Search results for: sales demand forecasting

Authors: Revoti Prasad Bora, Nikita Katyal

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Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, Cannibalization, promotion, Baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: Sutanto, Alexander Christy, N. Sutrisno

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The demand of a product is linearly dependent on the price and quality of the product. It is analog to the demand of the employee in job-seeker problem. This paper address a closed-loop supply chain (CLSC) where a university plays role as manufacturer that produce graduates as job-seeker according to the demand and promote them to a certain corporation through a trial. Unemployed occurs when the job-seeker failed the trial or dismissed. A third party accomodates the unemployed and sends them back to the university to increase their quality through training.

Keywords: CLSC, price, quality, job-seeker problem

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Authors: Hueiwang Anna Jeng, Norou Diawara, Nancy Welch, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

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Modeling effort is needed to predict the COVID-19 trends for developing management strategies and adaptation measures. The objective of this study was to assess whether SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalization cases, and death cases using copula-based time series modeling. SARS-CoV-2 RNA load in raw wastewater in Chesapeake VA was measured using the RT-qPCR method. Gaussian copula time series marginal regression model, incorporating an autoregressive moving average model and the copula function, served as a forecasting model. COVID-19 cases were correlated with wastewater viral load, hospitalization cases, and death cases. The forecasted trend of COVID-19 cases closely paralleled one of the reported cases, with over 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported cases. Wastewater SARS-CoV-2 viral load could serve as a predictor for COVID-19 cases and hospitalization cases.

Keywords: COVID-19, modeling, time series, copula function

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Authors: Nop Sopipan

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In this paper, we forecast the volatility of Baht/USDs using Markov Regime Switching GARCH (MRS-GARCH) models. These models allow volatility to have different dynamics according to unobserved regime variables. The main purpose of this paper is to find out whether MRS-GARCH models are an improvement on the GARCH type models in terms of modeling and forecasting Baht/USD volatility. The MRS-GARCH is the best performance model for Baht/USD volatility in short term but the GARCH model is best perform for long term.

Keywords: volatility, Markov Regime Switching, forecasting, Baht/USD

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Authors: Min Wei

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The traditional prediction methods of the forecast for tourism market are paid more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. With the application of Linear Regression Model, this paper attempts to construct a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation. The findings show is that a scientific evaluation system can implement the scientific concept of development, the harmonious development of man and nature co-ordinate.

Keywords: linear regression model, tourism market, forecast, tourism economics

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Authors: Yazid Abdullahi Abubakar, Jay Mitra

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This paper is concerned with entrepreneurial high-impact firms, which are firms that generate ‘both’ disproportionate levels of employment and sales growth, and have high levels of innovative activity. It investigates differences in factors influencing high-impact growth between indigenous and foreign firms. The study is based on an analysis of data from United Kingdom (UK) Innovation Scoreboard on 865 firms, which were divided into high-impact firms (those achieving positive growth in both sales and employment) and low-impact firms (negative or no growth in sales or employment); in order to identifying the critical differences in regional, sectorial and size related factors that facilitate knowledge spillovers and high-impact growth between indigenous and foreign firms. The findings suggest that: 1) Firms’ access to regional knowledge spillovers (from businesses and higher education institutions) is more significantly associated with high-impact growth of UK firms in comparison to foreign firms, 2) Because high-tech sectors have greater use of knowledge spillovers (compared to low-tech sectors), high-tech sectors are more associated with high-impact growth, but the relationship is stronger for UK firms compared to foreign firms, 3) Because small firms have greater need for knowledge spillovers (relative to large firms), there is a negative relationship between firm size and high-impact growth, but the negative relationship is greater for UK firms in comparison to foreign firms.

