Frontier of digitalization in Biomass-to-X supply chain: opportunity or threats?

Loy, Adrian Chun Minh; Kong, Karen Gah Hie; Lim, Juin Yau; How, Bing Shen

doi:10.1016/j.jobab.2023.03.001

Cited by 7 publications

(2 citation statements)

References 53 publications

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“…Our proposed DSS model is expected to provide several benefits: 1). for stakeholders by improving the decisionmaking process of potential areas of bioenergy agroindustry, the amount of biomass feedstock for bioenergy from the harvest to enable more informed decision-making [38]. 2) Using DSS, bioenergy industry players can increase company profits by optimizing the practice of making better decisions about the company's aggregate capacity and planning and determining the amount of biomass inventory [39].…”

Section: Managerial Implicationsmentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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This study aims to design a decision support system (DSS) development model in sustainable bioenergy supply chains. Our approach involves 1) identifying the bioenergy supply chain model through determining the spatial potential model of agroindustry area using geographic information system (GIS) and analytical hierarchy process (AHP); 2) determining the optimization model for aggregate production process planning using fuzzy goal programming for bioenergy production to design the biomass inventory level determination model using adaptive neuro-fuzzy inference system (ANFIS) approach; 3) designing the concept of DSS model in bioenergy supply chain. The results showed the identification of the supply chain model from the spatial model of potential agroindustry locations with three regional categories: 19.34% potential, 16.93% not potential, and 63.70% developing. Aggregate planning is appropriate based on three objective functions to be achieved in production planning for determining inventory levels using ANFIS using three input variables and comparing performance with RMSE, MAPE, and R2 inventory levels so that the model can predict inventory levels adaptively. The concept of the DSS Model on the bioenergy supply chain from agricultural centers to users by adding Internet of Things (IoT) technology can increase the effectiveness and efficiency of the bioenergy supply chain. The managerial implications of this research can provide relevant insights for the design and improvement of renewable energy management programs. Utilization of local biomass resources becomes more optimal.

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Section: Managerial Implicationsmentioning

confidence: 99%

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Krisnaningsih,

Arkeman,

Marimin

et al. 2024

IJSDP

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“…New digital tools are especially applicable in the primary sector of the bioeconomy, i.e., the agriculture sector. According to Rennings et al [11] and Loy et al [14], implementing digital technologies in the bioeconomy processes makes the economy more efficient. Agriculture is anticipated to have a significant role in propelling advancements in the digital economy, centred around progress made in smart or precision agriculture [15].…”

Section: Introductionmentioning

confidence: 99%

Digitalisation in Bioeconomy in the Baltic States and Poland

Zeverte-Rivza,

Girdziute,

Parlińska

et al. 2023

Sustainability

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The agricultural and food production sectors have a predominant role in the bioeconomy of the European Union (EU), followed by wood production. These sectors make significant contributions not only to national economies but also to local areas, in particular the rural ones. Although the digitalisation of businesses within the bioeconomy sector transforms the enterprises, improving the value chains and creating benefits for the rural communities where these enterprises are situated, there are still many barriers to digitalisation. This study has a dual aim: first, to analyse bioeconomy in the EU and the state of digitalisation in the EU, and second, to assess the barriers of the bioeconomy sector and ways to support digital transformation within this sector, focusing on agriculture as the main contributor to bioeconomy in the EU, taking Lithuania, Latvia, and Poland as the case studies. The cluster analysis was explored for the study of the digitalisation and R&D indicators of the EU. The Analytic Hierarchy Process (AHP) was used to determine digitalisation scenarios of the bioeconomy sector in Latvia, Lithuania, and Poland. Four stakeholder groups were selected as experts for the study: (i) national government, (ii) advisory and extension, (iii) research, and (iv) entrepreneurship. The findings of the cluster analysis identified 4 clusters in the EU, showing different levels of digitalisation. Strong links between digitalisation and R&D were also found. In this context, Latvia, Lithuania, and Poland were assigned to the low-performing cluster. The results of the AHP for the case studies of Latvia, Lithuania, and Poland indicate the overall need for prioritization of support for digital transformation using national and EU funding to achieve better results. Nonetheless, AHP findings also suggested that the opinions of the national government, consulting, and research experts were more aligned throughout all three countries, but the entrepreneurs’ opinions differed from these groups. These findings provide quantitative information regarding digitalisation in the bioeconomy of the EU. They also offer additional qualitative information about scenarios and criteria for increasing the level of digitalisation in the bioeconomy sector in Latvia, Lithuania, and Poland, which could be useful for policy-makers. This research could also have practical implications for shaping the future trajectory of the bioeconomy policy.

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A review on sustainable management of biomass: physicochemical modification and its application for the removal of recalcitrant pollutants—challenges, opportunities, and future directions

Tan,

Abdul Raman,

Zainal Abidin

et al. 2024

Environ Sci Pollut Res

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Frontier of digitalization in Biomass-to-X supply chain: opportunity or threats?

Cited by 7 publications

References 53 publications

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Decision Support System for Bioenergy Supply Chain Optimization: A Case Study at Lebak District, Banten Indonesia

Digitalisation in Bioeconomy in the Baltic States and Poland

A review on sustainable management of biomass: physicochemical modification and its application for the removal of recalcitrant pollutants—challenges, opportunities, and future directions

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