Sales Demand Research Articles

The Impact of Supply Chain Delays on Inventory Levels and Sale Demand Fulfillment: Analyzing the Effects of Lead Times and In-Transit Quantities

The Impact of Supply Chain Delays on Inventory Levels and Sale Demand Fulfillment: Analyzing the Effects of Lead Times and In-Transit Quantities

MARKET OF PRODUCTS OF THE FEED INDUSTRY IN TODAY'S CONDITIONS

The article is devoted to the study of the state and prospects for the development of the feed industry market in Ukraine. The main reasons for the unstable activity of subjects of compound feed production, such as the high sensitivity of the market to changes in the grain industry, the limitation of the available raw material base, the lack of high-tech production in the necessary volumes, the constant increase in costs for the production of compound feed products, the insufficient development of the market infrastructure, the lack of sufficient solvent demand. The war in Ukraine affected the demand for compound feed products, production, sales, and logistics. The article provides statistical data of the Ukrainian market in 2022–2023 on the volume of compound feed production and the prices of compound feed components, namely grain and oil crops. Considering the export problems in Ukraine, raw materials are competitive in price policy. This situation is favorable for the Ukrainian production of compound feed, which, in turn, allows to increase the production of animal husbandry products . State influence in the direction of vertical integration makes it possible to control the beginning of the production chain, primarily grain producers to ensure the sale of agricultural products, protection from price fluctuations in raw materials markets, and flexible response to consumer requests. State stimulation of research and innovation , such as providing financial support and incentives for research in the field of compound fodder production, will contribute to the development of new technologies , improvement of feed quality, and increased competitiveness . To date, compound feed producers have overcome infrastructure challenges associated with the loss of logistics chains, damaged businesses, and demonstrated positive development trends and sustainable economic performance.

Analysis of sales demand and production capacity to determine the need for direct labour

This study consists of meeting the sales demand using the existing production capacity in an electronic goods industry and thus determining the amount of direct labor (MOD) necessary to meet the needs of customers. Since obtaining the correct number of operators on a production line is extremely important in the pursuit of productivity and cost reduction. Therefore, using concepts such as takt time, cycle time and line efficiency and other information related to the product or products that will be assembled on a production line, it will be possible to quickly and assertively determine the number of operators that must serve directly the client's demand, thus guaranteeing productive efficiency and the correct application of labor resources using as assumptions the capacity and equipment already existing in the production line. Thus, based on the company's sales projection, it will be possible to determine month by month the number of operators, number of shifts and the appropriate volume to be produced to meet the requested demand.

Enhancing Sales Demand Prediction for Supply Chain Management: A Dimensionality Reduction Approach at Mukwano Company Limited, Uganda

In the dynamic landscape of today's business environment, the accurate prediction of sales demand plays a pivotal role for companies striving to maintain competitiveness. This study focuses on refining demand sales prediction for supply chain management at Mukwano Industries Limited through the application of dimensionality reduction techniques. The research involved a systematic iterative process encompassing problem identification, data preparation, modeling, evaluation, validation, optimization, and documentation. To ensure confidentiality, secondary data from the Mukwano IT department underwent meticulous merging and anonymization. Four dimensionality reduction algorithms, namely PCA, SVD, MDS, and t-SNE, were employed and evaluated using RMSE metrics. The results reveal that MDS and t-SNE exhibited exceptional performance, achieving accuracies of 89% and 88.8%, respectively. PCA and SVD also demonstrated commendable performance with an accuracy of 82.4%. The study underscores the crucial role of dimensionality reduction in enhancing predictive accuracy and optimizing inventory management. Recommendations include the incorporation of feature selection and regularization techniques to address the challenges associated with the curse of dimensionality. In conclusion, this research contributes valuable insights into the effectiveness of diverse dimensionality reduction techniques for demand prediction and inventory management. Additionally, the study highlights the need to address the curse of dimensionality and suggests exploring further research avenues in other aspects of supply chain management. These recommendations are essential for guiding future research efforts in this evolving field. Keywords: Demand sales prediction, Supply chain management, Feature selection, Inventory management, Predictive accuracy

