scholarly journals Improving Manufacturing Supply Chain by Integrating SMED and Production Scheduling

Logistics ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 4
Author(s):  
Viren Parwani ◽  
Guiping Hu
Keyword(s):  
Supply Chain ◽  
Production Scheduling ◽  
Large Scale ◽  
Production Efficiency ◽  
Manufacturing Sector ◽  
Lean Six Sigma ◽  
Cost Ratio ◽  
Increase Production Efficiency ◽  
Manufacturing Supply Chain ◽  
Scheduling Heuristic

Globalization has led to a significant effect on today’s manufacturing sector. Manufacturers need to find new and innovative ways to increase efficiency and reduce waste in the manufacturing supply chain. Lean/six sigma tools can help companies increase production efficiency and stay in competition. Manufacturing in smaller batches can keep the supply chain lean and customizable. This leads to frequent changeovers and downtime. A changeover is usually required when a single machine produces different products based on the requirement. A large-scale industry can either install multiple individual production lines to cater to the demand (usually expensive) or make frequent machinery changes. Single Minute Exchange Die (SMED) is a system designed for reducing the changeover time for machines. It reduces the time taken to complete the activities and eliminates non-essential activities throughout the changeover. Scheduling an operating procedure within SMED in such case is a challenge. Project scheduling model with workforce constraints can be used to create a set of heuristics to provide us with an optimized list of tasks. The paper proposes to design a scheduling heuristic model to allocate tasks to the operators to get the least amount of operator idle time and reduce changeover downtime costs. The paper further illustrates the benefit of the model in a case study and proposes its integration within the existing SMED methodology. This results in a benefit-to-cost ratio of 7.5% for production scheduling compared to that of stages 4 and 5 in SMED, which is 1.2%.

Key supply chain strategies for the post-COVID-19 era: implications for resilience and sustainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjoy Kumar Paul ◽  
Md. Abdul Moktadir ◽  
Kamrul Ahsan
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Manufacturing Sector ◽  
Dominant Role ◽  
Decision Makers ◽  
The Novel ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Sustainability Practices ◽  
Novel Coronavirus

PurposeThe impacts of the novel coronavirus (COVID-19) outbreak continue to devastate supply chain operations. To attain a competitive advantage in the post-COVID-19 era, decision-makers should explore key supply chain strategies to move forward and ready their policies to be implemented when the crisis sufficiently subsides. This is a significant and practical decision-making issue for any supply chain; hence, the purpose of this study is to explore and analyse key supply chain strategies to ensure robustness and resilience in the post-COVID-19 era.Design/methodology/approachThis study conducted an expert survey targeting practitioners and academics to explore key supply chain strategies as means of moving forward in the post-COVID-19 era. Further, the key strategies were quantitatively analysed by applying the best-worst method (BWM) to determine their priority importance in the context of the manufacturing sector.FindingsThe results revealed that supply chain resilience and sustainability practices could play a dominant role in this period. The findings of the study can assist supply chain decision-makers in their formulations of key strategies.Originality/valueThis is the first study to investigate key supply chain strategies for the post-COVID-19 era. This study will help practitioners paying attention to resilience and sustainability practices for managing the impacts of future large-scale disruptions.

scholarly journals 3D Printing in COVID-19: Productivity Estimation of the Most Promising Open Source Solutions in Emergency Situations

2020 ◽  
Vol 10 (11) ◽  
pp. 4004 ◽  
Author(s):  
Mika Salmi ◽  
Jan Sher Akmal ◽  
Eujin Pei ◽  
Jan Wolff ◽  
Alireza Jaribion ◽  
...  
Keyword(s):  
Supply Chain ◽  
3D Printing ◽  
Large Scale ◽  
Manufacturing Sector ◽  
Spare Parts ◽  
Frontline Workers ◽  
Emergency Situations ◽  
Medical Certification ◽  
Critical Issues ◽  
Time Critical

