Purpose: This study was carried out to create a research model investigating the impact of supply chain quality management (SCQM) practices on firm performance. Design/methodology/approach: Based on a comprehensive literature review, the practices were suggested. These SCQM practices will be analyzed and categorized into 4 dimensions: upstream (supplier assessment, supplier quality management), downstream sides of a supply chain (customer focus), internal process (product/service design, process management and logistics) and support practices (top management support, human resource management, information and supply chain integration). The measurement instrument of firm performance was developed including three aspects: operational performance, customer satisfaction and financial performance. Findings: A conceptual framework and a structural model were proposed as well as the development of hypotheses on the paths. Research limitations/implications: It is necessary to test the rationality of this model by empirical studies in different contexts. Originality/value: The research considers integration of quality and supply chain management still remains limited in the literature. Therefore, it is necessary to have a more focused approach in assessing quality management issues within the internal and external supply chain contexts. This study concentrates on the practices which improve quality aspects of supply chain, known as SCQM practices. Proposed structural model in this paper not only fills the voids in the literature but contributes a parsimonious conceptual framework for theory building in SCQM and firm performance. It also expects to offer a useful guidance for measuring and implementing SCQM practices as well as facilitate further studies in this field.

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Highlights A review on operations research (OR) models and methods for safety stock determination is conducted. No work has yet systematized research focusing on the safety stock determination problem. Articles are classified and discussed regarding the modeling approach, industrial application, solution technique and main performance criteria used. Research opportunities, promising research directions and trends are identified.

PurposeThis article aims to examine the simultaneous effect of risks on physical and intangible dimensions of supply chain performance under the globalization and Covid-19 perspectives.Design/methodology/approachThe manipulation of literature reviews together with the combination of Q-sort and empirical data in the construction industry to identify and assess risks and supply chain performance, is a novel approach in the supply chain risk management area. The analysis of Structural Equation Modeling that is able to calculate the simultaneous impact of various risks on supply chain performance, is used to validate this relationship.FindingsGlobal supply chains are currently facing interruptions caused by several sources of inherent uncertainties, e.g. natural disasters, war and terrorism, external legal issues, economic and political instability, social and cultural grievances, and diseases. The weaknesses of the current global supply chain have been revealed, resulting in delays, supply unfulfillment, labor shortages and demand fluctuation. These supply chain risks have a great on supply chain performance indicators, and the magnitude of their impact tends to increasingly impact in the context of globalization and the Covid-19 pandemic. Findings showed that the proposed risk models can be explained with Variance of supplier performance (25.5%), Innovation and learning (21.2%), Internal business (61.9%), Customer service (39.4%) and Finance (39.7%).Research limitations/implicationsSupply chain managers should keep in mind acceptable cost/benefit trade-offs in corporate risk mitigation efforts associated with major contingency risks. In doing so, the proposed hypothesized model can be “a road map” to achieve this purpose. Our research favors the adoption of supply chain management strategies, e.g. postponement, speculation and avoidance.Originality/valueThe trend toward globalization and the emergence of the Covid-19 pandemic increasing supply chain complexity are regarded as key drivers of supply chain risk and therefore enhance vulnerability to supply chain.

The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now looking to their data analytics infrastructures and searching for approaches to improve their decision-making capabilities, in order to enhance their results using new approaches such as Big Data and Machine Learning. The implementation of a Big Data Warehouse can be the first step to improve the organizations’ data analysis infrastructure and start retrieving value from the usage of Big Data technologies. Moving to Big Data technologies can provide several opportunities for organizations, such as the capability of analyzing an enormous quantity of data from different data sources in an efficient way. However, at the same time, different challenges can arise, including data quality, data management, and lack of knowledge within the organization, among others. In this work, we propose an approach that can be adopted in the logistics department of any organization in order to promote the Logistics 4.0 movement, while highlighting the main challenges and opportunities associated with the development and implementation of a Big Data Warehouse in a real demonstration case at a multinational automotive organization.

Demand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventoryrelated costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.

PurposeIn this paper, Supply Chain Quality Management dimensions are empirically tested in order to understand their impact on the organization performance based on the Balanced Scorecard perspectives.Design/methodology/approachIn order to validate the theoretical model proposed, an empirical study was carried out, supported by a large-scale questionnaire and statistical analysis.FindingsResults show that all the Supply Chain Quality Management dimensions have a significant positive correlation in the four Balanced Scorecard performance perspectives. Product/service quality and quality culture dimensions were the ones that presented the highest average scores. No significant differences were detected in any dimension for the different regions considered in this study.Originality/valueThe present research can help companies to achieve a better performance in the analyzed perspectives: customer, financial, internal process, and learning and growth. This work also contributes to the existing body of knowledge on Supply Chain Quality Management, analyzing its impact on organization performance, considering a more embracing perspective.