An integrated model for sustainable supplier selection and multi-period multi-product lot-sizing for packaging film industry in Iran

The emergence of sustainability issues has created increasing interest among those involved in the field of sustainable supply chain management. Companies are motivated to modify their supply chains activities based on sustainability issues to enhance their overall level of sustainability in order to fulfil demanding environmental and social legislation and to deal with increasing market forces from different stakeholder groups. Within supply chain activities, selecting appropriate suppliers based on the criteria of sustainability, e.g., economic, environmental, and societal might help companies move towards sustainable development. Although several studies have been accomplished to incorporate sustainability criteria into supplier selection problem, little attention has been paid to developing a comprehensive mathematical model that allocates the exact quantities of orders to suppliers considering lot-sizing problems. Moreover, the effect of inflation as an important issue for companies in the developing countries has been neglected in studies that examined multi-period multi-product lot-sizing along with supplier selection. In this study, a multi-objective mathematical model for sustainable supplier selection integrated with multi-period multi-product lot-sizing problem under the effects of inflation was developed. The model consists of four objective functions which are minimizing total cost, maximizing total social, total environmental score, and total economic qualitative scores. The mathematical model was developed based on the parameters discovered by preprocessing the social, environmental, and economic data of suppliers using a rule-based-weighted fuzzy approach and fuzzy analytical hierarchy process. The model attempted to simultaneously balance different costs under inflationary conditions to optimize the total cost of purchasing and other objective functions. A comprehensive framework was developed as a road map for procurement organizations in order to facilitate the allocation of optimal order quantities to suppliers in a sustainable supply chain. The proficiency and applicability of a proposed approach was illustrated using a case study of packaging films from the food industry. For each main criterion of sustainability, their related subcriteria and influencing factors were extracted from literature and the most related ones were selected by company’s experts. In this research, green competencies, environmental management system, pollution, occupational safety and health, training and education, contractual stakeholder, economic qualitative, and cost were selected by company’s experts as the main subcriteria of sustainable supplier selection. The consideration of sustainability criteria in the proposed multi-objective model revealed that a higher value of sustainable purchasing can be achieved in comparison with a single objective costbased model. In addition, the results show that the proposed model can provide a purchasing plan for the company while monitoring the effect of inflation and assuaging its concerns regarding sustainability issues

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Multiproduct supplye chain analysis through by simulation with kanban and EOQ system

This work reviews lean literature on the supply chain focused on the operational approach, from the lean management to the Kanban system. But, the main issue of this work is to analyze the behavior of a lean supply chain using a Kanban system managing the planning in two different ways. The difference between both is related to the production order or sequence to follow: the product with fewer inventories in stock (the most critical to run out) or the one which requires less set-up time to optimize unproductive times. The study the behavior of the supply chain, it would be done through simulation with many different scenarios: 5 different demands, each one with two coefficients of variance, 4 different batch sizes, 4 different compositions of production and process saturation and ensuring different service levels between 92% and 98%. To compare these supply chain models, an approach of the supply chain using the EOQ (Economic Order Quantity) system will be also simulated in the same conditions but with one batch size, the most economic one

Improving the sustainability of coal SC in both developed and developing countries by incorporating extended exergy accounting and different carbon reduction policies

In the age of Industry 4.0 and global warming, it is inevitable for decision-makers to change the way they view the coal supply chain (SC). In nature, energy is the currency, and nature is the source of energy for humankind. Coal is one of the most important sources of energy which provides much-needed electricity, as well as steel and cement production. This manuscript-based PhD thesis examines the coal SC network as well as the four carbon reduction strategies and plans to develop a comprehensive model for sustainable design. Thus, the Extended Exergy Accounting (EEA) method is incorporated into a coal SC under economic order quantity (EOQ) and economic production quantity (EPQs) in an uncertain environment. Using a real case study in coal SC in Iran, four carbon reduction policies such as carbon tax (Chapter 5), carbon trade (Chapter 6), carbon cap (Chapter 7), and carbon offset (Chapter 8) are examined. Additionally, all carbon policies are compared for sustainable performance of coal SCs in some developed and developing countries (the USA, China, India, Germany, Canada, Australia, etc.) with the world's most significant coal consumption. The objective function of the four optimization models under each carbon policy is to minimize the total exergy (in Joules as opposed to Dollars/Euros) of the coal SC in each country. The models have been solved using three recent metaheuristic algorithms, including Ant lion optimizer (ALO), Lion optimization algorithm (LOA), and Whale optimization algorithm (WOA), as well as three popular ones, such as Genetic algorithm (GA), Ant colony optimization (ACO), and Simulated annealing (SA), are suggested to determine a near-optimal solution to an exergy fuzzy nonlinear integer-programming (EFNIP). Moreover, the proposed metaheuristic algorithms are validated by using an exact method (by GAMS software) in small-size test problems. Finally, through a sensitivity analysis, this dissertation compares the effects of applying different percentages of exergy parameters (capital, labor, and environmental remediation) to coal SC models in each country. Using this approach, we can determine the best carbon reduction policy and exergy percentage that leads to the most sustainable performance (the lowest total exergy per Joule). The findings of this study may enhance the related research of sustainability assessment of SC as well as assist coal enterprises in making logical and measurable decisions

