scholarly journals Investigating Warehousing Operations from an Integrated Supply-Chain and Transportation Approach

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Sushant Siddharth Wanjari
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Geographical Location ◽  
Autonomous Driving ◽  
Integrated System ◽  
Transport Costs ◽  
Spatial Understanding ◽  
Organizational Perspective ◽  
Future Challenges ◽  
The Impact

Transportation a primary step in the supply chain of goods. The responsive time between the parts of this chain may critically affect the duration of the processes. By accounting on an integrated system, warehouses can increase the accuracy and reliability of the processes. This paper analyzes the feasibility of integrated transportation and warehousing platforms from two points of view: infrastructure (e.g. physical place, geographical location) and organizational perspective (e.g. software, data, models). This paper contributes to fill the gap between practitioners and researchers about the needs of both systems. This paper found that transportation and warehousing are two inherently linked systems. However, the current practice lacks substantial improvements in data collection and modeling of these systems. Future directions point towards the use of big data and the implementation of econometric concepts (i.e. choice models), together with a spatial understanding of the impact of warehousing locations (i.e. accessibility concept) in transport costs. E-commerce, big data, and autonomous driving are the future challenges to integrating these two systems of warehousing and transportation. Finally, with the current pandemic of COVID-19, improving freight services is becoming a basic need. This paper contributes to a better understanding of the needs of integrating transportation and warehousing in the current challenging times.

scholarly journals Impact of big data and predictive analytics capability on supply chain sustainability

2018 ◽  
Vol 29 (2) ◽  
pp. 513-538 ◽  
Author(s):  
Shirish Jeble ◽  
Rameshwar Dubey ◽  
Stephen J. Childe ◽  
Thanos Papadopoulos ◽  
David Roubaud ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Theoretical Model ◽  
Performance Measures ◽  
Structural Equation ◽  
Predictive Analytics ◽  
Equation Modeling ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.Design/methodology/approachThe authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.FindingsThe statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support forH4. Although the authors did not find support forH4(moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.Originality/valueThis study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.

The Impact of Big Data Capability on Enterprise Performance—theMediating Effect of Supply Chain Flexibility

2021 ◽  
Vol 7 (6) ◽  
pp. 5641-5655
Author(s):  
Ji Feng ◽  
Cheng Guiqing ◽  
Jia Xuedi ◽  
Luo Qubo ◽  
Wu Fan
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Positive Impact ◽  
Mediating Effect ◽  
Enterprise Performance ◽  
Chain Flexibility ◽  
Supply Chain Flexibility ◽  
The Impact ◽  
Positive Effect ◽  
Development Capability

Based on 234 survey data of 35 pilot demonstration enterprises in intelligent manufacturing, this paper tested the mediating role of supply chain flexibility in the process of big data capability affecting enterprise performance. The empirical results show that the foundation capability, application capability, and development capability all have a significant positive impact on enterprises performance. Big data foundation capability has a significant positive effect on the supply chain flexibility in terms of product flexibility, logistics flexibility and production flexibility, and it has no significant effect on purchasing flexibility and information flexibility. Both big data application capability and big data development capability have a positive effect on supply chain flexibility. In addition to purchasing flexibility, the other dimensions of supply chain flexibility and supply chain flexibility comprehensive factors all have a mediating effect on the relationship between big data capabilities and firm performance. The conclusions of this study have a positive enlightenment role for enterprises to develop big data capabilities and create a flexible supply chain to meet the needs of the market and customers.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

scholarly journals China’s E-Economy: An overview of Opportunities and Threats

2019 ◽  
Vol 5 (2) ◽  
pp. 74
Author(s):  
Qiao Yao
Keyword(s):  
Big Data ◽  
Capital Investment ◽  
Digital Age ◽  
Autonomous Driving ◽  
Industrial Sector ◽  
Venture Capital Investment ◽  
Economic Threat ◽  
The World ◽  
The Usa ◽  
The Impact

China is the world biggest country in terms of population. It has the highest number of internet and mobile users. The world most substantial labor forces reside in China. A large proportion of the world is dependent on its exports. Chinas economy grew, in the last decade because of its exports, it got attention all over the world. Economy experts consider China as an economic threat to the USA. However, more studies are mainly focused on China populations, Exports, and labor focus because of the high quantity. The dynamics of the economy has changed in the last decade because of internet penetration across the globe. The Chinas role in digital aspects is least studied. Therefore this paper has focused on providing an overview of E-economy of China. Through literature and world-leading financial and consultancy firms reports it has been observed that just like other aspects of the economy, the e-economy of China is also growing. Today in 2019 where more than 50% of the world has access to the internet, It is considered that the Silicon Valley of USA is deriving the digital age because all big tech companies are located in the USA. USA main exports are Internet-related or Tech products. It is a fact that the USA E-economy contributes more to GDP compared to China. However, China has a potentially bright future in this area and can be the leading country in technology. Exploring the future possibilities, the opportunities which China has to grow in the digital age, the researchers found already there are areas in digital aspects where China has to outnumber the USA. For instance, the Fintech China got more Capital venture investments in 2016 compared to the USA. China is the world second country after the USA in attracting venture capital investment for Virtual Reality, Autonomous Driving, Wearables technologies, Education Technology, Robotics and drones, and 3D Printing. China is in the third position in terms of attracting investment for big data and artificial intelligence. The study concludes that China needs to focus more on big data and AI to continue its growth.  The growing digitalization can improve agriculture and industrial activities as the economy is maturing. The paper is useful for digital experts to view the understand the e-economy in depth, future researchers can narrow down the topic to observe the impact of E-economy on agriculture and industrial sector.