Keywords: entrepreneurship, high-growth, indigenous firms, foreign firms, small firms, large firms

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Authors: José M. Brotons, Manuel E. Sansalvador

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The relationship between Corporate Social Responsibility (CSR) and financial performance (FP) is a subject of great interest that has not yet been resolved. In this work, we have developed a new and original tool to measure this relation. The tool quantifies the value contributed to companies that are committed to CSR. The theoretical model used is the fuzzy discounted cash flow method. Two assumptions have been considered, the first, the company has implemented the IQNet SR10 certification, and the second, the company has not implemented that certification. For the first one, the growth rate used for the time horizon is the rate maintained by the company after obtaining the IQNet SR10 certificate. For the second one, both, the growth rates company prior to the implementation of the certification, and the evolution of the sector will be taken into account. By using triangular fuzzy numbers, it is possible to deal adequately with each company’s forecasts as well as the information corresponding to the sector. Once the annual growth rate of the sales is obtained, the profit and loss accounts are generated from the annual estimate sales. For the remaining elements of this account, their regression with the nets sales has been considered. The difference between these two valuations, made in a fuzzy environment, allows obtaining the value of the IQNet SR10 certification. Although this study presents an innovative methodology to quantify the relation between CSR and FP, the authors are aware that only one company has been analyzed. This is precisely the main limitation of this study which in turn opens up an interesting line for future research: to broaden the sample of companies.

Keywords: corporate social responsibility, case study, financial performance, company valuation

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Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

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Authors: Mohammed Ibrahim Girei

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Adamawa state is one the major producers of both crop and animals in Nigeria. Agricultural production serves as the major means livelihood of the people in the state. However, the agricultural activities of the farmers in the state are at subsistence level. However integration of these small scale farmers in local, national and international market is paramount importance. The paper was designed to categorize farmers based on market participation among the cattle farmers in Adamawa state, Nigeria. The multistage sampling procedure was employed. To achieve this procedure, structured questionnaires were used to collect data from 400 respondents. The data were analyzed using the descriptive statistics. The result revealed that the majority of market participants were net sellers (78.51 %) (Sales greater than purchase), net buyers were (purchase greater than sales) 12.95 % and only 9% were autarkic (sales equal purchase). The study recommends that Government should provide more effective security services in cattle farming communities, which is very important as the market participants in the study area were net sellers (producers), it will help in addressing the problem of cattle rustling and promote more investment in cattle industry. There is a need to establish a standard cattle market, veterinary services and grazing reserves in the area so that to facilitate the cattle production and marketing system in the area and to meet up with the challenging of livestock development as a result of rapid human population growth in developing countries like Nigeria.

Keywords: categories, cattle, farmers, market, participation

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Authors: Djehich Mohamed Yousri

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In this research paper, the future of international relations is believed to have an important place on the theoretical and applied levels because policy makers in the world are in dire need of such analyzes that are useful in drawing up the foreign policies of their countries, and protecting their national security from potential future threats, and in this context, The topic raised a lot of scientific controversy and intellectual debate, especially in terms of the extent of the effectiveness, accuracy, and ability of foresight methods to identify potential futures, and this is what attributed the controversy to the scientific foundations for foreseeing international relations. An arena for intellectual discussion between different thinkers in international relations belonging to different theoretical schools, which confirms to us the conceptual and implied development of prediction in order to reach the scientific level.

Keywords: foresight, forecasting, international relations, international relations theory, concept of international relations

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Authors: Megumi Nishida, Naoya Motegi, Takurou Kikuchi, Tomoko Tokumura

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In recent years, power system has been changed and flexible power pricing system such as demand response has been sought in Japan. The demand response system is simple in the household sector and the owner, decision-maker, can gain the benefits of power saving. On the other hand, the execution of the demand response in the office building is more complex than household because various people such as owners, building administrators and occupants are involved in making decisions. While the owners benefit from the demand saving, the occupants are forced to be exposed to demand-saved environment certain benefits. One of the reasons is that building systems are usually centralized control and each occupant cannot choose either participate demand response event or not, and contribution of each occupant to demand response is unclear to provide incentives. However, the recent development of IT and building systems enables the personalized control of office environment where each occupant can control the lighting level or temperature around him or herself. Therefore, it can be possible to have a system which each occupant can make a decision of demand response participation in office building. This study investigates the personal behavior upon demand response requests, under the condition where each occupant can adjust their brightness individually in their workspace. Once workers participate in the demand response, their task lights are automatically turned off. The participation rates in the demand response events are compared between four groups which are divided by different motivation, the presence or absence of incentives and the way of participation. The result shows that there are the significant differences of participation rates in demand response event between four groups. The way of participation has a large effect on the participation rate. ‘Opt-out’ group, where the occupants are automatically enrolled in a demand response event if they don't express non-participation, will have the highest participation rate in the four groups. The incentive has also an effect on the participation rate. This study also reports that the impact of low illumination office environment on the occupants, such as stress or fatigue. The electrocardiogram and the questionnaire are used to investigate the autonomic nervous activity and subjective symptoms about the fatigue of the occupants. There is no big difference between dim workspace during demand response event and bright workspace in autonomic nervous activity and fatigue.

