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Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty.

In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios.

From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible.

In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size.

This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.

The purpose of this paper is to set the benchmark for finished goods consumer supply chain companies in terms of financial metrics driven from best performing supply chains in the world.

The paper used a financial data collected from 25 large industries in Ethiopia and 25 companies from the best performing supply chains in the world as ranked by Gartner® to identify the gaps in financial metrics. This method helps in setting benchmarks for the case companies.

The result shows that the Ethiopian supply chains are performing well under revenue growth and insufficient under revenue per employee metrics. The result shows us these supply chains are accumulating inventories and are also seen inefficient and ineffective in their performances.

Even though the research is only one of the few on case considered, it is not without limitation. The strategies to narrow the performance gaps for the respective case companies are not articulated.

It is an ideal for the managers in the case companies to look into their performance gaps and take the necessary actions to stay alive in this fierce competition era. Hence, the paper shows insights to the improvement of the supply chain performances.

The research can be considered the only one of the few in a case country. It is also the first of the type in covering large fast moving consumer goods companies’ metrics at large aligning with the best practicing supply chains in the world within the same industry vertical.

The purpose of this paper is to test the supply chain (SC) strategies and the linking of SCs to SC strategies in line with Lee’s (2002) model.

The paper used an empirical survey of 134 large industries to test the hypothesis suggested and strengthens the existing theories.

Companies and SC measures are matched distinctively to their respective SC strategies.

Even though the research is only one of the few on case considered, it is not without limitations. The benefits from matching SC measures to the SC strategies are not quantified. Besides, continental wise survey is needed to come with further improved theory.

Different SC types require typical SC measures in order to increase competitiveness. Linking SC performance to the respective SC strategy is compulsory.

The research can be considered the only one of the few in the continent in general and a case country in particular. It is also the first of the type in the world in testing Lee’s model as far as the authors’ knowledge concerns.

The paper aims to analyse the contextual relationship and dependency amongst enablers for lean manufacturing implementation in Bulgarian small and medium-sized enterprises (SMEs).

In this study, the interpretive structural modelling (ISM) technique was used to develop a hierarchical structural model for enablers. Also, the interpretive ranking process (IRP) was used to analyse and rank enablers with reference to performance variables. For the ISM approach, a structural self- integration matrix was developed with the help of experts’ suggestions and opinions. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to analyse the relationship amongst enablers. A total of nine experts were chosen for collecting the primary data in which seven experts belong to the industry and two experts were academicians. The dominant relationship amongst the enablers was analysed through IRP modelling.

A total of 11 enablers were identified for the purpose of this study. The model shows that “leadership and commitment by management”, “human resource management”, “customer relation management”, “supplier relation management” and “information technology system” are the most significant enablers for lean implementation in Bulgarian SMEs as these are positioned at the bottom levels in ISM model. MICMAC analysis shows that five enablers fall in the independent factor, two enablers in linkage factor and four enablers in the dependant factor while there is no enabler in the autonomous factor. ISM and IRP models show that “continuous improvement” is an essential enabler for the successful implementation of lean in Bulgarian SMEs. This study also helps to explain the comparative analysis of ISM and IRP, which indicates that IRP is a more robust modelling approach than ISM, as it incorporates the relationship of enablers with performance variables.

ISM and IRP modelling approaches are based solely on expert opinions and responses. This limitation can be overcome with the help of empirical study.

This study supports the professionals/experts to prioritise and manage enablers at strategic and tactical levels while implementing lean manufacturing practices in Bulgarian SMEs. The models developed in the study will be helpful for practitioners to understand and analyse the interdependence of enablers for lean manufacturing implementation.

This study helps to identify and prioritise enablers that affect lean manufacturing adoption using ISM and IRP approaches. Literature shows that numerous authors have used the ISM approach but the use of IRP approach is limited. The models were developed in the study, totally dependent on data collected from the experts to ensure their real-life validity.

The purpose of this study is to examine the alignment between the risk hedging strategy and supplier collaboration and its effect on manufacturing competitiveness.

Building on the resource-based view (RBV) and contingency theory (CT), this study is guided by a positivist philosophy and employs a survey strategy to investigate both the direct relationship between the risk hedging strategy and manufacturing competitiveness and the moderating role of supplier collaboration using data collected from 397 respondents of manufacturing firms. Partial least squares structural equation modeling (PLS-SEM) technique is used to analyze collected data and to present the research findings.

Consistent with the RBV and CT, study findings indicate that a significant positive relationship exists between the risk hedging strategy and manufacturing competitiveness and that this relationship becomes stronger when supplier collaboration is interactively aligned. Study findings provide important insights on the role of the risk hedging strategy in promoting manufacturing competitiveness. Furthermore, supplier collaboration as a moderator accounts for a significant proportion of the relationship between the risk hedging strategy and manufacturing competitiveness.

