Fuzzy Methods for Demand Forecasting in Supply Chain Management

2014 ◽  
pp. 243-268
Author(s):  
Başar Öztayşi ◽  
Eda Bolturk
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Chain Management ◽  
Fuzzy Methods

scholarly journals Demand Forecasting Using Fuzzy Methods Approach in Supply Chain Management

2021 ◽  
pp. 62-68
Author(s):  
Satyam Chouksey ◽  
Amit Soni
Keyword(s):  
Corporate Governance ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Chain Management ◽  
Fuzzy Methods ◽  
Everyday Work ◽  
Retail Organizations ◽  
A Company ◽  
Basic Input

Modern businesses are confronted with a variety of challenges in a challenging climate. Companies that succeed are more flexible, and they immediately adopt new or revised concepts for corporate governance. As time goes on, they begin to use these techniques in their everyday work. For a company, forecasting is a vital part of its operations. This is regarded as the most basic input in the SCM department and the company. In the context of SCM functions, companies whose chronological development is close to that of the SCM evolution begin to pay attention to the forecast. This research reveals that the firm’s organization can use the barriers and few practical solutions for forecasting. However, retail organizations are continuously looking for a forecasting approach that will allow them to keep their purchasing and sales in balance.

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 Electronic Data Interchange and Demand Forecasting Implications on Supply Chain Management Collaboration: A Customer Service Perspective

2017 ◽  
Vol 18 (2) ◽  
pp. 138
Author(s):  
Ilyas Masudin ◽  
Mohammed Sheikh Kamara
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Customer Service ◽  
Demand Forecasting ◽  
Electronic Data Interchange ◽  
Inventory Level ◽  
Electronic Data ◽  
Chain Management ◽  
Data Interchange ◽  
Good Customer Service

Customer service is a very important aspect within the supply chain. Through collaboration, the goal of each party within the supply chain is to add value to a product, in order to accelerate good customer service.  Good customer service leads to customer satisfaction and most importantly it developed customer loyalty. These are the main goal of ever firm in the supply chain, starting from raw material, production, distribution and down to the final consumer. This work is developed to investigate the impact of supply chain management collaboration activities on customer service in an inter-organizational context. This is done by examining how effective collaboration in supply chain management creating confidence and trust between vendor-customer relationships that provides benefit to both organizations; one of such benefit is improved customer service. This can be obtained through the use of Electronic Data Interchange (EDI), which ensures that products are delivered to customers faster with great accuracy, and demand forecasting and inventory management, which ensures that vendors maintain optimal inventory level so that they always have what customers want in stock. The method used in this work is by gathering information from several articles, journals and text books relating to this research work. There is a total of 49 including journals, books and articles used in this work, all of which are related to this study.

scholarly journals AN OVERVIEW OF FUZZY TECHNIQUES IN SUPPLY CHAIN MANAGEMENT: BIBLIOMETRICS, METHODOLOGIES, APPLICATIONS AND FUTURE DIRECTIONS

2021 ◽  
Vol 27 (2) ◽  
pp. 402-458
Author(s):  
Keyu Lu ◽  
Huchang Liao ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Fuzzy Decision Making ◽  
Future Directions ◽  
Chain Management ◽  
Cognitive Limitations ◽  
Fuzzy Methods ◽  
Fuzzy Techniques ◽  
Sets Theory

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM.

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 Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 565 ◽  
Author(s):  
Jiseong Noh ◽  
Hyun-Ji Park ◽  
Jong Soo Kim ◽  
Seung-June Hwang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Window Size ◽  
Batch Size ◽  
Chain Management ◽  
Product Demand ◽  
Forecasting Models ◽  
Gated Recurrent Unit

Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods

2021 ◽  
pp. 140-170
Author(s):  
Asma Husna ◽  
Saman Hassanzadeh Amin ◽  
Bharat Shah
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Deep Learning ◽  
Supply Chain Management ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Learning Approaches ◽  
Chain Management ◽  
Lstm Network ◽  
Artificial Neural Network Ann

Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) are adopted here for predictions from the Corporación Favorita grocery sales forecasting dataset collected from Kaggle website. Finally, the performances of the applied models are evaluated and compared. The results show that LSTM network tends to outperform the other two approaches in terms of performance. All experiments are conducted using Python's deep learning library and Keras and Tensorflow packages.

Graduate students' perceptions of supply chain skills for supply chain managers

2014 ◽  
Vol 21 (2) ◽  
pp. 276-299 ◽  
Author(s):  
Shams Rahman ◽  
Nie Qing
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Forecasting ◽  
Management Program ◽  
Management Skills ◽  
Chain Management ◽  
Content Type ◽  
Performance Matrix ◽  
Future Challenges ◽  
Skill Category

Purpose – The aim of this research is to identify the relevant skills and their relative importance, required by supply chain managers, and to suggest the key skill items that require improvement. Design/methodology/approach – Using a survey questionnaire, data were collected against 41 supply chain management skills. Using expert opinion, these skill items were then grouped to create four higher level supply chain skill-categories. These are supply chain general management (SCG) skill-category, supply chain analytical (SCA) skill-category, supply chain information technology (SCIT) skill-category, and supply chain environmental-related (SCE) skill-category. Subsequently, the importance-performance matrix (IPM) analysis was conducted to these higher level skill-categories to assess the strengths and weaknesses of the offered skills as perceived by the respondents. Findings – The analysis revealed that in order to prepare supply chain managers to face up to the future challenges educational institutions are required to devote their attention on areas such as warehousing management, distribution planning, demand forecasting, negotiation skill, cross-functional coordination skill, and knowledge of environmental issues in supply chains. Originality/value – This research provided insight into skills need for supply chain managers using IPM analysis. The results of the study could be adopted to upgrade the existing logistics and supply chain management program or design new logistics education and training programs to meet the current and future needs.

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