Customer-driven supply chains: Trends and practices in leading Italian companies

2020 ◽  
pp. 29-55
Author(s):  
Valeria Belvedere ◽  
Annalisa Tunisini
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Operating Conditions ◽  
Market Strategy ◽  
Chain Management ◽  
Technological Readiness ◽  
Management Processes

This paper aims at understanding whether and to what extent companies are facing the challenge of improving their supply chains according to a customer-driven approach. Although the most recent supply chain management literature developed theoretical reflections and conceptualizations on the need for customer centricity in supply chain management, companies' practice does not seem to follow these prescriptions and the empirical research highlighted a frequent misalignment between market strategy and supply chain management processes. The aim of this paper is to bridge these two perspectives by answering two research questions. First, how are companies revising their supply chains, that is, what is the nature of the most recent projects concerning supply chain improvements? Second, to what extent are companies that invest in such projects prioritizing those specific projects that make a concrete alignment between market orientation and supply chain operating conditions possible? The paper reports and discusses the findings of an empirical investigation conducted among leading Italian companies or Italian subsidiaries of multinational companies. In particular, a two-step research was conducted, consisting of ten indepth interviews and a survey. According to our study, Italian companies are revising their supply chains to provide prompt availability of the product in different (but coordinated) distribution channels. This led to the launch of projects related to Demand Forecasting and to Omnichannel strategy adoption. However, in most cases, the managerial and technological readiness of companies is not in line with the relevance of the challenges. Another area of improvement concerns projects aimed at adopting up-to-date technologies, mostly connected to the Industry 4.0 paradigm, to improve operational performance. In this case the major opportunities perceived by the companies relate to the adoption of Big Data Analytics in order to better understand market trends

scholarly journals A Review of the Wood Pellet Value Chain, Modern Value/Supply Chain Management Approaches, and Value/Supply Chain Models

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Natalie M. Hughes ◽  
Chander Shahi ◽  
Reino Pulkki
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Value Chain ◽  
Demand Forecasting ◽  
Value Chain Analysis ◽  
Wood Pellet ◽  
Chain Management ◽  
Chain Analysis ◽  
Management Approaches

We reviewed 153 peer-reviewed sources to provide identification of modern supply chain management techniques and exploration of supply chain modeling, to offer decision support to managers. Ultimately, the review is intended to assist member-companies of supply chains, mainly producers, improve their current management approaches, by directing them to studies that may be suitable for direct application to their supply chains and value chains for improved efficiency and profitability. We found that information on supply chain management and modeling techniques in general is available. However, few Canadian-based published studies exist regarding a demand-driven modeling approach to value/supply chain management for wood pellet production. Only three papers were found specifically on wood pellet value chain analysis. We propose that more studies should be carried out on the value chain of wood pellet manufacturing, as well as demand-driven management and modeling approaches with improved demand forecasting methods.

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.

scholarly journals Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains

2021 ◽  
Vol 13 (13) ◽  
pp. 7101
Author(s):  
Joash Mageto
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Data Analytics ◽  
Social Issues ◽  
Big Data Analytics ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Sustainable Supply Chain Management

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.

scholarly journals SPECIFICITY OF TRANSFORMATION OF SMART SUPPLY CHAINS

2020 ◽  
pp. 136-143
Author(s):  
Н.В. Островская ◽  
Ю.Е. Путихин
Keyword(s):  
At Risk ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Chain Management ◽  
Logistics Networks ◽  
Management Processes ◽  
Further Development

Трансформация умных цепей поставок в условиях цифровизации подвержена риску, связанному, в первую очередь, с пандемией коронавируса в 2020 году. В настоящее время процессы управления цепями поставок постепенно адаптируются к условиям пандемиии. Традиционные линейные цепи поставок трансформируются в цифровые логистические сети. Показано, что значительной степени инвестиции в цифровизацию своей логистической деятельности и внедрение умных цепей поставок закладывают основу трендов дальнейшего развития логистической деятельности в контексте цифровизации экономики. The transformation of smart supply chains in the context of digitalization is at risk associated primarily with the coronavirus pandemic in 2020. Currently, supply chain management processes are gradually adapting to the pandemic. Traditional linear supply chains are being transformed into digital logistics networks. It is shown that, to a large extent, investments in the digitalization of their logistics activities and the introduction of smart supply chains lay the foundation for trends in the further development of logistics activities in the context of the digitalization of the economy.

scholarly journals A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Cisse Sory Ibrahima ◽  
Jianwu Xue ◽  
Thierno Gueye
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Support Vector ◽  
Chain Management ◽  
Wide Range ◽  
Auto Parts

