Demand Chain Research Articles

New power system development path mechanism design

Carrying out green energy transformation, implementing clean energy power replacement and supply, and developing a new power system are some primary driving forces needed to fulfill China’s carbon-peak and carbon-neutral strategic goals. The construction of new power systems in China’s provinces and cities is developing rapidly, and the lack of a typical model promotes the application. The new power system path design should be based on the actual development of the power grid in different regions, energy use characteristics, and other actual needs to carry out the differentiated path design. In this context, this study analyzes the characteristics of the new domestic power system based on the policy background of the new domestic power system, constructs a new model for power system development stage identification, and proposes the overall design of the new power system development path from the power supply, transmission and distribution, and load sides. It also uses the Hebei South Network as an example to explore the development stage of the Hebei South Grid based on actual development needs. Finally, this study designs a novel power system development path for the entire supply and demand chain for the Hebei South Grid to propose ideas for constructing a new power system in China and to help green energy transformation.

Comparative Analysis of Photovoltaic Characteristics Using Different Models

The demand for electric energy is increasing day by day. As a result, to balance the supply and demand chains, the dilatability of power production must also be boosted. Instead of generating electric power from conventional energy sources, renewable energies could be thought of, due to considerable longitude and latitude alignment in our country. A photovoltaic module transfers light (photo) energy into an electrical form of energy. However, the need for an hour is to appropriately model the photovoltaic module and extract the inferences in terms of performance characteristics. Currently, the literature survey validates that there are many models to represent a solar cell/PV system. Amongst the prevailing models, a model whose inference is close to the manufacturer’s specifications is to be identified. Thus, in this work, an attempt is made to evaluate different PV models and simulate the same using SIMULINK tools to study the performance and compare it with the manufacturer's specification sheet. The simulation results are tabulated and used to validate the model.

Short-Term Energy Load Forecasting Model with Sample Filtering

Short-term energy load forecasting is a crucial task in the power smart grid, which enables the power utilities to understand the future energy demands and plans to attain the demand and supply equilibrium, thereby optimizing power deployment and reducing power losses. Several techniques have been implemented to enhance energy load forecasting. However, the nonlinear nature of the data collected in the smart grid makes it difficult to attain 100% energy load forecasting accuracy. For instance, the Deep Feedforward Neural Networks model based on Input Attention Mechanism and Hidden Connection Mechanism has a mean absolute percentage error of 3.17%; model based on Sequence to Sequence Recurrent Neural Network with Attention had a mean absolute percentage error of 2.7%. The model based on Deep Recurrent Neural Networks with Levenberg–Marquardt backpropagation algorithm had a mean absolute percentage error of 0.58; and Deep Feedforward Neural Network with sample weights model had 3.22 % as root mean squared error. To improve energy load forecasting accuracy, this work proposed a model based on Deep Recurrent Neural Networks and sample filtering, which provides an exhaustive elucidation for modelling a sophisticated stochastic relationship between the input and output features. Deep Recurrent Neural Networks have proven to be good at modelling the nonlinearities in data of different fields and are mostly used in energy load forecasting to reduce forecasting error and a high degree of overfitting. Sample filtering is achieved through the use of K-Means clustering which determines the number of clusters to be used in the model. Findings from the study showed that by employing Deep Recurrent Neural Networks and sample filtering, the short-term energy load forecasting accuracy is improved in reference to mean absolute percentage error and root mean squared error of 0.31% and 1.014, respectively. As a result of the reduction in error, the energy demand and supply chain equilibrium are enhanced, thereby optimizing power deployment and reducing power losses.
 Keywords: Machine learning, Neural networks, Sample filtering, Smart grid, Short-term energy forecasting

Examining the Relationship Between Supply Chain Integration, Innovation Speed and Supply Chain Performance Under Demand Uncertainty

Under demand uncertainty, supply chain network has to face disruptions in product systems of endogenous factors due to the time needed to adapt to stock production and feedback decisions along the chain of customer demand. This study aims to examine the effects of internal integration and external integration (including supplier integration and customer integration) on innovation speed with the moderation of demand uncertainty. Moreover, this study explores the relationship between supply chain integration, innovation speed and supply chain performance. Our results indicate that internal integration and external integration are positively associated with innovation speed. On the other hand, demand uncertainty moderates the positive relationship between internal integration and innovation speed as well as the relationship between supplier integration and innovation speed. This study provides a more nuanced understanding about the effect of three dimensions of supply chain integration on innovation speed and supply chain performance. More importantly, this study explains the mechanism of using supply chain integration to mitigate the effect of demand uncertainty and complements previous research on innovation.