Keywords: demand response, illumination, questionnaire, electrocardiogram

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Authors: Aishwarya Rai

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This study explores the transformative influence of generative AI on startups, businesses, and industries. We will explore how large businesses can benefit in the area of customer operations, where AI-powered chatbots can improve self-service and agent effectiveness, greatly increasing efficiency. In marketing and sales, generative AI could transform businesses by automating content development, data utilization, and personalization, resulting in a substantial increase in marketing and sales productivity. In software engineering-focused startups, generative AI can streamline activities, significantly impacting coding processes and work experiences. It can be extremely useful in product R&D for market analysis, virtual design, simulations, and test preparation, altering old workflows and increasing efficiency. Zooming into the retail and CPG industry, industry findings suggest a 1-2% increase in annual revenues, equating to $400 billion to $660 billion. By automating customer service, marketing, sales, and supply chain management, generative AI can streamline operations, optimizing personalized offerings and presenting itself as a disruptive force. While celebrating economic potential, we acknowledge challenges like external inference and adversarial attacks. Human involvement remains crucial for quality control and security in the era of generative AI-driven transformative innovation. This talk provides a comprehensive exploration of generative AI's pivotal role in reshaping businesses, recognizing its strategic impact on customer interactions, productivity, and operational efficiency.

Keywords: generative AI, digital transformation, LLM, artificial intelligence, startups, businesses

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Authors: Wheyming Song, Hung-Hsiang Lin, Scott Lian

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In this research, we investigate the optimal dispatching rule for machines and manpower allocation in the solar ingot-wafer systems. The performance of the method is measured by the sales profit for each dollar paid to the operators in a one week at steady-state. The decision variables are identification-number of machines and operators when each job is required to be served in each process. We propose a rule which is a function of operator’s ability, corresponding salary, and standing location while in the factory. The rule is named ‘Multi-nominal distribution dispatch rule’. The proposed rule performs better than many traditional rules including generic algorithm and particle swarm optimization. Simulation results show that the proposed Multi-nominal distribution dispatch rule improvement on the sales profit dramatically.

Keywords: dispatching, solar ingot, simulation, flexsim

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Authors: Rosa M. Senent

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Over the centuries, prostitution has been problematized from many sides, with women always at the center of the debate. However, prostitution is a gendered, demand-driven phenomenon. Thus, a focus must be put on the men who demand it, as an increasing number of studies have been done in the last few decades. The purpose of this paper is to expose how men's discourse online reveals the link between their demand for paid sex in prostitution and sex trafficking. The methodological tool employed was Critical Discourse Analysis (CDA). A critical analysis of sex buyers' discourse online showed that online communities of sex buyers are a useful tool in researching their behavior towards women, that their knowledge of sex trafficking and exploitation do not work as a deterrent for them to buy sex, and that the type of masculinity that sex buyers endorse is characterized by attitudes linked to the perpetuation of violence against women.