Initial Business Development of PT CNG with Implementation of the Mutualism Network Strategy (Munets) Model

The development of a business by a company is determined by the ability to develop strategies. Every company needs to use a strategy to expand its business. Not only large companies but small companies or companies that are going to start a business should also use strategic management. CNG Indonesian Produk is one of many new distribution manufacturing companies working in fashion, food, and agriculture. Brands that are still relatively new can become a fundamental problem, thus hindering the marketing process to be carried out. This study aims to develop appropriate strategies for developing the CNG Indonesian Product business by expanding the company's network which will have an impact on increasing demand and company productivity. Mutualism Network Strategy (Munets) is the model used in this study. Munets is a marketing strategy that utilizes pre-existing networks and newly formed networks to produce a symbiosis of mutualism that is mutually beneficial for both business owners and parties involved in the network formed. In its application, the increase in sales demand increased 5x compared to before the implementation of Munets. So it can be concluded that the Munets model can expand the company's partner network which can increase sales demand and produce a mapping of the needs of each company division and strategic application proposals. Of course, it is necessary to control the implementation of the Munets model and the application of other business strategy methods in the development of the Indonesian CNG Product company.

Increasing exercise participation during the COVID-19 pandemic: the buffering role of nostalgia.

Due to the coronavirus disease 2019 (COVID-19) pandemic, people faced difficulties engaging in exercise activities as usual. As a result, there has been an increase in the demand for home exercises and online sales. However, there is little research on individuals' buying and exercise behaviors during the pandemic. Thus, this study investigated how the perceived threat of COVID-19 influences exercise participants' compensatory consumption and exercise intention through emotional responses, such as feelings of solastalgia and nostalgia. A total of 488 responses were collected from Generation Y, as individuals belonging to Generation Y are more prone to impulsive buying compared to other generations and, importantly, consider exercise a crucial component of their general well-being. Data were examined using a three-step method that involved the use of SPSS 26.0 and EQS 6.4 software. Results showed that perception of COVID-19 positively influenced solastalgia and negatively affected nostalgia. Also, solastalgia had positive effects on nostalgia and online browsing, and nostalgia positively affected online browsing. Finally, this study found that online browsing positively influenced impulse buying and exercise intention, while impulse buying did not significantly affect exercise intention. This study contributes to identifying the crucial influence of emotions in decision-making and increasing the understanding of the connection between nostalgia and cognitive and emotional responses amid the COVID-19 pandemic.

Intelligent Design Suggestion and Sales Forecasting for New Products in the Apparel Industry

Abstract This study demonstrates how algorithms can assist humans in decision-making in the apparel industry. A two-stage method including suggestions and intelligent forecasting was proposed. In the first stage, a web crawler was used to browse a B2C apparel website to identify popular products. In the second stage, machine learning methods were used to predict the sales demand for new products. Additionally, we used Google Trends to collect external information indices to adjust the demand forecasting. Our numerical study shows that the intelligent forecasting approach can effectively reduce the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by at least 45.79, 26.35, and 26.34 %, respectively.

Sales Demand Forecasting in Car Industry Using Seagull Optimization Based Holt Winter and Quantile Regression Neural Network

Sales Demand Forecasting in Car Industry Using Seagull Optimization Based Holt Winter and Quantile Regression Neural Network

Analyzing drivers of organic food sales-A pooled spatial data analysis for Hamburg (Germany).