Understanding demand and supply side factors: a cross-sector analysis from Saudi Arabia

PurposeIntegrating demand with supply has always been an evolving research area, which is affected by different factors including, but not limited to, sustainability, changing consumer demands and emerging supply chain practices. However, most of the research targeted developed countries due to better data access and enhanced information visibility. The research on supply and demand integration in Saudi Arabia is still in its infancy. This study explores this gap by providing a comprehensive analysis of demand and supply chain integration in the context of sustainability. This is done by first creating a developing countries-based research framework and then validating it through surveying 162 Saudi companies operating in multiple sectors like food, fashion and retail. Results suggest that there is more emphasis on supply-side factors relative to demand-side factors in the context of integration. Demand side factors only marginally impact waste reduction efforts. Similarly, the food sector is most sensitive to integration efforts attaching high importance to sustainability and efficiency initiatives. These results can be a useful guide for managers and policymakers who are planning and executing at the interface of demand and supply chains in developing countries.Design/methodology/approachCross-sector analysis by survey questionnaire of 162. Descriptive statistical analyses were performed on the data for the study participants. Continuous variables were summarised using mean and standard deviation (SD), median and IQR; and proportions were used for nominal and ordinal variables. A total score measuring perception of employees towards supply–demand integration was analysed and compared by company-related factors (sector/industry, length of service in firm (years) and the number of employees). Comparisons were made using the chi-square test or ANOVA. A linear regression model was utilised to examine the independent effect of supply and demand side factors on employees' perception of integration. The model controlled for the sector, length of service and number of employees. Statistical significance was considered at p < 0.05.FindingsThe linear regression results showed that supply items significantly predicted integration. That is, higher supply–demand integration scores were significantly related to indicators for using supply variables in supply chain management (SCM) improves operational efficiency (OE), integrating supply variables at the execution stage reduces overall waste and including at least one supply-side variable in supply planning (p < 0.05). The indicator for integrating demand variables at the execution stage reduces overall waste was only marginally related to an increased integration scores (p = 0.089). Lastly, a significantly better perception of supply–demand integration was reported by employees in the food sector compared to those in the fashion sector (p = 0.018). Length of service or number of employees in the firm was not related to perception towards supply–demand integration.Research limitations/implicationsThough accessing 162 companies in the Saudi market is a significant sample size given the fact that they are quite conservative in sharing supply and demand information. However, for better generalisation and richer insights, more sectors like manufacturing, oil and gas and hospitality should be added in future. Cross-country analysis from developing countries can be another interesting future direction.Originality/valueThis empirical paper draws its strength from the scale and depth of empirical data collected from Saudi companies belonging to multiple sectors to better understand demand and supply chain integration, especially from sustainability and waste reduction. This is true in the era of the pandemic and its associated risks for both upstream and downstream. The developing countries' perspective is important as not enough is written in the literature given the difficulties in accessing the information and subsequent analysis. The insights are bound to be unique as a consumer in these countries is undergoing significant socioeconomic changes affecting the supply chain of the companies.

Research on the Construction of Manufacturing Industry Chain Ecosystem—A Case Study of Tianjin Manufacturing Industry

This study takes Tianjin as an example to analyze how to build the manufacturing industry chain ecosystem. Based on the ecosystem theory, the related literature of manufacturing industry chain and value chain, and combined with various action plans for the development of manufacturing industry in Tianjin, the structure chart and construction roadmap of industrial chain ecosystem were drawn. Based on the input–output data of 42 sectors in Tianjin, this study calculated and analyzed the changes in embedment degree of various manufacturing industries in Global Value Chain (GVC) and National Value Chain (NVC) in Tianjin from 2010 to 2017. It is found that the industrial chain ecosystem is mainly composed of four elements: the enterprises on the industrial chain, the information flowing among enterprises, the goods circulating among enterprises, and the external environment of enterprises. The connection of supply and demand chains, enterprise chains, space chains and value chains forms an industrial chain, and the value chain is a crucial relationship chain in the connection mechanism. In addition, 2015 is a turning point for Tianjin manufacturing industry to embed in GVC and NVC, mainly due to the industrial economic adjustment in Tianjin, the global economic turmoil, and the establishment of the manufacturing industrial park. In terms of R&D intensity, high and medium high-tech manufacturing industries tend to be embedded in GVC, but are easily affected by enterprises and environmental factors. However, low and medium low-tech manufacturing industries still have ample space to integrate into GVC, information and products are important factors to determine the upgrading of its industrial chain. Therefore, in order to better construct the industrial chain ecosystem of manufacturing industry and enhance the industrial competitiveness of Tianjin’s manufacturing industry, it is necessary to give priority to the development of high-tech manufacturing industry, expand the international openness of low-tech manufacturing industry, support regional advantageous industries, and carry out dynamic regulation of industrial ecology.