Demand forecasting and big data analytics in supply chain management are gaining interest. This is attributed to the wide range of big data analytics in supply chain management, in addition to demand forecasting, and behavioral analysis. In this article, we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications, identify gaps, and provide ideas for future research. Algorithms will then be classified and then applied in supply chain management such as neural networks, k-nearest neighbors, time series forecasting, clustering, regression analysis, support vector regression and support vector machines. An extensive hierarchical model for short-term auto parts demand assessment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series. The concept of extensive relevance assessment was proposed, and subsequently methods to reflect the relevance of automotive demand factors were discussed. Using a wide range of skills, the factors and cofactors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components. Then, it is compared with the existing data and predicted the short-term historical data. The result proved the predictive error is less than 6%, which supports the validity of the prediction method. This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Lesconsumer-to-consumer supply chains

2018 ◽  
Vol 44 (277) ◽  
pp. 93-107
Author(s):  
Aurélien Rouquet ◽  
Christine Roussat ◽  
Valentina Carbone
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Chain Management ◽  
Le Fort

La littérature ensupply chain management(SCM) a délaissé un type desupply chains : lesconsumer-to-consumer(C2C)supply chains, qui relient les consommateurs lorsqu’ils échangent des produits. Reposant sur une approche conceptuelle, cet article montre à la communauté logistique et SCM l’intérêt qu’il y a à explorer cessupply chains. L’article dégage quatre spécificités de ces chaînes : 1) leur orientation perpendiculaire auxsupply chainsclassiques, 2) le fort amateurisme de ses acteurs, 3) leur large encastrement social, 4) leur structure plus directe. L’étude des C2Csupply chainsest susceptible d’élargir le spectre du SCM en y intégrant plus fortement le consommateur.

scholarly journals Supply Chain Management Open Innovation: Virtual Integration in the Network Logistics System

2021 ◽  
Vol 7 (1) ◽  
pp. 54
Author(s):  
Vladimir Shcherbakov ◽  
Galina Silkina
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
System Integration ◽  
Business Models ◽  
Industrial Revolution ◽  
Linear Models ◽  
Logistics System ◽  
Chain Management ◽  
Economic Relations

The customer-oriented approach is actively developing within the global trend of the modern industrial revolution that is Industry 4.0. The focus on customer interests has led to cooperation and integration in supply chains, improving their efficiency and increasing transparency, awareness, and trust. However, an issue emerging in this scenario is that conventional supply chain management (SCM) procedures are unable to identify the potential proposal for a particular user. Modern businesses need to build integrated supply chains, which require well-developed infrastructure and easily available complementary services, relying on logistics as a networking technology. Supply chains of this generation grow from traditional individual desynchronized economic relations (linear models with some feedback and the simplest network configurations) to scalable, adaptable, harmonized partner networks. The logistics potential allows additional income by reducing the total costs of participants in the network, thus increasing the competitiveness of companies; this can be implemented based on new models of interaction in the current digital environment through, firstly, system integration. Our goal consists of identifying the essential characteristics of system integration and substantiating the methods for its implementation in the digital economy. The study is based on the analysis of global best practices, considering the reports from leading consulting companies and competent analytical agencies. We have confirmed that the role of a virtual system integrator of supply chains belongs to logistics platforms; the effects of a transition to platform business models are discussed in detail.

Big Data Analytics in the Agribusiness Supply Chain Management

2021 ◽  
Vol 9 (3) ◽  
pp. 32-42
Author(s):  
Marisol Valencia-Cárdenas ◽  
Jorge Anibal Restrepo-Morales ◽  
Francisco Javier Día-Serna
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation Model ◽  
Inventory Management ◽  
Latent Variables ◽  
Structural Equation ◽  
Big Data Analytics ◽  
Equation Model ◽  
Chain Management

Importance and impact of the systems related to Agribusiness and Agri-food, are increasing around the world and demand a paramount attention. Collaboration in the inventory management is an integral part of the supply chain management, related to proactive integration among the chain actors facilitating production and supply, in especial in the agroindustrial sector of the Departamento de Antioquia, Colombia. This research establishes the main relationships between latent variables as collaboration, technology, models, optimization and inventory management, based on a literature review and applying a Structural Equation Model to a survey data of a sample of agribusiness companies. The results show that Available Technologies associated with Big Data, generates improvement of Collaboration Strategies, improving also Forecasting and Optimization; besides, Inventory Planning and Collaboration are related to Available Technologies associated with Big Data. A Poisson regression model and a Structural Equation Model estimations detect that the increasing strategies of technologies and Big Data are favorable to apply collaboration in the supply chain management, increasing possibilities to the enterprise competitiveness.

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