Offsite construction supply chain strategies for matching affordable rental housing demand: A system dynamics approach
استراتژی های زنجیره تأمین ساخت و ساز خارج از ساختمان برای مطابقت با تقاضای مسکن اجاره ای ارزان قیمت: رویکرد پویایی سیستم-2021
Australian housing affordability is influenced by both housing supply and demand factors. These factors include lengthy construction and planning process. The affordability crisis affects the housing rental sector, which accommodates more than 20 % of Australian household. This research developed a system dynamics model to simulate demography-linked affordable rental housing demand and supply in South East Queensland (SEQ). A Prefabricated Offsite Construction (OSC) housing supply strategy is compared with a traditional building approach (BAU) to investigate the effectiveness of OSC techniques to reduce informational asymmetries during development planning stages to deliver better affordable rental housing is linked to housing needs in SEQ. The model focuses on demographic groups housing demand of one, two- and three-bedroom apartment units and examines how reductions in the development process, through OSC methods, influence the efficiency of Government supported affordable rental housing supply schemes. Overall, the study finds that reduced planning and construction timeframes through OSC methods may improve demography-linked rental housing supply by approximately 6.6 % overall compared to BAU in SEQ. For 1,2- and 3-bedroom apartment demand, OSC strategies are expected to improve supply efficiency by 8.7 %, 8.4 % and 9.2 %, respectively. Optimal OSC strategies were assessed and found that flexibility in development sizes have an outsized positive effect. The study has implications for Government supported affordable rental housing strategies, including the build to rent sector.
Keywords: Offsite construction | Sustainable development | Social resilience | Affordable housing policy | Urban systems modelling | System dynamics | Prefabrication
Paradoxes and mysteries in virus-infected supply chains: Hidden bottlenecks, changing consumer behaviors, and other non-usual suspects
پارادوکس ها و رمز و رازها در زنجیره های تأمین آلوده به ویروس: گلوگاه های پنهان ، تغییر رفتار مصرف کننده و سایر مظنونان غیر معمول-2021
In the early onset of the COVID-19 pandemic in the U.S., consumers experienced surprising shortages of essential goods such as toilet paper, yeast and flour, and some of their favorite meat cuts that appeared to be unrelated to the pandemic at first look. The “usual” explanations attributing these shortages to “demand spikes” (e.g., foreign suppliers, trade wars and tariffs, and “hoarding behavior” of panicked consumers) often failed to explain them, and in the best of cases, predicted only temporary shortages. However, shortages in these supply chains ended up being in many cases real “supply chain struggles,” with their true causes going beyond the “usual” ones, and revealing a set of deeper and “unusual causes.” Our detailed analysis of the affected supply chains identifies these overlooked failure factors and hidden causes. Our underestimated causes include demand shifts among market segments, siloed demand planning, bottlenecks in shared resources such as transportation and distribution, and supply chain structures driven by the economics of an efficiency era that failed in a risk-fraught environment. We conclude on the profound lessons learned from the pandemic crisis on supply chains, and the implied challenges to address for building resilient supply chains for the future. The resilient supply chains of the future require rethinking the relevant “systems” we plan and optimize (e.g., firm, industry, region, global factor resources, etc.). The usual answer of building firm-specific redundancy of assets and operational flexibility might be prohibitive in the level of investment required for any one firm, or their financial stakeholders, to pursue and accept.
Keywords: supply chain | pandemic | essential goods | shortages
Uncovering research streams on agri-food supply chain management: A bibliometric study
کشف جریانهای تحقیق در مورد مدیریت زنجیره تأمین مواد غذایی کشاورزی: یک مطالعه کتابشناختی-2021
This study carried out a bibliometric analysis to critically review the evolution of the agri-food supply chain (AFSC) research field over the period of 2008–2019. A set of 1236 articles was analyzed from the Web of Science database. Besides using different analytical scientometric tools (topic mapping, co-citation, co-authorship and overlay visualization networks), this study identified frequently-used keywords, new and hot research topics and frequently-studied supply chain management (SCM) practices. Frequently used keywords are food supply chain, food waste, sustainability, food safety, SCM, food industry, and food security. New research themes include contract, blockchain, internet of things, resilience, and short food supply chain, a topic that demands further research especially due to the international COVID-19 pandemic and the need of farmers to be closer to the consumers. Hot research topics, that is, subjects that have been studied in highly cited papers were also identified include life cycle assessment, environmental impact, packaging, water use, food waste prevention, food waste generation, blockchain and carbon footprint. Among SCM practices, this study observed that risk and sustainable SCM are frequently used keywords. Procurement and reverse logistics were observed in fewer studies. SCM, food waste, food quality, GHG emissions and risk management are sustainable SCM practices frequently observed.