scholarly journals The impact of big data and business analytics on supply chain management

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Hans W. Ittmann
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Chain Management ◽  
Business Competitiveness ◽  
The Impact ◽  
Proper Analysis

Background: Change is inevitable and as supply chain managers prepare for the future they face many challenges. Two major trends over the last few years are the growing importance of ‘big data’ and analysing these data though ‘analytics’. The data contain much value and companies need to capitalise on the variety of data sources by in-depth and proper analysis through the use of ‘big data’ analytics.Objective: This article endeavours to highlight the evolving nature of the supply chain management (SCM) environment, to identify how the two major trends (‘big data’ and analytics) will impact SCM in future, to show the benefits that can be derived if these trends are embraced and to make recommendations to supply chain managers.Method: The importance of extracting value from the huge amounts of data available in the SCM area is stated. ‘Big data’ and analytics are defined and the impact of these in various SCM applications clearly illustrated.Results: It is shown, through examples, how the SCM area can be impacted by these new trends and developments. In these examples ‘big data’ analytics have already been embraced, used and implemented successfully. Big data is a reality and using analytics to extract value from the data has the potential to make a huge impact.Conclusion: It is strongly recommended that supply chain managers take note of these two trends, since better use of ‘big data’ analytics can ensure that they keep abreast with developments and changes which can assist in enhancing business competitiveness.

scholarly journals A comparative study to measure the impact of big data analytics on service supply chain process

2018 ◽  
Vol 8 (4) ◽  
pp. 356-365
Author(s):  
Bhawna Singh ◽  
Rushina i Singh ◽  
Suman Shokeen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Null Hypothesis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Strategic Decision ◽  
Chain Process ◽  
Service Supply Chain ◽  
Supply Chain Process ◽  
The Impact

Big data analytics is becoming a key to success for many organization as it extracts the productive value from a huge amount of raw data. This data helps in strategic decision making for continuous process improvements and advancement. This study also focusses on the impact of big data analytics on one of the most important process of an organization which is service supply chain process. ERP (Enterprise Resource Planning) is the tool which is used for big data analytics and based on that study was initiated for pre- and post-implementation of ERP for the years 2015 and 2017 respectively. Null hypothesis and H1 hypothesis are formed. Then, ten major factors have been identified which act as performance indicators for service supply chain process. Based on these factors data has been collected and analyzed. After obtaining the values one-way ANNOVA technique has been applied for testing of hypothesis. It has been observed that F calculated value comes out to be very high in comparison to the F table value which rejects the null hypothesis and proves that Big data analytics has an impact on service supply process.

scholarly journals The Impact of Economic Sanctions on Landlocked States (Comparative Case Studies of CAR and Mali)

2019 ◽  
pp. 49-63 ◽  
Author(s):  
Anna Sharova
Keyword(s):  
Geographical Location ◽  
Political Elites ◽  
Negative Consequences ◽  
International Division ◽  
Transport Costs ◽  
African Countries ◽  
Geographical Position ◽  
Comparative Case Studies ◽  
The Impact ◽  
Geographical Feature

Landlocked states are a special category of countries whose economic and social development is associated with a number of additional difficulties due to their geographical location. Among them are: limitation of participation in the international division of labor, high transport costs and costs associated with bureaucratic procedures for crossing the borders of third countries, as well as reducing the competitiveness of exports. The African continent has the largest number of such states. Simultaneously with the indicated political and geographical feature, various sanctions are in force or imposed on a number of this category of African countries, both by the UN and states individually. The sum of these factors negatively affected the development of these states. This article examines in detail two country cases of applying international sanctions against landlocked African countries: the CAR and Mali. The study led to the conclusion that the effectiveness of sanctions imposed against these countries and targeted sanctions against members of their political elites is low. The main damage and negative consequences are for the general population, since they directly relate to everyday life needs and requirements. For a significant part of the population of both countries, the costs of sanctions are compensated by the possibilities of the “economy of war”: illegal extraction of local natural resources, smuggling and speculation of essential goods. Peculiarities of the country’s geographical position, lack of access to the sea, under these conditions, can serve as a factor for further “decoupling” of elites from the sanctions issue and the continuation of their policies.

scholarly journals Data analytics in supply chain management

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Demand Forecasting ◽  
Chain Management ◽  
Strategic Sourcing ◽  
Management Processes ◽  
The Impact

The presentation discussed the impact of the technologies related to the 4th industrial revolution on big data. The 4th industrial revolution ecosystem is characterized by the presence of smart PPR (Product, Process, and Resource) who generates data. It transforms the product-based business model to product-data-driven service model. Big data also exist due to the digital transformation of supply chain management processes. Data analytics and machine learning can improve the supply chain management processes, such as demand forecasting, production, strategic sourcing, etc. Finally, the presentation gives some examples of the application of data analytics in real companies.

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