Marta Rinaldi

Modeling and multi-objective optimization of closed loop supply chains

The optimization of the asset management process in a closed-loop supply chain is investigated.A simulation model, based on an adapted EOQ policy, is developed to reproduce the assets flow.A multi-objective optimization, including both cost and strategic KPIs, is carried out on the closed-loop supply chain.The optimal configuration of the asset management process is identified for each scenario simulated. This study investigates the issue of optimizing the asset management process in a real closed-loop supply chain (CLSC), consisting of a pallet provider, a manufacturer and 7 retailers. A detailed simulation model, based on an adapted economic order quantity (EOQ) policy is developed under Microsoft Excel to reproduce the reorder process of assets by the manufacturer and the corresponding flow of returnable transport items (RTIs) in the CLSC. A multi-objective optimization, including both economic and strategic key performance indicators of the system, is then carried out exploiting the commercial software ModeFRONTIER. The optimization investigates three scenarios, which refer to as many operating conditions of the manufacturer. Scenario 1 basically reproduces the current operating conditions of the manufacturer, while scenarios 2 and 3 are both hypothetical, and describe situations where the manufacturer would like to minimize the purchase of new assets and the pick-up of assets from its customers, respectively. For each scenario, the optimal configuration (i.e., the setting of the asset management process that performs best in the multi-objective optimization) is identified. Scenarios 1 and 3 are found to generate the most interesting performance of the assets management process, from both the economic and strategic perspectives. Because the present paper is grounded on a real CLSC, the results are expected to be useful to logistics and supply chain managers, to support the evaluation of the performance of CLSCs.

Improving the efficiency of public administrations through business process reengineering and simulation: A case study

Purpose – The purpose of this paper is to propose a business process reengineering (BPR) approach to a public administration of Italy, to first assess the efficiency of the administration, then to redesign its internal processes, to improve the current performance. Design/methodology/approach – A detailed mapping of the AS IS processes of the public administration was initially carried out, together with the collection of the relevant data. Then, a simulation model was designed to support the BPR approach. In particular, the model was exploited to assess the performance of the AS IS scenario of the organization, then to investigate numerous TO BE process configurations and evaluate the achievable performance improvements. Findings – From the study, it emerged that the current efficiency level of the public administration examined has potentials to be significantly improved. For instance, by maintaining its current workforce, the public administration could consider the opportunity of providing additional ...

Analysis and optimisation of inventory management policies for perishable food products: a simulation study

In this paper, we analyse three traditional reorder policies, namely economic order interval (EOI), economic order quantity (EOQ) and (S, s), applied to five food products with different shelf-life characteristics; three fresh products with limited shelf-life are considered. An ad hoc simulation model, reproducing a real two-echelon supply chain, was developed under Microsoft ExcelTM to simulate the product flow along the supply chain, according to the three policies. From the simulation, the minimum cost setting is first derived for all policies. Then, additional performance parameters (e.g., the throughput time of items) are computed and compared with the products constraints (e.g., the shelf-life), to assess the real suitability of implementing each policy to the products considered. Because both the supply chain modelled and the products data are derived from a real scenario, our outcomes should be of practical usefulness to inventory managers, to optimise inventory management of perishable products.

Intelligent Algorithms for Warehouse Management

Warehouses are important links in the supply chain; here, products are temporarily stored and retrieved subsequently from storage locations to fulfill customer’ orders. The order picking activity is one of the most time-consuming processes of a warehouse and is estimated to contribute for more than 55 % of the total cost of warehouse operations. Accordingly, scientists, as well as logistics managers, consider order picking as one of the most promising area for productivity improvements. This chapter is intended to provide the reader with an overview of different intelligent tools applicable to the issue of picking optimization. Specifically, by this chapter, we show how different types of intelligent algorithms can be used to optimize order picking operations in a warehouse, by decreasing the travel distance (and thus time) of pickers. The set of intelligent algorithms analyzed include: genetic algorithms, artificial neural networks, simulated annealing, ant colony optimization and particles swarm optimization models. For each intelligent algorithm, we start with a brief theoretical overview. Then, based on the available literature, we show how the algorithm can be implemented for the optimization of order picking operations. The expected pros and cons of each algorithm are also discussed.