Keywords: Agri-food supply chain | Bibliometric analysis | Co-authorship | Co-citation analysis | Scientometrics | Supply chain management practices
Bi-objective optimal design of hydrogen and methane supply chains based on Power-to-Gas systems
طراحی بهینه دو هدفه زنجیره های تأمین هیدروژن و متان بر اساس سیستم های نیرو به گاز-2021
This paper presents a methodological design framework for Hydrogen and Methane Supply Chains (HMSC) based on Power-to-Gas (PtG) systems. The novelty of the work is twofold, first considering a specific demand for hydrogen for electromobility in addition to the hydrogen demand required as a feedstock to produce synthetic methane from the methanation process. and performing a bi-objective optimization of the HMSC to provide effective support for the study of deployment scenarios. The approach is based on a Mixed Integer Linear Programming (MILP) approach with augmented epsilon-constraint implemented in the GAMS environment according to a multi-period approach (2035-2050) with several available energy sources (wind, PV, hydro, national network) for hydrogen production. Carbon dioxide sources stem mainly from mechanization and gasification processes. The objectives to be minimized simultaneously are the Total Annual Cost and the greenhouse gas emissions related to the whole HMSC over the entire period studied.
KEYWORDS: Power-to-Gas | Methanation | Hydrogen | MILP | Augmented epsilon constraint | GAMS | optimization approach
The effect of natural/human-made hazards on business establishments and their supply chains
تأثیر خطرات طبیعی / انسانی بر موسسات تجاری و زنجیره های تأمین آنها-2021
This paper examines the impact of natural and human-made hazards on payroll, GDP, employment, and establishment survival/creation in the year of hazard occurrence in the U.S. economy and more specifically in the U.S. manufacturing/goods producing industry. Many of the papers that examine economic impacts of hazards consider upstream impacts of supply chain disruption. Measures of downstream impacts are often limited, particularly in measuring the short-term impacts. This paper examines how manufacturers and other establishments are impacted, at the industry and total economy level, by a disruption in supplies of goods with low substitutability, which is often referred to as the ripple effect. In this paper, eight models are developed to explore supply chain vulnerability, at the industry-level, to hazard events across geographic areas of the U.S. during the 2005 to 2016 time period. The most severe impacts are due to hazards in the manufacturing/goods industry supply chain, where payroll, GDP, and employment declined 2.9%, 3.9%, and 8.6%, respectively. For all establishments, payroll and employment declined 5.3% and 3.0%, respectively. The results further suggest that the compound effect of hazards through the supply chain possibly exceeds that of the local hazard (i.e., direct impact). This can create an incentive misalignment. The establishment that invests in mitigation efforts and experiences the hazard locally does not directly experience the majority of the net benefit. The findings also suggest there is a need to better understand the short-term downstream impacts from all hazards, especially at the aggregated national level.
Keywords: Hazards | Supply chain | Manufacturing | Value added | GDP
Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models
بهینه سازی قوی مبتنی بر داده ها برای مدیریت عدم قطعیت در مدل های برنامه ریزی زنجیره تامین-2021
While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work.
Keywords: Uncertainty Modelling | Supply chain Management | Data driven Robust Optimization | Neuro Fuzzy Clustering | Multi-Layered Perceptron
Exploring the feasibility of introducing electric freight vehicles in the short food supply chain: A multi-stakeholder approach
بررسی امکان معرفی وسایل نقلیه باری الکتریکی در زنجیره تأمین مواد غذایی کوتاه: یک رویکرد چند ذی نفع-2021
The transition towards more sustainable approaches in the Food Supply Chain was concretely visible in the implementation of alternative models, like the Short Food Supply Chain. Some authors raise doubts on the environmental impact of this model, in particular for the externalities caused by the transport system, suggesting the adoption of Electric Freight vehicles. By adopting a multi-stakeholder approach, the objective of this study is to explore both the barriers and potentialities involved in the adoption of Electric Freight vehicles in the Short Food Supply Chain, and the existence of a shared strategy at the system level able to foster their adoption. Results suggest that, for entrepreneurs, Electric Freight vehicles appear as a viable option, although more efforts are still needed at a governmental level, through the promotion of public measures in the form of support for purchasing costs or rental rate and offering technical expertise services. In terms of infrastructures, as is clear from interviews, improving the charging infrastructure efficiency to ensure EFVs shift optimization and increasing the number of charging points are today a priority. On the whole, more collaborative methods should be inaugurated, contributing to a shared vision of urban mobility which takes into account all supply chain actors (charging point operators, automotive industry, rental car services, farmers, and local authorities) to ensure the system works in a more efficient way.
