainable logistics

Logistics plays a key role for industries since it reveals a critical function designed and managed to pursue economic goals. A large amount of literature is available, providing models which can be used to minimise logistic costs. However, models usually neglect externalities, i.e. social and environmental costs that have to be considered in the framework of sustainable manufacturing. In this article, the authors propose an inventory model to support decision making in means of transport selection and order lot sizing (sustainable order quantity) which minimise both logistic and environmental costs. Dependency of logistic and environmental costs is shaped as functions of the ‘loss factor’, a parameter adopted to classify means of transport based on loss in transport energy. Time and cost constraints as well as the actual means of transport availability reduce the domain of theoretical solutions and limit the set of feasible ones. When there are no feasible solutions, three different strategies are identified and discussed to support logistic management in meeting sub-optimal feasible logistic performance under time, cost and means of transport constraints.

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

A real options-based model to supporting risk allocation in price cap regulation approach for public utilities

Price cap regulation of public utilities is based on an incentive mechanism to prevent monopolistic infrastructure firms from charging excessive prices. The challenge of this regulation mechanism is to define incentives able to avoid abnormal profits of firms and simultaneously increase quality of service and promote investment projects. A new risk-based approach to support the definition of the fair incentive mechanism as between the regulator, the community and the firm is proposed. The methodology is based on the combined use of real options theory and Monte Carlo simulation. The methodology is then applied to the Italian water market where the regulator adopts a ‘hybrid’ price cap mechanism that gives monopolistic firms the incentive to implement investment projects for reducing the actual infrastructural gap in the water supply system. The results reveal the capability of the proposed model to support public decision makers at the negotiation stage to define the incentive scheme and investment plan able to increase the quality of service allowing a fair risk allocation among parties.

Modelling dynamics of a supply chain under uncertainty: a case from the automotive industry

The authors investigate a three-stage supply chain of a multinational firm in the automotive components industry. Factories, located in Italy, carryout the manufacturing process of components for braking equipment. The first two manufacturing sites provide the final assembly site with components for original equipment (OEC); they also produce components for the aftermarket (AMCs). The demand for OECs is dynamic and is deterministically known on a one-year horizon. On the other hand, the demand for AMCs is uncertain and is distributed according to probability density functions. To face such complexity in evaluating the supply chain performance, a dynamic and stochastic simulation model is proposed. Two different scenarios are investigated according to whether the manufacturing sites are onsidered as independent business units or as units that obey strict production requirements of the supply chain. The results obtained confirm the effectiveness of the model, which reveals a suitable tool for tactical/strategic decision making.

A Sustainable Order Quantity Model under Uncertain Product Demand

Abstract The Economic Order Quantity (EOQ) model is one of the most known classical continuous review inventory model based on the trade-off between holding and order costs. In actual logistic systems, transport costs play a key role, and in case of stochastic variability of product demand and/or supply lead time, a safety stock is required. Transport and safety stock costs affect the solution of the lot size problem. Recently, wide attention is being paid to sustainable manufacturing and in particular on negative effects of transport. Transports are the main source of air pollutant emissions in EU and are expected to increase in magnitude in the future. Costs evaluations of transport emissions are defined in EU documents based on Kyoto protocol. When transport environmental costs are considered in the logistic cost function a new lot-sizing problem can be defined. In (Digiesi, Mossa and Mummolo 2012), the Sustainable Order Quantity (SOQ) analytical model is proposed aiming at identifying optimal lot-size and transport means which minimize the logistic and environmental costs in case of a deterministic product demand. In this paper, the authors propose an SOQ model in case of stochastic variability of product demand. Analytical model solutions rely on sustainable (in terms of both logistic and environmental costs) order size, transport means and safety stocks. Results obtained in a full case study from automotive supply chain case study are presented.