Performance Analysis of the Water Supply System of a Dairy Company by Means of an Advanced Simulation Tool

Abstract This work presents the result of a research project, concerning the development of a simulation model for a water supply system of a dairy company, located in Parma, Italy. The approach developed allows investigating, through process simulation, the plant areas where the efficiency of the water supply system can be significantly improved by means of some simple interventions. The final objective is to reduce the water consumption of the plant; this represents a relevant issue to the dairy industry. In line with this goal, at first the simulation model was used to reproduce the current (AS IS) system, so as to reach a precise knowledge of the water flows in the plant. In the second part of the work, a series of alternative (TO BE) scenarios was investigated, and the related performance was assessed, thus identifying the best plant configuration. The process simulator was designed under Microsoft Excel, programmed with Visual Basic for Applications. Thanks to the study implemented, an optimal scenario of the water supply system was finally identified, which allows savings up to 7.2% of water compared to the original configuration.

Investigating the demand propagation in EOQ supply networks using a probabilistic model

In this paper, we introduce a new demand probabilistic approach, named M.DPA.eoq (Montanari Demand Probabilistic Approach in economic order quantity [EOQ] scenario), for predicting the demand seen by an upper tier echelon (e.g. a distribution centre) of a supply network, serving several lower tier echelons operating according to an EOQ reorder policy. The M.DPA.eoq is based on an analytic approach, by which we derive the distribution of the demand seen by the upper tier echelon of the supply network. The approach has been designed to be very simple, so as to gain in pedagogical value. The simplicity and ease of application of this approach are confirmed by the possibility of exploiting general purpose software, such as Microsoft ExcelTM, to implement and validate it. Moreover, the M.DPA.eoq has potential to be directly exploited by practitioners, such as supply network managers, to estimate the distribution of the demand the upper tier echelon will face under a defined network structure. Students and researchers could also benefit from such a model, given its ease of understanding and usage. With the purpose of showing its potential usefulness in real cases, we discuss two practical implications of the M.DPA.eoq, referring to the use of its results for: (1) computing the bullwhip effect of the network; and (2) analysing the impact of each retail store on the variance of the demand seen by the upper tier echelon.

Temperature Analysis of the Water Supply System of a Dairy Company by Means of a Simulation Model

Abstract This paper builds upon the study by Marchini et al. (2014, International Journal of Food Engineering, pp. 557–571) and represents the second part of a research project whose general aim was to analyse and optimize, through simulation, the water distribution system of a dairy company. In the first paper, the authors focused on finding opportunities for recycling water in the distribution system of the targeted company, i.e. a dairy company located near Parma (Italy) and manufacturing Parmigiano Reggiano cheese and butter. In this work, we go ahead by analysing the thermal properties (such as, primarily, the temperature) of the water used inside the distribution system and of that discharged. To this purpose, we add the relevant thermal equations to the MS ExcelTM simulation model developed in the first study. The resulting model is able to derive the temperature trend of water inside all the tanks of the distribution system. Situations where the temperature of the water used for rinsing is higher than the maximum allowed value of approx. 25°C are also highlighted and suggestions for improvement are proposed.

Analysis, Simulation and Optimization of the Milking Process in a Cowshed for the Production of Parmigiano Reggiano

Abstract The aim of this study is to optimize the milking process of a cowshed, located near Parma (Italy), which provides milk to some dairy companies, for the production of Parmigiano Reggiano cheese. The ultimate goal of the analysis is to reduce the time required for milking operations, thus optimizing the whole management of the farm processes. A discrete-event simulation model is designed under Simul8™ to reproduce the main processes of the cowshed and the movements of the animals inside the cowshed, before and after milking. The model exploits real data collected from the direct observation of the farm and is validated by comparing the results provided with the real performance of the milking activities. Then, it is used to test two new configurations of the cowshed layout and to assess their performance, in terms of the total time required for milking. Interesting savings in the total milking time are found.