A review on benchmarking of supply chain performance measures

2008 ◽  
Vol 15 (1) ◽  
pp. 25-51 ◽  
Author(s):  
Wai Peng Wong ◽  
Kuan Yew Wong
Keyword(s):  
Supply Chain ◽  
Performance Measures ◽  
Supply Chain Performance

Developing and Implementing a Port Fluidity Performance Measurement Methodology using Automatic Identification System Data

2018 ◽  
Vol 2672 (11) ◽  
pp. 30-40 ◽  
Author(s):  
C. James Kruse ◽  
Kenneth N. Mitchell ◽  
Patricia K. DiJoseph ◽  
Dong Hun Kang ◽  
David L. Schrank ◽  
...  
Keyword(s):  
Supply Chain ◽  
Travel Time ◽  
Performance Measures ◽  
Automatic Identification ◽  
Identification System ◽  
Army Corps ◽  
Supply Chain Performance ◽  
Travel Time Reliability ◽  
Automatic Identification System ◽  
The U.S

The U.S. Army Corps of Engineers (USACE) is responsible for the maintenance of federally authorized navigation channels and associated infrastructure. As such, USACE requires objective performance measures for determining the level of service being provided by the hundreds of maintained navigation projects nationwide. To this end, the U.S. Army Engineer Research and Development Center partnered with Texas A&M Transportation Institute to develop a freight fluidity assessment framework for coastal ports. The goal was to use archival automatic identification system (AIS) data to develop and demonstrate how ports can be objectively compared in relation to fluidity, or the turnaround time reliability of oceangoing vessels. The framework allows USACE to evaluate maintained navigation project conditions alongside port system performance indices, thereby providing insight into questions of required maintained channel dimensions. The freight fluidity concept focuses on supply chain performance measures such as travel time reliability and end-to-end shipping costs. Although there are numerous research efforts underway to implement freight fluidity, this is the first known application to U.S. ports. This paper covers AIS data inputs, quality control, and performance measures development, and also provides a demonstration application of the methodology at the Port of Mobile, Alabama, highlighting travel time and travel time reliability operating statistics for the overall port area. This work provides foundational knowledge to practitioners and port stakeholders looking to improve supply chain performance and is also valuable for researchers interested in the development and application of multimodal freight fluidity performance measures.

A New Approach to Supply Chain Performance Measurement

2016 ◽  
pp. 111-128
Author(s):  
Amit Kumar Marwah ◽  
Girish Thakar ◽  
R. C. Gupta
Keyword(s):  
Factor Analysis ◽  
Supply Chain ◽  
Performance Measures ◽  
Research Work ◽  
Primary Data ◽  
Supply Chain Performance ◽  
New Approach ◽  
Factors Affecting ◽  
Manufacturing Organizations ◽  
Overall Reliability

Existing research work has established that many of today's manufacturing organizations have failed to develop a comprehensive supply chain performance measures. In this chapter, the authors intend to empirically assess the effects of supplier buyer relations and human metrics on supply chain performance in the context of Indian manufacturing organizations. After rigorous literature review, total 18 variables have been identified which are later on reduced in number by factor analysis. As a pilot study, primary data is collected from 100 manufacturing organizations by means of a questionnaire and a scale is developed. On a sample size of 100, the proposed hypotheses are tested by applying two-tailed tests. t-test and factor analysis resulted in 5 factors, 2 related to supplier-buyer relations and 3 related to human metrics. The overall reliability of the scale comes out to be 0.697. The research work provides a new approach to the manufacturing organizations to understand the factors affecting supply chain performance. The present study is limited to Indian manufacturing organizations.

scholarly journals Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand

2020 ◽  
Vol 12 (16) ◽  
pp. 6470 ◽  
Author(s):  
Ahmed Shaban ◽  
Mohamed A. Shalaby ◽  
Giulio Di Gravio ◽  
Riccardo Patriarca
Keyword(s):  
Supply Chain ◽  
Performance Measures ◽  
Bullwhip Effect ◽  
Service Level ◽  
Operating Conditions ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Ordering Policy ◽  
Smoothing Parameters ◽  
The Impact

The bullwhip effect reflects the variance amplification of demand as they are moving upstream in a supply chain, and leading to the distortion of demand information that hinders supply chain performance sustainability. Extensive research has been undertaken to model, measure, and analyze the bullwhip effect while assuming stationary independent and identically distributed (i.i.d) demand, employing the classical order-up-to (OUT) policy and allowing return orders. On the contrary, correlated demand where a period’s demand is related to previous periods’ demands is evident in several real-life situations, such as demand patterns that exhibit trends or seasonality. This paper assumes correlated demand and aims to investigate the order variance ratio (OVR), net stock amplification ratio (NSA), and average fill rate/service level (AFR). Moreover, the impact of correlated demand on the supply chain performance under various operational parameters, such as lead-time, forecasting parameter, and ordering policy parameters, is analyzed. A simulation modeling approach is adopted to analyze the response of a single-echelon supply chain model that restricts return orders and faces a first order autoregressive demand process AR(1). A generalized order-up-to policy that allows order smoothing through the proper tuning of its smoothing parameters is applied. The characterization results confirm that the correlated demand affects the three performance measures and interacts with the operating conditions. The results also indicate that the generalized OUT inventory policy should be adopted with the correlated demand, as its smoothing parameters can be adapted to utilize the demand characteristics such that OVR and NSA can be reduced without affecting the service level (AFR), implying sustainable supply chain operations. Furthermore, the results of a factorial design have confirmed that the ordering policy parameters and their interactions have the largest impact on the three performance measures. Based on the above characterization, the paper provides management with means to sustain good performance of a supply chain whenever a correlated demand pattern is realized through selecting the control parameters that decrease the bullwhip effect.

