scholarly journals Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques

2022 ◽  
Vol 15 (1) ◽  
pp. 0-0
Keyword(s):  
Supply Chain ◽  
Supervised Learning ◽  
Explicit Knowledge ◽  
Confusion Matrix ◽  
Demand Forecasting ◽  
Hybrid Approach ◽  
Factors Affecting ◽  
Management Processes ◽  
Kappa Value ◽  
The One

In today’s context (competition and knowledge economy), ML and KM on the supply chain level have received increased attention aiming to determine long and short-term success of many companies. However, demand forecasting with maximum accuracy is absolutely critical to invest in various fields, which places the knowledge extract process in high demand. In this paper, we propose a hybrid approach of prediction into a demand forecasting process in supply chain based on the one hand, on the processes analysis for best professional knowledge for required competencies. And on the other hand, the use of different data sources by supervised learning to improve the process of acquiring explicit knowledge, maximizing the efficiency of the demand forecasting, and comparing the obtained efficiency results. Therefore, the results reveal that the practices of KM should be considered as the most important factors affecting the demand forecasting process in supply chain. The classifier performance is examined by using a confusion matrix based on their Accuracy and Kappa value.

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.

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 Optimal Strategies for CLSC considering Supply Disruption and Carbon Tax

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lin Tong ◽  
Kuan Yang ◽  
Wei-Jin Xu
Keyword(s):  
Supply Chain ◽  
Carbon Tax ◽  
Optimal Strategies ◽  
Supply Disruption ◽  
Factors Affecting ◽  
Global Environmental ◽  
Two Factors ◽  
The One ◽  
The Cost

Under the background of economic globalization, supply chain is becoming more and more complex, which is manifested in the instability of external environment. On the one hand, with the improvement of global environmental protection awareness, the government's policy tools for environmental impact (carbon tax) on the whole supply chain have become one of the major external problems faced by the supply chain enterprises; on the other hand, the intensification of competition between upstream and downstream in supply chains makes supply disruption an important proposition to be solved urgently. In this paper, the two propositions of green and supply disruption are reduced to two factors affecting the cost. The average total cost function of the manufacturer as a recycler is established. The practicability of the algorithm and the effectiveness of the model are verified by Lingo, Particle Swarm Optimization, and Genetic Algorithm, with the purpose of obtaining the optimal strategies for manufacturers who play the role of the recycler in the closed-loop supply chain.

Factors Affecting Sustainability of Project Management Processes

2015 ◽  
Vol 19 (2) ◽  
pp. 9-18
Author(s):  
Ahmed Oraby ◽  
Mohamed Mohamdeen ◽  
Hassan Hassan ◽  
Ibrahim Nosseir
Keyword(s):  
Project Management ◽  
Factors Affecting ◽  
Management Processes ◽  
Project Management Processes

scholarly journals Information Acquisition and Its Incentives in an E-Commerce Supply Chain under the Offline Showroom Model

2021 ◽  
Vol 16 (5) ◽  
pp. 1791-1804
Author(s):  
Mengli Li ◽  
Xumei Zhang
Keyword(s):  
Supply Chain ◽  
Information Acquisition ◽  
Incentive Contracts ◽  
Demand Forecast ◽  
Competition Intensity ◽  
Demand Information ◽  
Optimal Information ◽  
Acquisition Strategy ◽  
The One ◽  
The Impact

Recently, the showroom model has developed fast for allowing consumers to evaluate a product offline and then buy it online. This paper aims at exploring the optimal information acquisition strategy and its incentive contracts in an e-commerce supply chain with two competing e-tailers and an offline showroom. Based on signaling game theory, we build a mathematical model by considering the impact of experience service and competition intensity on consumers’ demand. We find that, on the one hand, information acquisition promotes supply chain members to obtain demand information directly or indirectly, which leads to forecast revenue. On the other hand, information acquisition promotes supply chain members to distort optimal decisions, which results in signal cost. The optimal information acquisition strategy depends on the joint impact of forecast revenue, signal cost and demand forecast cost. Notably, in some conditions, the offline showroom will not acquire demand information even when its cost is equal to zero. We also design two different information acquisition incentive contracts to obtain Pareto improvement for all supply chain members.