Internal resources provide an explanation of the competitive differences among firms employing the risk hedging strategy. However, the environment presents opportunities for firms to acquire additional resources to fulfil the unique collaborative requirements of the risk hedging strategy. This study has used the moderation perspective to explain the interaction between the risk hedging strategy and supplier collaboration and its effect on manufacturing competitiveness. Future studies could incorporate other alignment concepts such as mediation and systems approach and compare the results to improve the theory.

This study can be considered as its kind in the supply chain management literature and both practitioners and researchers can benefit from the experience of resource-based and contingency analysis research and the results of aligning the risk hedging strategy with supplier collaboration for higher levels of manufacturing competitiveness.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

This research identifies the supply chain performance indices and designs an evaluation framework to assess and compare the Indian petroleum supply chain performance. We presented a case study of three Indian petroleum companies. For this purpose, we identified fifteen performance criteria extracted from previous literature and expert inputs and classified them into four groups.

A fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) method is employed for evaluating the performance of the Indian petroleum supply chain.

The design and evaluation framework suggests that the top three performance measurement criteria, the purity of the products, compliance with environmental laws, and new technology adoption. The result findings also indicate that company C contributes to a maximum satisfaction level of 77%. Simultaneously, companies A and B hold satisfaction levels of 72% and 67%.

The managers should ensure that environmental standards, new technology adoption, and quality are significant concerns in the petroleum supply chain. The managers should follow national and international policies to preserve the environment and ensure safety in operational activities.

This paper makes two contributions in the domain of performance measurement of the petroleum supply chain. First, it identifies the prominent supply chain performance indices. Second, it proposes a model to assess and compare the performance of Indian petroleum companies.

Previous studies on hospital supply chain performance have attempted to measure the performance of the hospital supply chain either by the measurement of performance indicators or the performance of specific activities. This paper attempts to measure the internal hospital supply chain's performance indicators to find their interdependencies to understand the relationship among them and identify the key performance indicators for each of those aspects of the logistics process toward improvement.

In this research, a systematic assessment and analysis method under vagueness is proposed to assess, analyze and measure the internal health care performance aspects (HCPA). The proposed method combines the group Decision-Making and Trial Evaluation Laboratory (DEMATEL) method and rough set theory.

The study results indicate that the most critical aspects of hospital supply chain performance are completeness of treatment, clinical care process time and no delay in treatment.

The causal relationship from rough-DEMATEL can advise management officials that to improve the completeness of treatment toward patient safety, clinical care process time should be addressed initially and with it, patient safety aspects such as free from error, clinical care productivity, etc. should be improved as well. Improvement of these aspects will improve the other aspects they are related to.

This paper aims to propose a dynamic model for measurement supply chain performance (SCP) based on a dynamic balanced scorecard (DBSC). Balanced scorecard (BSC) can be defined as a popular performance measurement method that can translate the strategy into a set of performance indicators and manage the status of implementing the various strategies. However, BSC is unable to simulate the complicated environment and the dynamic behavior of performance metrics. Therefore, the author combines BSC with system dynamics (SD) to explore a more efficient tool for measurement SCP.

A dynamic causal model is proposed based on the causal hypotheses. The developed DBSC enables managers to evaluate and measure the SCP in a much-balanced way. Using DBSC makes it possible that different SCP metrics to be reviewed and distributed into the four above-mentioned perspectives. It also enables supply chain (SC) managers to evaluate different strategies to improve SCP.

investigates two strategies to improve SCP as follows: (1) competitive strategy and (2) harvesting big data and using data mining techniques to determine the customer's expectations and then compares the results of these two strategies based on the four perspectives of DBSC and introduce the best strategy. Finally, harvesting big data and data mining is selected as the best strategy.

This study proposes a novel strategic management tool for measurement SCP and simulation of the complicated environment and the dynamic behavior of performance metrics. The proposed DBSC model enables managers to compare different strategies and select the best strategy.

The purpose of this paper is to develop and validate a scale measurement of supply chain operations reference (SCOR)-related performance indicators and proposed constructs, SCOR-related performance indicators as practices within the Indian manufacturing sector.

A literature-based model on SCOR processes with five constructs and respective performance indicators was empirically validated by using a structured questionnaire. A total of 155 respondents among Indian manufacturing sector participated in this research, and the returned questionnaires were analyzed by using structural equation modeling.

The study established a relationship among the SCOR-related performance indicators and overall supply chain performance indicators (OSCPI). The moderation effect of demographic characteristics, namely, employee size, company age and type of company showed significant differences between SCOR-related performance indicators and overall supply chain indicators.

The scope of the study is limited to specific Indian manufacturing firms. The survey could not represent whole population of manufacturing sector.

The findings assist managers/supply chain practitioners in improving the performance measures identified using the standard framework, i.e., SCOR processes, overall supply chain performance measures as standard practices for Indian manufacturing sector for a profitable and sustainable business growth in global environment.

This research holds a value for suggested practices under SCOR processes and the proposed model for OSCPI, a path finder/performance measurement tool for supply chain professionals in the Indian context.