Keywords: masculinities, prostitution, sex trafficking, violence

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Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

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This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model

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Authors: Thabo M. Bafitlhile, Adewole Oladele

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Botswana has been affected by floods since long ago and is still experiencing this tragic event. Flooding occurs mostly in the North-West, North-East, and parts of Central district due to heavy rainfalls experienced in these areas. The torrential rains destroyed homes, roads, flooded dams, fields and destroyed livestock and livelihoods. Palapye is one area in the central district that has been experiencing floods ever since 1995 when its greatest flood on record occurred. Heavy storms result in floods and inundation; this has been exacerbated by poor and absence of drainage structures. Since floods are a part of nature, they have existed and will to continue to exist, hence more destruction. Furthermore floods and highway plays major role in erosion and destruction of roads structures. Already today, many culverts, trenches, and other drainage facilities lack the capacity to deal with current frequency for extreme flows. Future changes in the pattern of hydro climatic events will have implications for the design and maintenance costs of roads. Increase in rainfall and severe weather events can affect the demand for emergent responses. Therefore flood forecasting and warning is a prerequisite for successful mitigation of flood damage. In flood prone areas like Palapye, preventive measures should be taken to reduce possible adverse effects of floods on the environment including road structures. Therefore this paper attempts to estimate return periods associated with huge storms of different magnitude from recorded historical rainfall depth using statistical method. The method of annual maxima was used to select data sets for the rainfall analysis. In the statistical method, the Type 1 extreme value (Gumbel), Log Normal, Log Pearson 3 distributions were all applied to the annual maximum series for Palapye area to produce IDF curves. The Kolmogorov-Smirnov test and Chi Squared were used to confirm the appropriateness of fitted distributions for the location and the data do fit the distributions used to predict expected frequencies. This will be a beneficial tool for urgent flood forecasting and water resource administration as proper drainage design will be design based on the estimated flood events and will help to reclaim and protect the road structures from adverse impacts of flood.

Keywords: drainage, estimate, evaluation, floods, flood forecasting

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Authors: Sreto Boljevic

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In the absence of considerable investment in electricity generation, transmission and distribution network (DN) capacity, the demand for electrical energy will quickly strain the capacity of the existing electrical power network. With anticipated growth and proliferation of Electric vehicles (EVs) and Heat pump (HPs) identified the likelihood that the additional load from EV changing and the HPs operation will require capital investment in the DN. While an area-wide implementation of EVs and HPs will contribute to the decarbonization of the energy system, they represent new challenges for the existing low-voltage (LV) network. Distributed energy resources (DER), operating both as part of the DN and in the off-network mode, have been offered as a means to meet growing electricity demand while maintaining and ever-improving DN reliability, resiliency and power quality. DN planning has traditionally been done by forecasting future growth in demand and estimating peak load that the network should meet. However, new problems are arising. These problems are associated with a high degree of proliferation of EVs and HPs as load imposes on DN. In addition to that, the promotion of electricity generation from renewable energy sources (RES). High distributed generation (DG) penetration and a large increase in load proliferation at low-voltage DNs may have numerous impacts on DNs that create issues that include energy losses, voltage control, fault levels, reliability, resiliency and power quality. To mitigate negative impacts and at a same time enhance positive impacts regarding the new operational state of DN, CHP system integration can be seen as best action to postpone/reduce capital investment needed to facilitate promotion and maximize benefits of EVs, HPs and RES integration in low-voltage DN. The aim of this paper is to generate an algorithm by using an analytical approach. Algorithm implementation will provide a way for optimal placement of the CHP system in the DN in order to maximize the integration of RES and increase in proliferation of EVs and HPs.

Keywords: combined heat & power (CHP), distribution networks, EVs, HPs, RES

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Mahsa Ghorbani Jazin

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The insurance industry has a prominent place in the economy of every country in the world. Fire insurance policies are types of non-life insurance, which protect insureds against financial losses of fire and related risks. In this paper, factors that are affecting the demand for fire insurance in Iran have been examined. Due to this reason, information and data have been collected during the period 1989-2019. In this research, the final model was estimated. The obtained results represent that as the population and literacy rate increase, people are more willing to purchase fire insurance. On the other hand, the actual per capita income has a negative influence on the demand for this type of insurance. Also, the amount of compensation that is paid in losses can be assumed as an indirect advertisement for fire insurance and attracts people to buy this policy. Finally, the new technology in the insurance industry is examined as a new underestimated way for increasing demand, especially in Iran.