scholarly journals Decisions and Coordination in E-Commerce Supply Chain under Logistics Outsourcing and Altruistic Preferences

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 253
Author(s):  
Yuyan Wang ◽  
Zhaoqing Yu ◽  
Liang Shen ◽  
Runjie Fan ◽  
Rongyun Tang
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Stackelberg Game ◽  
Consumer Preferences ◽  
Third Party ◽  
Altruistic Behavior ◽  
Fixed Cost ◽  
Equilibrium Solutions ◽  
Factors Affecting ◽  
Dual Channel

Considering the peculiarities of logistics in the electronic commerce (e-commerce) supply chain (ESC) and e-commerce platform’s altruistic preferences, a model including an e-commerce platform, third-party logistics service provider, and manufacturer is constructed. Based on this, three decision models are proposed and equilibrium solutions are obtained by the Stackelberg game. Then, an “altruistic preference joint fixed-cost” contract is proposed to maximize system efficiency. Finally, numerical analysis is used to validate the findings of the paper. The article not only analyzes and compares the optimal decisions under different ESC models, but also explores the intrinsic factors affecting the decisions. This paper finds that the conclusions of dual-channel supply chains or traditional supply chains do not necessarily apply to ESC, and that the effect of altruistic behavior under ESC is influenced by consumer preferences. Moreover, there is a multiparty win–win state for ESC, and this state can be achieved through the “altruistic preference joint fixed-cost” contract. Therefore, the findings of this paper contribute to the development of an e-commerce market and the cooperation of ESC members.

Unfilled Training Positions in Germany – Regional and Establishment-Specific Determinants

2019 ◽  
Vol 239 (4) ◽  
pp. 661-701 ◽  
Author(s):  
Sandra Dummert ◽  
Ute Leber ◽  
Barbara Schwengler
Keyword(s):  
Related Factors ◽  
Factors Affecting ◽  
External Conditions ◽  
Western Germany ◽  
Enterprise Level ◽  
Specific Factors ◽  
Regional Factors ◽  
The One ◽  
Training Position ◽  
Quantitative Importance

AbstractThe current situation in the German apprenticeship market is characterized by two contradictory developments. On the one hand, establishments are experiencing increasing problems filling the training positions they offer, and the number of vacant training positions is climbing. On the other hand, the transition into training is still difficult for many young people, and the number of unsuccessful vocational training applicants is rising. Hence, matching supply with demand is becoming increasingly difficult in the German job market for training positions. Using the Linked Employer-Employee dataset (LIAB) from the Institute for Employment Research (IAB), our paper provides a closer examination of the phenomenon of unfilled training positions. It presents an overview of the evolution of vacant training positions in eastern and western Germany and attempts to explain the number of vacancies by investigating the factors responsible for the establishments’ inability to fill their training positions. We assume that training position vacancies are due not only to internal company reasons such as firm size or the wage offer for apprentices but also to external conditions such as general regional factors. Therefore, our analysis also considers the situation on the demand side of the labor market within a region. The results of our multilevel mixed-effects estimations show that in addition to characteristics on the enterprise level, regional determinants also affect the share of vacant apprenticeships. With respect to establishment-related factors, mainly characteristics that describe the attractiveness of the firm prove to be important. With regard to regional-specific factors, we find that the availability of school leavers in a region in addition to the level of regional-sectoral competition impacts the recruiting success of establishments. Our analysis also shows that there are remarkable differences between eastern and western Germany concerning both the quantitative importance of unfilled training positions and the factors affecting them.

scholarly journals Landslide Mapping Using Multiscale LiDAR Digital Elevation Models

2020 ◽  
Vol 26 (4) ◽  
pp. 405-425
Author(s):  
Javed Miandad ◽  
Margaret M. Darrow ◽  
Michael D. Hendricks ◽  
Ronald P. Daanen
Keyword(s):  
Image Classification ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Support Vector ◽  
Classification Approach ◽  
Resolution Image ◽  
Kappa Value ◽  
The Individual ◽  
Segmentation Image

ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

Sign in / Sign up

Export Citation Format

Share Document