Assessment of joint inventory replenishment: a cooperative games approach

This research deals with the design of a logistics strategy with a collaborative approach between non-competing companies, who through joint coordination of the replenishment of their inventories reduce their costs thanks to the exploitation of economies of scale. The collaboration scope includes sharing logistic resources with limited capacities; transport units, warehouses, and management processes. These elements conform a novel extension of the Joint Replenishment Problem (JRP) named the Schochastic Collaborative Joint replenishment Problem (S-CJRP). The introduction of this model helps to increase practical elements into the inventory replenishment problem and to assess to what extent collaboration in inventory replenishment and logistics resources sharing might reduce the inventory costs. Overall, results showed that the proposed model could be a viable alternative to reduce logistics costs and demonstrated how the model can be a financially preferred alternative than individual investments to leverage resources capacity expansions. Furthermore, for a practical instance, the work shows the potential of JRP models to help decision-makers to better understand the impacts of fleet renewal and inventory replenishment decisions over the cost and CO2 emissions.DoctoradoDoctor en Ingeniería Industria

Multi-objective optimisation of dynamic short-term credit portfolio selection :the adoption of third party logistics credit for financing working capital contrained small and medium sized enterprises in supply chains

PhD ThesisMany companies, especially small and medium sized enterprises, are faced with liquidity problems. The shortage of working capital in their businesses has prevented supply chains from achieving effectiveness and efficiency in management. Although they can access short-term loans from banks and suppliers, the willingness of these credit lenders to lend short-term capital is often restricted by the fact that they cannot monitor whether or how their customers will use the loans according to the agreements. In many cases, this fact makes it difficult for capitalconstrained companies to obtain sufficient working capital from existing funding sources. A business practice called Integrated Logistics and Financial Service has been developed, which can improve banks’ monitoring of how their loans will eventually be used via the alliance of third party logistics companies and banks. The emergence of credit offered by third party logistics companies (termed as 3PLC) provides more choices for working capital constrained companies. Following on traditional bank overdrafts and trade credit, the new 3PLC became the third type of credit available to short-term working capital constrained companies. A new issue arising from this situation is how a working capital constrained company can determine a credit portfolio from multiple working capital sources. Current studies of credit portfolio management are still silent in considering 3PLC. Moreover, limited studies have integrated credit portfolio management into material flow management in supply chains. In light of the aforementioned discussions, this thesis aims to optimise dynamic credit portfolio management in supply chains to achieve the different business objectives of working capital constrained companies. To achieve the above aims, this thesis firstly applies an analytic hierarchy process and linear programming model to optimise a single objective. It applies the analytic hierarchy process to evaluate the concerns of working capital-constrained companies in selecting credit. These concerns are identified through a thorough literature review focusing on the considerations of small and medium sized enterprises’ in borrowing short-term credit. The analytic hierarchy process has been applied to determine the priority of the identified concerns and the preferences of borrowers for bank overdrafts, trade credit and 3PLC. A linear programming model has been developed based on the results obtained from the analytic hierarchy process model. It determines the maximum borrowing amount for a given period from multiple credit sources. To reflect the complexity of working capital constrained companies borrowing credit, thisthesis has extended the model from single objective optimisation to multiple objectives optimisation. Consequently, a goal-programming model has been developed. This model provides the solution of optimizing two business objectives including overall cost and backorder penalty cost minimization. Numerical examples have been conducted to test and analyse all the mathematical models. This thesis contributes the following aspects: 1) the new 3PLC together with bank overdraft and trade credit have been considered into credit portfolio management; 2) borrower’s concerns and credit preferences relating to the three types of credit have been identified and evaluated; 3) mathematical models have been developed for credit portfolio selection over multiple periods

Management of working capital in public health care.

Thesis(MBA)-University of Natal, Durban, 2003.Two cases from public health care levels were compared on practises used to manage working capital with two cases from the same levels of health care in private health sector. The objective was to establish whether the practises in public health sector comply with the efficient management of working capital principles and whether it was practically feasible to apply the methods in health care provision. Primary and secondary data was collected. Staffs at an operational and administrative level were interviewed at both the primary and the Secondary health care. It was found that principles of working capital practiced in private sector are mostly consistent with working capital theories and could actually be implemented effectively in public health sector without risking patient health. Inefficiencies were identified in the public sector at both an operational and administrative level especially at a secondary health care level. Finally the study makes recommendations on how to address such inefficiencies

An evaluation of the economic cost impacts of classical forecast errors

Evidence from literature suggests that there is no shortage of studies concerned with the supply chain risk management and the associated performance by the individual echelons and functional business areas or through coordinated efforts. Literature has also demonstrated strong association between the performance of supply chain inventory management and control policies and profitability. Thus, integration of operational policies with financial decisions has been seen as an avenue to improve and to better corporate strategic financial objectives in supply chain sector organisations through optimal inventory investment. This is quite important since measures to improve financial performance implicitly influence and restrict operational performance including the management of inventory. However, on the modelling of inventory and finance and in measuring the impact of one on the other, traditional approaches tend to think of one as the input into the other without due consideration for the interconnections between the two over time. In particular, the traditional inventory cost model appears to present a disconnect between operational choices and financial decisions. This thesis models both and their interconnections explicitly and simultaneously. Supposing a periodic review inventory policy with finite horizon and single perishable product, this study proposes a simple easy to understand solution. Specifically, in evaluating the economic consequences of classical forecast error metrics on inventory control system, study improves the current approach by creating a versatile consolidative costs evaluation function that aligns both operational and financial decisions as well as captures the business contextual considerations. The research study results revealed that we can easily utilise the proposed robust costs structure at the right scale (of demand uncertainty) and in the right scope (of financial capacity) to reveal the real and correct cost effects that facilitates users to produce practically feasible plans for their businesses