Real-time monitoring of food price policy interventions during the first two years of COVID-19

COVID-19 has resulted in a shock to agrifood systems around the world, with the potential for low- and middle-income countries to be particularly affected. Although policy responses were more muted than during the 2007–2008 world food crisis, efforts to insulate from supply shocks and ensure local availability during COVID-19 have generally included export restrictions and import tariff reductions, among other responses. In an effort to enable rapid market monitoring and realignment, we develop a new indicator defined as a monthly nominal rate of protection “express” which seeks to indicate how policies enacted are affecting prices domestically in real-time in order to understand how they responded. This analysis examines changes to this indicator during the first two years of the pandemic in 24 low- and middle-income countries for the most-consumed staple cereals of the poor and food insecure. We show that gaps between domestic and international prices declined by a median of 20.3 percentage points compared to the same months in recent previous years. While policies were enacted to mitigate price increases that would have eventually been transmitted to poor consumers, other factors related to international demand and supply chain disruptions may also have contributed to the observed trend in the analyzed countries. Moreover, impacts on prices varied across countries and commodities, depending on region, net trade, and previous gap levels. Finally, this indicator can contribute to examining primary drivers of changes and conducting causal analysis to facilitate adequate agrifood policy responses to support economic recovery in the post-COVID-19 era.

Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning

Despite the efforts of the World Health Organization, blood transfusions and delivery are still the crucial challenges in blood supply chain management, especially when there is a high demand and not enough blood inventory. Consequently, reducing uncertainty in blood demand, waste, and shortages has become a primary goal. In this paper, we propose a smart platform-oriented approach that will create a robust blood demand and supply chain able to achieve the goals of reducing uncertainty in blood demand by forecasting blood collection/demand, and reducing blood wastage and shortage by balancing blood collection and distribution based on an effective blood inventory management. We use machine learning and time series forecasting models to develop an AI/ML decision support system. It is an effective tool with three main modules that directly and indirectly impact all phases of the blood supply chain: (i) the blood demand forecasting module is designed to forecast blood demand; (ii) blood donor classification helps predict daily unbooked donors thereby enhancing the ability to control the volume of blood collected based on the results of blood demand forecasting; and (iii) scheduling blood donation appointments according to the expected number and type of blood donations, thus improving the quantity of blood by reducing the number of canceled appointments, and indirectly improving the quality and quantity of blood supply by decreasing the number of unqualified donors, thereby reducing the amount of invalid blood after and before preparation. As a result of the system’s improvements, blood shortages and waste can be reduced. The proposed solution provides robust and accurate predictions and identifies important clinical predictors for blood demand forecasting. Compared with the past year’s historical data, our integrated proposed system increased collected blood volume by 11%, decreased inventory wastage by 20%, and had a low incidence of shortages.

Inventory model for instantaneous deteriorating items with time sensitive demand for post COVID-19 recovery

The Covid-19 epidemic has caused substantial obstacles to the supply network globally. Hence there is urgency and necessity to build a model for cash flow in the chain of demand and supply system. This research suggests an inventory model to assist retailers in determining the optimal ordering quantity and replenishment cycle to reduce the total cost in different payment cases. The current study looks toward a partial advance and delays in the payment system considering time-sensitive demand, shortage, and partial backlogging for instantaneous deteriorating items. During the financial crisis, the partial advance and delay-in-payment strategy is planned to keep orders flowing from retailers to suppliers and customers to retailers. The impact of advanced and delayed payments on the total cost of a retailer is examined. To exemplify the model?s application, numerical examples are used. A sensitivity study of critical parameters has been done to identify more sensitive parameters which reveal the clear depiction of present problems.