Keywords: fire insurance, demand, per capita income, literacy rate, population, compensation paid, Insurtech

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Authors: Simiso Fisokuhle Nyandeni

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Climate change has turned out to be a pandemic that has drawn the attention of many countries’ households around the globe, especially those whose livelihood and economic status depend on agricultural productivity. Hence, the agricultural sector is regarded as the sector that is most dependent on climate conditions for its productivity/harvest, yet in recent years this sector has been experiencing drought. However, adaptation seems to be a tool that every farmer looks upon as a solution to their challenges as their productivity keeps on being vulnerable to climate effects. Thus, exposure/access to the market seems to be a major challenge that faces especially small-scale farmers. We, therefore, examine the small-scale farmers’ constraints or challenges towards getting access to the market for them to get proper sales of their farming products. As a result, the adaptation capacity of every farm household varies on the financial status.

Keywords: climate change, small-scale farming, agriculture sector, adaptation

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Authors: Kwa-Sur Tam, Byung O. Kang

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By using different ranges of daily sky clearness index defined in this paper, any day can be classified as a clear sky day, a partly cloudy day or a cloudy day. This paper demonstrates how short-term forecasting of solar irradiation can be improved by taking into consideration the type of day so defined. The source of day type dependency has been identified. Forecasting methods that take into consideration of day type have been developed and their efficacy have been established. While all methods that implement some form of adjustment to the cloud cover forecast provided by the U.S. National Weather Service provide accuracy improvement, methods that incorporate day type dependency provides even further improvement in forecast accuracy.

Keywords: day types, forecast methods, National Weather Service, sky cover, solar energy

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Authors: Rita U. Onolemhemhen, Saheed L. Bello, Akin P. Iwayemi

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The effectiveness of energy demand policy depends on identifying the key drivers of energy demand both in the short-run and the long-run. This paper examines the influence of regional differences on the link between energy demand and other explanatory variables for Nigeria, China and USA using the Vector Error Correction Model (VECM) approach. This study employed annual time series data on energy consumption (ED), real gross domestic product (GDP) per capita (RGDP), real energy prices (P) and urbanization (N) for a thirty-six-year sample period. The utilized time-series data are sourced from World Bank’s World Development Indicators (WDI, 2016) and US Energy Information Administration (EIA). Results from the study, shows that all the independent variables (income, urbanization, and price) substantially affect the long-run energy consumption in Nigeria, USA and China, whereas, income has no significant effect on short-run energy demand in USA and Nigeria. In addition, the long-run effect of urbanization is relatively stronger in China. Urbanization is a key factor in energy demand, it therefore recommended that more attention should be given to the development of rural communities to reduce the inflow of migrants into urban communities which causes the increase in energy demand and energy excesses should be penalized while energy management should be incentivized.

Keywords: economic growth, energy demand, income, real GDP, urbanization, VECM

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Authors: Fatemeh Torfi

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Estimation of stochastic demand in physical distribution in general and efficient transport routs management in particular is emerging as a crucial factor in urban planning domain. It is particularly important in some municipalities such as Tehran where a sound demand management calls for a realistic analysis of the routing system. The methodology involved critically investigating a fuzzy least-squares linear regression approach (FLLRs) to estimate the stochastic demands in the vehicle routing problem (VRP) bearing in mind the customer's preferences order. A FLLR method is proposed in solving the VRP with stochastic demands. Approximate-distance fuzzy least-squares (ADFL) estimator ADFL estimator is applied to original data taken from a case study. The SSR values of the ADFL estimator and real demand are obtained and then compared to SSR values of the nominal demand and real demand. Empirical results showed that the proposed methods can be viable in solving problems under circumstances of having vague and imprecise performance ratings. The results further proved that application of the ADFL was realistic and efficient estimator to face the stochastic demand challenges in vehicle routing system management and solve relevant problems.

