Impact of inventory management on demand fulfilment, cost and emission of forest-based biomass supply chains using simulation modelling

Akhtari, Shaghaygh; Sowlati, Taraneh; Siller-Benitez, Diana G.; Röeser, Dominik

doi:10.1016/j.biosystemseng.2018.11.015

Cited by 44 publications

(11 citation statements)

References 28 publications

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“…Berger et al 37 developed a simulation model for comparing four different strategies of inventory management. Akhtari et al 28 used a discrete-event simulation model to compare demand fulfillment and cost for two continuous inventory systems: (1) order-up-to-level and (2) fixed order quantity. Thammatadatrakul and Chiadamrong 34 proposed a hybrid simulation for investigating inventory control policies in remanufacturing systems.…”

Section: A Review Of Inventory Simulationmentioning

confidence: 99%

“…Corlu et al 9 developed inventory system simulations to quantify the amount of demand parameter uncertainty and then used this quantification to understand the mean service level variance. Akhtari et al 28 developed a discrete-event simulation model to address biomass inventory management and compare demand fulfillment, cost, and emission of a forest-based biomass supply chain.…”

Section: A Review Of Inventory Simulationmentioning

confidence: 99%

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Modeling and simulation for inventory management of repairable items in maintenance systems

Osman

2022

SIMULATION

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Simulation models have become promising methods to investigate and improve real-world systems. This paper describes the development of effective simulation-based models for maintenance systems in a manufacturing environment whereby production lines send their faulty but repairable items to a repair shop. The proposed models aim at investigating the centralized storage of repaired items with three replenishing strategies, lot-for-lot ( L4L), economic order quantity and reorder point ( Q-R), and minimum-maximum order level ( s-S) reordering policies of repairable items with backordering allowance in a risk-free and flexible manner to effectively manage the procurement of new repairable items and control three inventories of repairable items, repaired ready-for-use, new, and faulty awaiting repair. The models take into account the uncertain environment of maintenance systems and the probabilistic nature of the failures, repair times, and lead times of repairable items. The demand for repairable items in these three inventories is both stochastic and dynamic. A simulation case problem is presented to demonstrate the suitability and applicability of the proposed simulation models and to study the influence of different parameters on performance measures using parametric analysis. The main results are the vital decision-making insights provided by the developed models about the three reordering policies and the three inventories of repairable items. These managerial insights are essential for achieving minimum inventory costs and maximum service levels.

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Section: A Review Of Inventory Simulationmentioning

confidence: 99%

Section: A Review Of Inventory Simulationmentioning

confidence: 99%

Modeling and simulation for inventory management of repairable items in maintenance systems

Osman

2022

SIMULATION

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“…It was found that integral harvesting is the best strategy for supply of the residues in terms of cost (5.90 USD/dry t), energy inputs (56.89 MJ/dry t), and CO 2 emissions (4.18 kg CO 2 /dry t) (Lozano-Moreno and Maréchal, 2019). Akhtari et al (2019) used the AnyLogic software to develop a dynamic simulation model to compare demand fulfilment, cost, and emissions of a forest-based BSC for two inventory systems by considering multiple conversion facilities that use different types of biomass.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

An investigation of the environmental sustainability of palm biomass supply chains via dynamic simulation modeling: A case of Malaysia

Zahraee

Golroudbary

Shiwakoti

et al. 2019

Journal of Cleaner Production

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With the ever-increasing need for clean and accessible energy sources, biomass has long been considered as a potential prospect. Malaysia is one of the leading suppliers and has the largest palm oil plantation in the world. Therefore, biomass from the palm oil industry appears to be a very promising alternative source of raw materials, including for renewable energy, in Malaysia.However, ensuring the environmental sustainability of the biomass supply chain (BSC) is one of the major challenges faced by the biomass industry. Therefore, this paper aims to assess the effect of changing the efficiency of transportation technology (TT) and production technology (PT) on the environmental sustainability of the palm oil BSC. We developed a dynamic simulation model to explore the greenhouse gas (GHG) emissions for 50 years (2000-2050) for three palm biomass suppliers (Johor, Perak and Pahang) in Malaysia. Results show that supplier in Perak produced the highest rate of GHG emissions in the whole system. Also, suppliers in Johor and Perak had the highest GHG emissions with 180-240 million tonnes (mt) CO 2 equivalent (CO 2 e) and 375-400 mt CO 2 e, respectively, from transportation of empty fruit bunches (EFB) product. In terms of production, the supplier in Perak had the highest (and constant value) of GHG emissions (up to 160 mt CO 2 e). The analysis shows that the supplier in Pahang generated the lowest level of GHG emissions. Our findings suggest that improvement of the transportation and production efficiency of BSC should be the priority to work towards the sustainable development of the palm biomass industry.

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“…Data from multiple sources were collected and stored in a big data warehouse, then analyzed and used by a simulation model. Akhtari et al [25] analyzed the impact of two inventory systems, order up to level and fixed order quantity, in a forest-based biomass supply chain in Canada. Cost, demand fulfilment, and CO 2 emissions were used as key performance indicators.…”

Section: Simulation Model For Supply Chain Managementmentioning

confidence: 99%

Advanced Methodologies for Biomass Supply Chain Planning

Duc

Nananukul

2019

Processes

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Renewable energy resources have received increasing attention due to environmental concerns. Biomass, one of the most important renewable energy resources, is abundant in agricultural-based countries. Typically, the biomass supply chain is large due to the huge amount of relevant data required for building the model. As a result, using a standard optimization package to determine the solution for the biomass supply chain model might not be practical. In this study, the focus is on developing and applying advanced methodologies that can be used to determine a solution for the biomass supply chain model efficiently. The decisions related to plant selection, and distribution of biomass from suppliers to plants require optimization. The methodologies considered in this research are based on stochastic programming, parameter search, and simulation-based optimization. Computational results and managerial insights based on case studies from different regions of Vietnam are provided. The results show that parameter search is suitable for small problems only, while stochastic programming is suitable for small and medium problems. For large problem, simulation-based optimization performs better when considering the quality of the solution and the run time, although, this method does not guarantee an optimal solution. It provides good solutions where the gaps to the optimal solutions are between 0.59% and 8.41%.

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Impact of inventory management on demand fulfilment, cost and emission of forest-based biomass supply chains using simulation modelling

Cited by 44 publications

References 28 publications

Modeling and simulation for inventory management of repairable items in maintenance systems

Modeling and simulation for inventory management of repairable items in maintenance systems

An investigation of the environmental sustainability of palm biomass supply chains via dynamic simulation modeling: A case of Malaysia

Advanced Methodologies for Biomass Supply Chain Planning

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