A demand and supply chain inventory management with probabilistic and time dependent price

In the real world, it has been a challenging part for the vendors and stockholders selling the perishable product. Especially, when price of the product reduces with time, due to high rate of deterioration and eventually vanishes. The products like vegetable scrap, food products, wood stock, etc. comes under this category. In this paper, we develop a demand and supply chain inventory model with deterioration and exponential demand to maximise the profit of a stockholder selling a perishable product. The selling price is probabilistic and a decreasing function of time so that, after a finite time limit the product has no market value. We consider three probability distributions uniform, triangular and gamma, and compare the total profit under three distributions and represent the result graphically. We perform sensitivity analysis and interpret the result both theoretically and graphically. Further, we study the model under different situation and provide managerial implications.

Development of production cooperation in Russia: Quantitative measurement

<abstract> <p>We analyze the dynamics of the production cooperation development in Russia in 2011–2020 on the basis of Input-Output tables. For this purpose, we use indicators that characterize the degree of fragmentation of production and help assess enterprises' interaction in input demand and output supply chains.</p> </abstract>

Tracking and unpacking the impact of Covid-19 on informal food traders in South Africa

In developing economies like South Africa, the informal traders account for more than half of the Gross Domestic Product (GDP) and over 70% of the employment. The primary objective of this conceptual paper is to establish the extent to which the socio-economic aspects of informal food traders have been impacted by the COVID-19 pandemic with special attention given to South Africa. Desktop research was adopted as the methodological approach in the form of literature review taking lens of qualitative-interpretative research paradigm. The study demonstrates that COVID-19 pandemic had huge adverse impact on informal food traders. Hence, this study plays an integral role to inform government about the integrated responses from the informal traders in respect of the pandemic and policies implemented. The study also provides amicable insights and recommendations regarding disruptions aligned to incomes, food demand, food trade and supply chains of food particularly among vulnerable and poor fragments of the South African population, posed by future pandemic of the same or similar nature.   JEL Classification: M31, M38, M30

Will consumers pay more for organic logo: an empirical study of organic products purchasing in Thailand

This study aimed to investigate the relationship between the willingness to pay for the quality of agricultural products toward the logo of consumers in different regions of Thailand and explained an opportunity for organic products in future markets. 408 respondents from February – June 2019 were collected based on paper-based questionnaires that focused on consumers’ choices, recognition, and attitudes regarding safety and organic knowledge. Three hypotheses were in this study. A choice experiment logit model was used. The questions included socioeconomic, attitudes, preferences, perception of safety and organic products related to logo. The study showed respondents who understood in the concept of safety and organic agriculture and had information logo standards tended to purchase organic products. The organic logo has impacted the purchasing power of respondents. Consumers were willing to pay more for third-party certified logo products (Organic Thailand and IFOAM) rather than local logos (PGS). Satisfaction with marketing, quality standard, and prices did not increase consumers’ demand while they pay for higher prices based on their interest and understanding. Consumers were willing to trade off between trusted logos and a higher price. The supported knowledge on production and markets from public and private agencies were beneficial along the supply and demand chain and agricultural society. To enlarge the organic product markets in the future, these are not well-researched factors that impact third-party and local logos. There were several studies about the perception levels, interest, understanding, and knowledge of the respondents’ impact on the purchasing power of organic products in Thailand.

Performance analysis of biogas net metering with the grid-connected station

Energy demand in Pakistan has increased surprisingly for a few years. The current generation of Pakistan is mainly reliant on thermal (fossil fuels) and crude oils. Pakistan is facing an energy gap of 5000–6000 MW between the demand and supply chain. The difference in Energy demand and energy generation for Pakistan is major causes of power system blackouts, power tripping, and load shedding problem. This entity intrigues the government of Pakistan to open the door for Alternative Energy Sources (AES) i.e. biogas, wind and solar, etc. Biogas becomes a vital source of energy because of technological evolution, free source, environmental friendly, consumer insistence and different state policies. Integration of biogas generation through Net Metering (N-M) with electricity grid at different levels (domestic, commercial and industrial level) can minimize the energy deficiency. Present research investigates the biogas Net Metering in Pakistan. Analysis has been performed through ETAP software. It is observed that the primary burdened bus 21 has a 98.76% voltage level in Case 2, while in Case 1 the voltage profile of Bus 21 is 97.78%. Results show the contribution of Distributed Generation (DG) system in the reduction of power deficit and burden on the utility grid.

Public perceptions of digital fashion: An analysis of sentiment and Latent Dirichlet Allocation topic modeling.