Special issue on “sustainable manufacturing”

Many issues are presently contributing to a greater attention to sustainability. Among them, we can consider the growing cost of energy, the pollution generated by industrial, transportation and civil activities, the shortages in the availability of strategic raw materials, the social concerns for the environment generated by large disasters, such as massive losses of oil in the sea or toxic emissions from industrial accidents, etc. The best known definition of sustainability goes back to the report published on 1987 by the Brundtland Commission, which introduced sustainable development as ‘. . . the development that meets the needs of the present, without compromising the ability of future generations to meet their own needs’. Certainly, sustainability will be a main matter of attention for the future, under the social, political and industrial perspectives. Today, the aims of sustainable development are pursued with increasing world-wide attention by governments, industries as well as by research and educational institutions. Within the sustainable development concept, sustainable manufacturing represents an important area, given the importance of manufacturing in the modern societies and considering its big impact from the point of view of energy consumption, use of physical resources and emissions to the environment. As such, sustainable manufacturing practices can be considered important elements in the implementation of the great picture of sustainable development. Sustainable manufacturing can be defined as the set of technical and organisational solutions contributing to the development and implementation of innovative methods, practices and technologies, in the manufacturing field, for addressing the world-wide resources shortages, for mitigating the excess of environmental load and for enabling an environmentally benign life cycle of products. Traditionally, environment, society, and economy are considered the three main dimensions of sustainability. However, when focusing on sustainable manufacturing, the role of technology should also be considered, both for its importance as an integral component of the human way of living and for the role it can play in developing a sustainable approach to manufacturing. In this regard, research plays a strategic role in providing knowledge to innovate products, processes, production systems, industrial organisations and for introducing new business models for achieving sustainability in manufacturing. Science-based disciplines and, in particular, those referring to the area of industrial engineering can contribute to transform research results in innovative solutions for industry, which are compliant with the limits of the environment in an efficient way. In some recent editorials, Stephen Childe, the editor of PPC, has considered how the launch of the Gillette razor in 1903, with its disposable blade, was the starting point of the disposable era that created a business model in which the consumer would repeatedly buy supplies of manufactured items, and subsequently throw them away. A hundred years later the sustainability issues are making us question whether it is sustainable to dispose of items and replace them with new ones. Probably this model will not continue without receiving strong criticism. However, the change problem is not trivial. As Childe underlines, the pursuing of efficiency and efficacy was always made in factories, because manufacturers try to limit the use of resources for reducing their production costs, but, on the other side, the current business model for manufacturing pushes companies to shorten the life-cycles of products and to encourage customers to change to the latest product model and in this way, contributing to an useless waste of resources. On the contrary, a sustainability approach in manufacturing should not only minimise the waste of resources both internally to the factory and externally in the logistic of supply chains (thus improving sustainability from the beginning of the life of the product), but also consider the remaining parts of the product’s life-cycle (i.e. the operating life and the end of life). From a manufacturing point of view this means to consider all services related to the product operating life (i.e. maintenance, re-building, upgrading) and all processes and services related to the end of

The future for industrial engineers: education and research opportunities

EU graduation and the recruitment of industrial engineers (IEs) have been investigated. An increasing demand is observed for graduates in almost all industrial engineering (IE) subjects. The labour market in the EU is evolving towards the service sector even if manufacturing still represents a significant share of both IE employment and gross domestic product. On average, IE in the EU is still within the framework of the ‘market-driven’ paradigm, which contrasts with the knowledge society aims pursued by the ‘Bologna process’. R&D resources and human capital are identified as major success factors to overcome current limits for IE development in the EU. With reference to both factors, a comparison between the EU, Japan and the US is provided. In the EU, the attractiveness of universities and the social dimension are recognized as major forces attracting human capital. Patent maps outline current and future IE research and education fields of interests. Finally, EU higher education opportunities are briefly described.

Technical-economic optimization of a wind power plant by means of a stochastic analytical model

Stochastic analytical approaches are particularly suitable to design and study engineering systems based on aleatory information, as wind systems are. The present paper provides a stochastic analytical model to evaluate the performance of a wind power plant. The model has been applied to a small isolated wind system. The results obtained indicate a strong influence of wind speed stochastic variability on the minimum production costs turbine size. The results also point out the economic advantage in the use of a stochastic analytical approach in wind system design compared with a design procedure based on system average performances evaluated according to a determinist approach.

Strategic Energy Planning of Residential Buildings in a Smart City: A System Dynamics Approach

Buildings are the largest urban energy consumers, but their impact can be largely cut back by improving efficiency. Policy-making plays a crucial role in harmonizing national and local incentive schemes. The authors analyse variables related to energy consumption, then propose a simulation model based on System Dynamics applied to a medium-sized Italian city. The model allows the testing of “what-if” scenarios and analysis of the results of implementing energy efficiency policies. Results stress the importance of a holistic view of urban energy processes. Simulation trends provide essential information for the citys future energy and carbon emission profiles, helping policymakers to achieve their goal.