Keywords: Electric Fright Transport | Short Food Supply Chain | Last Miles food | System Innovation | Case study | Sicily
Linking business model design and operational performance: The mediating role of supply chain integration
پیوند دادن طراحی مدل تجاری و عملکرد عملیاتی: نقش واسطه ای ادغام زنجیره تأمین-2021
Despite the increasing interest in the role of business model design (BMD) in improving performance, its in- fluence on operational performance remains unexplored, as do the underlying mechanisms of such effects. Drawing on dynamic capability theory, we propose that supply chain integration (SCI), including external integration and internal integration, mediates the relationship between BMD and operational performance. Matched survey data and objective performance data were collected from 131 Chinese manufacturing firms in three waves to test our research model. The key results are that external integration fully mediates the effect of novelty-centered BMD on operational performance, and efficiency-centered BMD directly improves operational performance. Theoretical and practical insights on how BMD and SCI can be leveraged to support operational performance are discussed.
Keywords: Business model design | Supply chain integration | Operational performance | Dynamic capability theory
Machine learning: Best way to sustain the supply chain in the era of industry 4:0
یادگیری ماشین: بهترین راه برای حفظ زنجیره تأمین در عصر صنعت 4:0-2021
With the rapidly growing importance in the industries on the adaptation of advanced technologies, the involvement of IT-enabled systems has increased in developing the pathway for the future industry. The learning’s from these technologies becomes paramount for the present industries which gives a sense of belongingness and significance of the industry towards the market. The digital revolution world-wide affected the physical happenings of the events in the manufacturing industries such as the procurement, manufacturing/assembling & distribution of goods. This digital reformation is known as Industry 4.0 which generally means the advancement in the existing business models where all the business operations are interconnected with each other by digital mode (virtual representation based on operations). In this kind of environment, it is being necessary to map all the operations digitally in such a manner so that the physical flows of resources/goods will not suffer at any stage. Machine learning in the present scenario is one of the thrust areas for the researchers and the practitioners. The output in the machine learning process is having many dependencies on the input data such as the functions and characteristics imparted to the machine at the earlier stage. The present paper aptly reflects the thoughts and reflections of present-day industries and the opportunities to express feelings, thoughts, and contribute towards the future industries.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International Conference on Computational and Experimental Methods in Mechanical Engineering.
Keywords: Machine Learning (ML) | Supply Chain (SC) | Industry 4.0 | Resources utilization | Digital transformation
Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context
تسهیل تجزیه و تحلیل های زنجیره تامین مجهز به هوش مصنوعی از طریق مدیریت اتحاد در طول بحران های همه گیر در زمینه B2B-2021
The COVID-19 pandemic has disrupted global supply chains and exposed weak links in the chains far beyond what most people have witnessed in their living memory. The scale of disruption affects every nation and industry, and the sudden and dramatic changes in demand and supply that have occurred during the pandemic crisis clearly differentiate its impact from other crises. Using the dynamic capabilities view, we studied alliance management capability (AMC) and artificial intelligence (AI) driven supply chain analytics capability (AI-SCAC) as dynamic capabilities, under the moderating effect of environmental dynamism. We tested our four research hypotheses using survey data collected from the Indian auto components manufacturing industry. For data analysis we used Warp PLS 7.0 (a variance-based structural equation modelling tool). We found that alliance management capability under the mediating effect of artificial intelligence-powered supply chain analytics capability enhances the operational and financial performance of the organization. Moreover, we also observed that the alliance management capability has a significant effect on artificial intelligence-powered supply chain analytics capability under the moderating effect of environmental dynamism. The results of our study provide a nuanced understanding of the dynamic capabilities and the relational view of organization. Finally, we noted the limitations of our study and provide numerous research directions that may help answer some of the questions that arise from our study.
Keywords: Artificial intelligence | Supply chain analytics | Alliance management | Environmental dynamism | Dynamic capability view