Since digital technology has had a significant impact on the fashion industry, digital fashion has become a hot topic in today's society. Currently, research on digital fashion is focused on the transformation of enterprise marketing strategies and the discussion of digital technology. Despite this, the current study does not include an analysis of the audience's emotional and cognitive responses to digital fashion on social networking platforms. A comprehensive analysis and discussion of 52,891 posts about digital fashion and virtual fashion published on social networking sites was conducted using k-means clustering analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis in this study. The study examines the public's perception and hot topics about digital fashion, as well as the industry's development situation and trends. According to the findings, both positive and neutral emotions accompany the public's attitude toward digital fashion. There is a wide range of topics covered in the discussion. Innovations in digital technology have impacted the creation of jobs, talent demand, marketing strategies, profit forms, and industrial chain innovation of fashion-related businesses. Researchers in related fields will find this study useful not only as a reference for research methods and directions, but also as a source of references for research methodology. A case study and data reference will also be provided to industry practitioners.

Editorial

Editorial

Green for Green: Approach to Sustainable use of Stubble

Abstract: The contemporary zeitgeist of the national capital is rendered in haze and smog due to extensive stubble burning. The paper talks of the various steps the government has already taken and tries to identify the lacuna behind its unjust boom. It also looks at the effective use of various agro wastes and turning them into a 21st-century industry product, for biogas generation or construction material, which can foster a pyramidal development through economic, environmental, and social sustainability. It also identifies the indispensable role of the government not just in creating awareness and structuring better policies but rather in providing opportunities by agglomerating the demand and supply chain to stimulate the waste into revenue generation.

Impact of pricing leadership on blockchain data acquisition efforts in a circular supply chain

Motivated by the industrial observation of the increasing application of blockchain in the supply chain and its improvement in terms of consumer demand, this study examines the impact of investment decisions of blockchain data acquisition nodes upstream and downstream of the supply chain on firms’ product recycling and pricing, market demand, and profits. The influences of different pricing leadership (i.e. supplier-led vs. retailer-led) in a supply chain are also thoroughly investigated. First, the presence of a strong retailer dramatically reduces suppliers’ incentive to invest in blockchain. However, the supplier is still likely to invest more in a retailer-led supply chain than that in a supply chain led by itself under certain conditions; that is, the cost of blockchain is relatively small, or consumers’ sensitivity to blockchain or product recycling benefits is rather large. Second, the total number of blockchain data acquisition nodes, market demand, and supply chain profit in a retailer-led supply chain are higher than in a supplier-led supply chain. Finally, interestingly, the supplier will be better off in a retailer-led supply chain when the return effect of product recycling is relatively high, while the cost-effectiveness of blockchain investment is relatively low.

Effect of Food Quality and Nutritional Attributes on Consumer Choices during the COVID-19 Pandemic

(1) Background: During COVID-19, disruption in food demand and supply chain led to changes in food choices in response to consumer demand, frequency of delivered items, and production setup during a pandemic. The aim of the study was to evaluate the effect of quality and nutritional attributes on consumer food consumption behavior, attitude, and practices. (2) Methods: In this regard, cross-sectional survey research was conducted through a structured questionnaire. (3) Results: The results of the study showed that there was no difference in the receptiveness of COVID-19 infection between both genders. Quality perspective (p = 0.001) was deemed a significant positive predictor in the change of food consumption patterns during the COVID-19 pandemic. It also stated price (p = 0.045) and purity (p = 0.009) as a quality factor while sugar (p = 0.028) and fiber (p = 0.034) content, as nutritional attributes, influenced the consumption frequency of food groups. The overall experience of online shopping was in the neutral category. (4) Conclusions: It was concluded that food quality cues as well as nutritional attributes affected consumer food choices during the COVID-19 pandemic regardless of gender. Online shopping trends were influenced but overall experience remained neutral during the pandemic.

Pricing and coordination of automotive supply chain for incorporating efficient charging facilities

This paper constructs a new energy vehicle and traditional vehicle supply chain model composed of a vehicle manufacturer and an automobile retailer, compares the pricing, demand and supply chain profit of the two types of vehicles under decentralized decision-making and centralized decision-making, and then designs a revenue sharing + ex-factory price negotiation contract mechanism to coordinate the supply chain under certain conditions. The results show that: when the contract parameters meet certain conditions, the combination contract can achieve supply chain coordination and improve the profit of each member. In addition, we further study the impact of the perfection of charging facilities on the pricing, demand and supply chain profit of the two types of vehicles.