Keywords: fuzzy least-squares, stochastic, location, routing problems

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Authors: Siddhartha Paul, Debabrata Das

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Closed-loop supply chain (CLSC) management focuses on integrating forward and reverse flow of material as well as information to maximize value creation over the entire life-cycle of a product. Bullwhip effect in supply chain management refers to the phenomenon where a small variation in customers’ demand results in larger variation of orders at the upstream levels of supply chain. Since the quality and quantity of products returned to the collection centers (as a part of reverse logistics process) are uncertain, bullwhip effect is inevitable in CLSC. Therefore, in the present study, first, through an extensive literature survey, we identify all the important factors related to forward as well as reverse supply chain which causes bullwhip effect in CLSC. Second, we develop a system dynamics model to study the interrelationship among the factors and their effect on the performance of overall CLSC. Finally, the results of the simulation study suggest that demand forecasting, lead times, information sharing, inventory and work in progress adjustment rate, supply shortages, batch ordering, price variations, erratic human behavior, parameter correcting, delivery time delays, return rate of used products, manufacturing and remanufacturing capacity constraints are the important factors which have a significant influence on system’s performance, specifically on bullwhip effect in a CLSC.

Keywords: bullwhip effect, closed-loop supply chain, system dynamics, variance ratio

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Authors: Reza Beheshti

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This paper examines remedies for defective manufactured goods in commercial circular economic transactions. The linear ‘take-make-dispose’ model fits well with the conventional remedial framework in which damages are considered the primary remedy. Damages under English Sales Law encourages buyers to look for a substitute seller with broadly similar goods to the ones agreed on in the original contract, enter into contract with this new seller and hence terminate the original contract. By doing so, the buyer ends the contractual relationship. This seems contrary to the core principles of the circular economy: keeping products, components, and materials in longer use, which can partly be achieved by product refurbishment. This process involves returning a product to good working condition by replacing or repairing major components that are faulty or close to failure and making ‘cosmetic’ changes to update the appearance of a product. This remedy has not been widely accepted or applied in commercial cases, which in turn flags up the secondary nature of performance-related remedies. This paper critically analyses the laws concerning the seller’s duty to cure in English law and the extent to which they correspond with core principles of the circular economy. In addition, this paper takes into account the potential of circular economic transactions being characterised as something other than sales. In such situations, the likely outcome will be a license to use products, which may limit the choice of remedy further. Consequently, this paper suggests an outline remedial framework specifically for commercial circular economic transactions in manufactured goods.

Keywords: circular economy, contract law, remedies, English Sales Law

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Mojtaba Molaahmadi, Morteza Raei Dehaghi, Abdolrahim Arghavan

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The present study aims to explore the effect of computerization on marketing performance in Snowa Company. In other words, this study intends to respond to this question that whether or not there is a relationship between utilization of computerization in marketing activities and marketing performance. The statistical population included 60 marketing managers of Snowa Company. In order to test the research hypotheses, Pearson correlation coefficient was employed. The reliability was equal to 96.8%. In this study, computerization was the independent variable and marketing performance was the dependent variable with characteristics of market share, improving the competitive position, and sales volume. The results of testing the hypotheses revealed that there is a significant relationship between utilization of computerization and market share, sales volume and improving the competitive position

Keywords: computerization, e-marketing information, information technology, marketing performance

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Authors: Lu Liao

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This paper studies the impact of private labels on the advertising incentives of national brands. The worldwide expansion of private labels over the past two decades not only transformed the choice sets of consumers but also forced manufacturers of national brands to design new marketing strategies to maintain their market positions. This paper first develops a consumer demand model that incorporates spillover effects of advertising for antacids, including private labels and finds positive spillovers of national brands’ advertising on demand for private label antacids. With the demand estimates, it provides a simulation for the equilibrium prices and advertising levels for leading national brands in a counterfactual where private labels are eliminated to quantify national brands’ advertising incentives as a response to the rise of private labels.

Keywords: advertising, private label, marketing, demand

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Authors: Hasibe Berfu Demi̇r, Şakir Esnaf

Abstract:

In the past decade, extensive improvements have been done in electrical grid infrastructures. It is very important to make plans on supply, demand, transmission, distribution and pricing for the development of the electricity energy sector. Based on this perspective, in this study, Stackelberg game approach is proposed for demand participation management (DRM), which has become an important component in the smart grid to effectively reduce power generation costs and user bills. The purpose of this study is to examine electricity consumption from a dynamic pricing perspective. The results obtained were compared with the current situation and the results were interpreted.

Keywords: lectricity, stackelberg, smart grid, demand response managment, dynamic pricing

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