Supply chain performance measures for producers and processors of premium beef cuts: A conjoint approach

2004 ◽  
Vol 4 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Alan McDermott ◽  
Simon Lovatt ◽  
Scott Koslow
Keyword(s):  
Supply Chain ◽  
New Zealand ◽  
Performance Measures ◽  
Lead Time ◽  
High Price ◽  
Supply Chain Performance ◽  
Analysis Methodology ◽  
Beef Producers ◽  
Short Lead Time ◽  
Conjoint Approach

The performance measures important to New Zealand beef producers and processors in their selling and buying decisions were studied using a conjoint analysis methodology. 98 producers and 5 processors were asked to rank and rate various scenarios. Producers preferred situations in which they received a high price, had high payment security, a premium for quality, had a short lead-time and the processor shared some information. Processors focussed on factors that enabled them to reduce their risk and cost of supply, and ensure traceability back to farms.

Configuration of supply chain integration and delivery performance

2016 ◽  
Vol 11 (1) ◽  
pp. 43-74 ◽  
Author(s):  
Premaratne Samaranayake ◽  
Tritos Laosirihongthong
Keyword(s):  
Supply Chain ◽  
Performance Evaluation ◽  
Performance Measures ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Content Type ◽  
Delivery Performance ◽  
Supply Chain Model ◽  
Evaluation And Monitoring ◽  
Integrated Supply Chain

Purpose – The purpose of this paper is to develop a conceptual framework of integrated supply chain model that can be used to measure, evaluate and monitor operational performance under dynamic and uncertain conditions. Design/methodology/approach – The research methodology consists of two stages: configuration of a conceptual framework of integrated supply chain model linked with performance measures and illustration of the integrated supply chain model and delivery performance using a case of dairy industry. The integrated supply chain model is based on a unitary structuring technique and forms the basis for measuring and evaluating supply chain performance. Delivery performance with variation of demand (forecast and actual) is monitored using a fuzzy-based decision support system, based on three inputs: capacity utilization (influenced by production disruption), raw materials shortage and quality of dairy products. Findings – Integration of supply chain components (materials, resources, operations, activities, suppliers, etc.) of key processes using unitary structuring approach enables information integration in real time for performance evaluation and monitoring in complex supply chain situations. In addition, real-time performance monitoring is recognized as being of great importance for supply chain management in responding to uncertainties inherent in the operational environment. Research limitations/implications – Implementation of an integrated model requires maintenance of supply chain components with all necessary data and information in a system environment such as enterprise resource planning. Practical implications – The integrated model provides decision-makers with an overall view of supply chain components and direct links that need to be maintained for supply chain performance evaluation and monitoring. Wider adaptation and diffusion of the proposed model require further validation of the model and feasibility of implementation, using real-time data and information on selected performance measures. Originality/value – Integration of supply chain components across supply chain processes directly linked with performance measures is a novel approach for effective supply chain performance evaluation and monitoring in complex supply chains under dynamic and uncertain conditions.

Understanding big data-driven supply chain and performance measures for customer satisfaction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramadas Thekkoote
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Customer Satisfaction ◽  
Performance Measures ◽  
Data Driven ◽  
Equation Modeling ◽  
Supply Chain Performance ◽  
Content Type ◽  
Different Dimensions ◽  
The Impact

PurposeSupply chain analytics with big data capability are now growing to the next frontier in transforming the supply chain. However, very few studies have identified its different dimensions and overall effects on supply chain performance measures and customer satisfaction. The aim of this paper to design the data-driven supply chain model to evaluate the impact on supply chain performance and customer satisfaction.Design/methodology/approachThis research uses the resource-based view, emerging literature on big data, supply chain performance measures and customer satisfaction theory to develop the big data-driven supply chain (BDDSC) model. The model tested using questionnaire data collected from supply chain managers and supply chain analysts. To prove the research model, the study uses the structural equation modeling technique.FindingsThe results of the study identify the supply chain performance measures (integration, innovation, flexibility, efficiency, quality and market performance) and customer satisfaction (cost, flexibility, quality and delivery) positively associated with the BDDSC model.Originality/valueThis paper fills the significant gap in the BDDSC on the different dimensions of supply chain performance measures and their impacts on customer satisfaction.

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