The COVID-19 pandemic has caused a surge of demand for medical supplies and spare parts, which has put pressure on the manufacturing sector. As a result, 3D printing communities and companies are currently operating to ease the breakdown in the medical supply chain. If no parts are available, 3D printing can potentially be used to produce time-critical parts on demand such as nasal swabs, face shields, respirators, and spares for ventilators. A structured search using online sources and feedback from key experts in the 3D printing area was applied to highlight critical issues and to suggest potential solutions. The prescribed outcomes were estimated in terms of cost and productivity at a small and large scale. This study analyzes the number and costs of parts that can be manufactured with a single machine within 24 h. It extrapolates this potential with the number of identical 3D printers in the world to estimate the global potential that can help practitioners, frontline workers, and those most vulnerable during the pandemic. It also proposes alternative 3D printing processes and materials that can be applicable. This new unregulated supply chain has also opened new questions concerning medical certification and Intellectual property rights (IPR). There is also a pressing need to develop new standards for 3D printing of medical parts for the current pandemic, and to ensure better national resilience.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

scholarly journals Development of Immunohistochemical Methods for Casein Detection in Meat Products

Foods ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 28
Author(s):  
Ludmila Kalčáková ◽  
Matej Pospiech ◽  
Bohuslava Tremlová ◽  
Zdeňka Javůrková ◽  
Irina Chernukha
Keyword(s):  
Milk Protein ◽  
Production Efficiency ◽  
Raw Materials ◽  
Meat Products ◽  
Milk Proteins ◽  
Increase Production Efficiency ◽  
Allergenic Potential ◽  
Significant Difference ◽  
Dairy Ingredients ◽  
Immunohistochemical Methods

To increase production efficiency of meat products, milk protein additives are often used. Despite a number of advantages, use of dairy ingredients involves a certain risk, namely the allergenic potential of milk proteins. A number of methods have been developed to detect milk-origin raw materials in foodstuffs, including immunological reference methods. This study presents newly developed immunohistochemical (IHC) methods for casein detection in meat products. Casein was successfully detected directly in meat products where sensitivity was determined at 1.21 and specificity at 0.28. The results obtained from the IHC were compared with the Enzyme-Linked Immuno Sorbent Assay (ELISA) and there was no statistically significant difference between the IHC and ELISA methods (p > 0.05). The correspondence between the methods was 72% in total. The highest correspondence was reached in frankfurters (90%), the lowest in canned pâté (44%).

Supply chain optimization of large-scale continuous processes

2005 ◽  
Vol 29 (6) ◽  
pp. 1305-1316 ◽  
Author(s):  
E.P. Schulz ◽  
M.S. Diaz ◽  
J.A. Bandoni
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Supply Chain Optimization ◽  
Continuous Processes

Network effect in shared supply chain platform value co-creation behavior in evolutionary game1

2021 ◽  
pp. 1-12
Author(s):  
Zou Xiaohong ◽  
Chen Jinlong ◽  
Gao Shuanping
Keyword(s):  
Boundary Condition ◽  
Supply Chain ◽  
Large Scale ◽  
Simulation Analysis ◽  
Supply And Demand ◽  
Evolutionary Game ◽  
Dynamic Game ◽  
Network Effect ◽  
Chain Model ◽  
The Cost

The shared supply chain model has provided new ideas for solving contradictions between supply and demand for large-scale standardized production by manufacturers and personalized demands of consumers. On the basis of a platform network effect perspective, this study constructs an evolutionary game model of value co-creation behavior for a shared supply chain platform and manufacturers, analyzes their evolutionary stable strategies, and uses numerical simulation analysis to further verify the model. The results revealed that the boundary condition for manufacturers to participate in value co-creation on a shared supply chain platform is that the net production cost of the manufacturers’ participation in the platform value co-creation must be less than that of nonparticipation. In addition, the boundary condition for the shared supply chain platform to actively participate in value co-creation is that the cost of the shared supply chain platform for active participation in value co-creation must be less than that of passive participation. Moreover, value co-creation behavior on the shared supply chain platform is a dynamic game interaction process between players with different benefit perceptions. Finally, the costs and benefits generated by the network effect can affect value co-creation on shared supply chain platforms.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

Sign in / Sign up

Export Citation Format

Share Document