Paving the way to net-zero : identifying environmental sustainability factors for business model innovation through carbon disclosure project data

Net-zero emission targets are crucial, given the environmental impact of the food and beverage industries. Our study proposes an environmentally focused Sustainable Business Model (SBM) using data from 252 food, beverage, and tobacco companies that reported to the Carbon Disclosure Project (CDP). We investigated the risks, opportunities, business strategies, emission reduction initiatives, and supply chain interactions associated with climate change by analyzing their qualitative answers using the NVivo software. Following the grounded theory approach, we identified the Environmental Sustainability Factors (ESFs) that support businesses in meeting pollution reduction targets. The ESFs were integrated with Osterwalder’s business model canvas to create an archetype focused on delivering “net-zero” or “carbon neutral” value to customers. The model’s efficacy is enhanced by the advantages and motivations of environmental collaborations. The paper provides critical support for sustainability theories and assists Small and Medium Enterprises (SMEs) to develop strategic business models for net-zero emission targets

An industrial blockchain-based multi-criteria decision framework for global freight management in agricultural supply chains

In view of increasing supply chain disruption events, for example the China–United States trade war, the COVID-19 pandemic, and the Russia–Ukraine war, the complexity and dynamicity of global freight management keeps increasing. To build a resilient and sustainable supply chain, industrial practitioners are eager to systematically revamp the freight management decision process related to the selection of carriers, shipping lanes, and third-party logistics service providers. Therefore, this study aims at strengthening decision-making capabilities for global freight management, in which an industrial blockchain-based global freight decision framework (IB-GFDF) is proposed to incorporate consortium blockchain technology with the Bayesian best-worst method. Through the blockchain technology, pairwise comparisons can be conducted over the international freight network in a decentralized and immutable manner, and thus, a secure and commonly agreed-on pairwise comparison dataset is acquired. Subsequently, the pairwise comparison dataset with multi-stakeholder opinions is analyzed using the Bayesian best-worst method in order to prioritize the selection decision criteria related to carriers, shipping lanes, and 3PL service providers for global freight management. To verify the methodological feasibility, a case study of an Australian agricultural supply chain firm was conducted to support the development end-to-end (E2E) supply chain solutions originated from Australia. It was found that port infrastructure, ports of call and communication effectiveness were the major criteria for the selection decision, which can be emphasized in future global freight collaboration. In addition, an immutable and append-only record of pairwise comparisons can be established to support the visibility of time-varying stakeholders’ preferences

A cost optimization model for intra-shipment in a supply chain

In this research, a decision model is developed aiming to optimize the costs relating to the shipment of goods from one or more suppliers to the wholesaler which has received order placed by customers. The various suppliers have a variety of purchase prices for the goods and they also have varying delivery lead times. Moreover, the wholesaler is also allowed to consider to purchase the goods from other wholesalers which belong to the same organization (known as intra-shipment) and normally with a relatively higher price than the suppliers. Due to the immediate availability of goods in store, it is assumed that there is zero lead time for delivery. As such, the supplier needs to make a decision based on the computation of all the costs involved and identifies and optimum arrangement that optimize the costs. The model to be developed in this paper is able to provide expertise advice to support the decision to be made by the wholesaler, taking into consideration all the expected costs and achieve the minimization of total logistics costs

Computational intelligence approach for process parameter settings using knowledge representation

This study proposes a fuzzy approach which integrates fuzzy rule sets in a chromosome. To enhance the functionality and capability of the fuzzy set, Genetic Algorithms (GA) technique is incorporated to produce a better and improved fuzzy set which is able to generate the expected result. Past data were selected to create the chromosomes and form the primary population set. This approach capitalizes on the merits of both techniques and offsets the drawbacks of them which may undermine the performance. This research signifies the hybrid approach to identify the optimal criteria for process control in order to achieve the target of the whole operations with an innovative methodology that has not been covered adequately to-date. A case example has been conducted to validate the practicality of the approach and the outcome demonstrated that the proposed approach is able to achieve the results as expected

