scholarly journals Supply Chain Efficiency in the Discount Store Industry Post COVID-19: Applying the Supply Chain Efficiency Ratio

2021 ◽  
pp. 423-430
Author(s):  
Kevin Forehand ◽  
Juan Roman ◽  
Thomas Schaefer
Keyword(s):  
Supply Chain ◽  
Efficiency Ratio ◽  
Discount Store ◽  
Supply Chain Efficiency

Buffet Effect Simulation Model on Demand Chain Management

2010 ◽  
Vol 44-47 ◽  
pp. 688-692
Author(s):  
Xiao Yan Wang ◽  
Jian Sun
Keyword(s):  
Supply Chain ◽  
Side Information ◽  
Bullwhip Effect ◽  
Chain Management ◽  
Demand Chain ◽  
Supply Chain Efficiency ◽  
Demand Fluctuations ◽  
Partner Collaboration ◽  
Transmission Ability ◽  
Demand Chain Management

Bullwhip effect means the magnification of demand fluctuations, which is evident in a supply chain when demand increases and decreases, while the concept of Demand Chain Management means to make the planning on the basis of the demand side information so as to solve the problem of inconsistent upstream and downstream information by means of partner collaboration in the supply chain. Demand chain emphasizes the customer demand as its core value so as to achieve the best balance between the supply chain efficiency and customer satisfaction. Compared with the supply chain, the demand chain advises the enterprise to strengthen the information transmission ability to promote the performance. Under the demand chain management, the extent of bullwhip effect are weakened, and the fluctuation range against demand chain management is lower than against traditional supply chain.

scholarly journals Optimizing the Supply Chain in a Fuel Process Industry

2021 ◽  
Vol 12 (5) ◽  
pp. 549-553
Author(s):  
Amit Agrawal
Keyword(s):  
Supply Chain ◽  
Performance Management ◽  
Inventory Management ◽  
Best Practice ◽  
Business Processes ◽  
Raw Material ◽  
Bottom Line ◽  
Supply Chain Efficiency ◽  
Value Streams ◽  
And Performance

The Supply Chain Improvement (SCO) project is being introduced by KNPC, which recognises the major business advantages of improved hydrocarbon supply chain efficiency. The objectives of this work is to strengthen, optimise, and integrate supply scheduling and processes not just at stage of enterprise, but also throughout the entire KNPC framework. This is expected to lead to an improvement in the company's bottom line and facilitate the management of business operations at the highest level of efficiency, agility and profitability. In ever changing market conditions and globally competitiveness, it is necessary that raw material supply storage and product delivery were carried out at the lowest possible time and efficiency. Otherwise export oriented refinery like KNPC cannot remain in business with profit. KNPC has therefore embarked on ambitious multi-year operational excellence programs aimed at enhancing its operations and business processes that include short / mid-term planning, scheduling, accounting for growth, inventory management, and performance management. The aim is to achieve top-quartile financial results by accessing new value streams, encapsulating business processes of best practice and motivating employees of businesses to work in a collaborative atmosphere within the global and cross-functional business cycle to make smarter, quicker and more competitive choices.

scholarly journals Enterprise Intelligent Procurement Management Model Exploration

2021 ◽  
Author(s):  
Baixing Yang ◽  
Sai Yang
Keyword(s):  
Supply Chain ◽  
Balanced Scorecard ◽  
Maturity Model ◽  
Procurement Management ◽  
The Balanced Scorecard ◽  
Supply Chain Efficiency ◽  
Development Direction ◽  
Procurement System ◽  
Benchmarking System

With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains

2021 ◽  
Author(s):  
Gabrielle Gauthier Melançon ◽  
Philippe Grangier ◽  
Eric Prescott-Gagnon ◽  
Emmanuel Sabourin ◽  
Louis-Martin Rousseau
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Service Level ◽  
Learning System ◽  
Safety Stock ◽  
Service Levels ◽  
Sources Of Uncertainty ◽  
Supply Chain Efficiency ◽  
Percentage Points ◽  
Different Sources

Despite advanced supply chain planning and execution systems, manufacturers and distributors tend to observe service levels below their targets, owing to different sources of uncertainty and risks. These risks, such as drastic changes in demand, machine failures, or systems not properly configured, can lead to planning or execution issues in the supply chain. It is too expensive to have planners continually track all situations at a granular level to ensure that no deviations or configuration problems occur. We present a machine learning system that predicts service-level failures a few weeks in advance and alerts the planners. The system includes a user interface that explains the alerts and helps to identify failure fixes. We conducted this research in cooperation with Michelin. Through experiments carried out over the course of four phases, we confirmed that machine learning can help predict service-level failures. In our last experiment, planners were able to use these predictions to make adjustments on tires for which failures were predicted, resulting in an improvement in the service level of 10 percentage points. Additionally, the system enabled planners to identify recurrent issues in their supply chain, such as safety-stock computation problems, impacting the overall supply chain efficiency. The proposed system showcases the importance of reducing the silos in supply chain management.

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