scholarly journals A Zero-Waste Multi-Criteria Decision-Support Model for the Iron and Steel Industry in Developing Countries: A Case Study

2021 ◽  
Vol 13 (5) ◽  
pp. 2832
Author(s):  
Yolandi Schoeman ◽  
Paul Oberholster ◽  
Vernon Somerset
Keyword(s):  
Decision Support ◽  
Waste Management ◽  
Steel Industry ◽  
Industrial Waste ◽  
Iron And Steel Industry ◽  
Decision Support Model ◽  
Iron And Steel ◽  
Zero Waste ◽  
Support Model

The iron and steel industry is a major global industry that consumes vast quantities of energy and causes environmental degradation through greenhouse gas emissions and industrial waste generation, treatment, and disposal. There is a need to manage complex iron and steel industrial waste in Africa, which requires a system engineering approach to zero waste management as informed by multi-criteria decision-making. The purpose of the current study was to develop a hybrid four-step multi-criteria decision-support model, the i-ZEWATA (Industrial Zero Waste Tiered Analysis). I-ZEWATA acts as a road map to understand, design, assess, and evaluate the iron and steel industrial waste systems with the ultimate objective of moving towards and achieving a zero-waste footprint. The results demonstrate that iron and steel waste can be identified, visualized, prioritized, and managed to promote zero-waste by applying a system-engineered approach. Additionally, relationship patterns to environmental, social, operational, and economic aspects with system behavioral patterns and outcomes were identified. It was clear from the case study in South Africa that, although technology and solution investment is essential, waste management, valorization, and treatment components require a concerted effort to improve industrial waste operational management through effective zero-waste decision-support towards a circular economy.

scholarly journals Value Stream Mapping as a Supporting Management Tool to Identify the Flow of Industrial Waste: A Case Study

2020 ◽  
Vol 13 (1) ◽  
pp. 91
Author(s):  
Yolandi Schoeman ◽  
Paul Oberholster ◽  
Vernon Somerset
Keyword(s):  
Steel Industry ◽  
Industrial Waste ◽  
Management Tool ◽  
Value Stream Mapping ◽  
First Year ◽  
Iron And Steel Industry ◽  
Waste Generation ◽  
Iron And Steel ◽  
Value Stream

The Value Stream Mapping (VSM) method was applied to a case study in the iron and steel industry in Southern Africa as a supporting management tool to identify, demonstrate, and evaluate industrial waste and comprised of three steps. The first step included collecting and verifying waste generation and flow data as the VSM data input step. The second step comprises three phases: mapping waste generation and fractions and horizontal and vertical performance analysis. The third step is comprised of actual and future state maps compilation. Following the first year of implementation, waste was reduced by 28%, and waste removal cost by 45%. Implementing the VSM method demonstrated cost savings and reduced waste flow within the study’s first year. The initial waste generation reduction target of 5% per annum was exceeded. The VSM method application proved to be a practical method for the iron and steel industry to visualize and analyze waste flows, identify opportunities and challenges in waste management operations, reduce waste, promote lean manufacturing, and achieve an environmentally responsible zero-waste environment.

Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail

2014 ◽  
pp. 989-1018 ◽  
Author(s):  
Leo Mršić
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Bayesian Network ◽  
Real Life ◽  
Heterogeneous Data Source ◽  
Decision Support Model ◽  
Market Trends ◽  
Series Transformation ◽  
Support Model

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.

The Design and Implementation of the Iron and Steel Industry Sales System Based on Business Intelligence

2012 ◽  
Vol 268-270 ◽  
pp. 1657-1660 ◽  
Author(s):  
Hong Bo Ren ◽  
Yu Wang ◽  
Rong Luo ◽  
Ji Min Yu
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Management ◽  
Business Intelligence ◽  
Steel Industry ◽  
Historical Data ◽  
Iron And Steel Industry ◽  
Iron And Steel ◽  
Enterprise Informatization ◽  
Design And Implementation

As the construction of enterprise informatization in the iron and steel industry has increasingly matured, it leads enterprise to accumulate a large number of business data that can not be used effectively. So the enterprise lack of data information for auxiliary decision-making activities. This paper puts forward a construction of the iron and steel industry sales system based on BI. Combining sales information management with decision analysis for integrated solutions can handle daily business, effectively extract and analyze enterprise historical data into useful information which provides decision support for enterprises.

A decision support model for traffic congestion in protected areas: A case study of Shiretoko National Park

2013 ◽  
Vol 8 ◽  
pp. 18-27 ◽  
Author(s):  
Kensuke Ishikawa ◽  
Naoko Hachiya ◽  
Tetsuya Aikoh ◽  
Yasushi Shoji ◽  
Katsuhiro Nishinari ◽  
...  
Keyword(s):  
Decision Support ◽  
Protected Areas ◽  
Traffic Congestion ◽  
National Park ◽  
Decision Support Model ◽  
Support Model
Sign in / Sign up

Export Citation Format

Share Document