A hybrid fuzzy optimization model to minimize logistics cost

This paper presents a supply chain network in which supplier selection, lateral transshipment, and vehicle routing can be involved. We develop a Hybrid Fuzzy Optimization Model (HFOM) based on the integration of fuzzy logic and genetic algorithms to solve the problem. In order to demonstrate the effectiveness of the HFOM, several approaches including branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the HFOM through simulations. Results show that the HFOM outperforms other search methods

Development of an optimised transport logistics system for supporting distribution operations

This paper presents the development of an Optimised Transport Logistics System (OTLS) that adopts Case-Based Reasoning (CBR) technology to select appropriate heuristics rules for planning cargo loading, and selecting the appropriate carrier. To validate the proposed system, a prototype has been developed and tested in a third-party logistics firm, which helps an electrical appliance manufacturing company to deliver its products to local department stores or customers. The case example is outlined with analysis of the feasibility of this proposed system, based on test results

Factors and methods of environmentally responsible operations : a review in the logistics trans-shipment and supply chain environment

The purpose of this paper is to discuss the underlying needs for and determinants (factors, tools and methods) of environmentally responsible operations (EROs) and present some direction for work in the areas involving an environmental/quality/operations management interface. Several typical ERA tools/methods are described, and the findings of a literature review, of selected journal articles on environmental management and related areas are presented. The review identifies 15 ERO factors under three groupings, namely policy, product/process, and performance evaluation. The relevance of these factors to the ERO tools/methods is also discussed. The findings are useful for implementing EROs and developing a paradigm for integrating environmental, quality and operations management in organisations. The paper underlines the ERO potentials for environmental, quality and operations management interface and suggests the venues that warrant future work. Future research in this area particularly related to logistics trans-shipment and supply chain environment will be conducted

A technology management strategy selection method for firms in joint venture partnerships

This paper demonstrates how a fuzzy analytic hierarchy process approach can be used to determine technology management strategies for firms in partnerships that result in their sustained technological development. Firms with resource constraints tend to form venture partnerships with technologically advanced partners for indirect strategic benefits. In such partnerships, technology management strategies of host firms need to be manoeuvred strategically as they build local capabilities. Selection of technology management strategy is generally based on subjective judgements that use fuzzy data analysed under multiple decision criteria. Considering the degree of technological contribution from the source firm, technological competency of the host firm, and dominance of the partners as well as the clarity of roles between partners as decision factors, this paper demonstrates how to determine the optimal technology management strategy. The different technological stages of a real firm are analysed in order to illustrate the application of the proposed approach

A technology management strategy selection method for firms in joint venture partnerships

This paper demonstrates how a fuzzy analytic hierarchy process approach can be used to determine technology management strategies for firms in partnerships that result in their sustained technological development. Firms with resource constraints tend to form venture partnerships with technologically advanced partners for indirect strategic benefits. In such partnerships, technology management strategies of host firms need to be manoeuvred strategically as they build local capabilities. Selection of technology management strategy is generally based on subjective judgements that use fuzzy data analysed under multiple decision criteria. Considering the degree of technological contribution from the source firm, technological competency of the host firm, and dominance of the partners as well as the clarity of roles between partners as decision factors, this paper demonstrates how to determine the optimal technology management strategy. The different technological stages of a real firm are analysed in order to illustrate the application of the proposed approach

A hybrid risk management model : a case study of the textile industry

Purpose: The purpose of this paper is to propose a hybrid risk management model, focusing on identification and evaluation of potential risk scenarios in industry/enterprise level, which assists in preventing negative impacts from adverse risks. Design/methodology/approach: The proposed hybrid risk management model embraces the concept of hierarchical holographic modelling (HHM), enterprise-wise risk management (ERM) and risk filtering, ranking, and management (RFRM) that could be applied in real commercial settings. A case study is conducted in order to validate this comprehensive theoretical model. Findings: This study shows the potential risks that may be faced by the textile industry in Hong Kong. Corresponding responses are suggested for the risks in different levels, which provide a systematic approach in managing the risks. Research limitations/implications: The use of a single case study may limit the generalizability of the findings. Practical implications: The risks suffered by the textile industry are identified through the case study, which provide an insight for better planning and preparation, so as to gain a better chance of success than that of competitors. Originality/value: The proposed model does not only provide theoretical merits to the literature but can also be applied to different industries for risk management practices