Shifting the food system to a more sustainable one requires changes on both sides of the supply chain, with the consumer playing a key role. Therefore, understanding the factors that positively correlate with increased organic food sales over time for an entire population can help guide policymakers, industry, and research to increase this transition further. Using a statistical approach, we developed a spatial pooled cross-sectional model to analyze factors that positively correlate with an increased demand for organic food sales over 20 years (1999-2019) for an entire region (the city-state of Hamburg, Germany), accounting for spatial effects through the spatial error model, spatially lagged X model, and spatial Durbin error model. The results indicated that voting behavior strongly correlated with increased organic food sales over time. Specifically, areas with a higher number of residents that voted for a political party with a core focus on environmental issues, the Greens and the Left Party in Germany. However, there is a stronger connection with the more "radical" Left Party than with the "mainstream" Green Party, which may provide evidence for the attitude-behavior gap, as Left Party supporters are very convinced of their attitudes (pro-environment) and behavior thus follows. By including time and space, this analysis is the first to summarize developments over time for a metropolitan population while accounting for spatial effects and identifying areas for targeted marketing that need further motivation to increase organic food sales.

The Prediction of Douyin Live Sales based on Neural Network Algorithms

This paper aims to optimize neural network algorithms in order to improve their predictive performance and meet the demand for Douyin live sales forecasting. The Douyin live data of Florasis between January 2022 and June 2022 were collected from Huitun data and preprocessed. Using the Pearson correlation coefficient, six factors that are highly correlated with live sales were selected for subsequent prediction. This paper briefly introduces the back-propagation neural network (BPNN) algorithm and analyzes its parameter optimization methods, including particle swarm optimization (PSO), the artificial bee colony (ABC) algorithm, and the beetle antler search (BAS) algorithm. Then, an improved beetle antennae search (IBAS) algorithm was developed by introducing inertia weight and used to construct an IBAS-BPNN model for predicting sales volume in Douyin live streaming. The results showed that compared with the BPNN, PSO-BPNN, and ABC-BPNN algorithms, the IBAS-BPNN algorithm had better prediction performance, with a root-mean-square error of 335.6694, a mean absolute percentage error of 0.0532%, an equilibrium coefficient of 0.9889, and a shorter training time of 90.07 s. The experimental results demonstrate the reliability of the IBAS-BPNN algorithm for predicting sales volume in Douyin live streaming, providing new insights into parameter optimization of BPNN and offering references for further research on BPNN parameter optimization. It also provides an effective method with both timeliness and high accuracy for predicting sales volume in Douyin live streaming in practical applications. Doi: 10.28991/HIJ-2023-04-02-09 Full Text: PDF

СТАН ТА ТЕНДЕНЦІЇ РОЗВИТКУ ВІДЧИЗНЯНОГО РИНКУ КОМБІКОРМІВ ТА БІОЛОГІЧНИХ МІНЕРАЛЬНО-ВІТАМІННИХ ДОБАВОК

The article is dedicated to the study of the state and prospects of the market for compound feeds and biological mineral-vitamin supplements in Ukraine. The article provides statistical data on the production and consumption volumes of feeds and supplements on the domestic market up to 2022 and in 2022, particularly during the Russian-Ukrainian war. The authors analysed the factors affecting the demand for these products, such as the increase in animal populations, the expansion of animal husbandry scales, the development of production technologies, and more. The trends in the development of the market for compound feeds and supplements are also highlighted, including changes in demand for feeds and supplements for domestic animals, reasons for the decline or increase in demand for these products, trends in the coming years, and related products. The authors highlight the importance of developing national production of compound feeds and supplements, which will provide independence from imports, increase competitiveness, and improve product quality. An important factor that affected the demand for compound feed products, production, sales, and logistics of these products was the war. The article will investigate the impact of military operations on the activities of farms, agroholdings, and other institutions that purchased compound feeds, biological mineral-vitamin supplements, and other products in bulk and how this changed demand, among other things. In addition, it is worth noting the importance of establishing cooperation between feed producers and farmers. Producers must be ready to meet farmers' needs and understand their requirements, which will help maintain the competitiveness of the national market. The development of scientific research institutes that study technologies and develop new compound feeds and supplements is also essential. The article is an important source of information for professionals in the animal husbandry industry, producers of compound feeds and supplements, and those interested in the development of the domestic food market and national security.

Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study

Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 7525.

Implementation of Business Profit Planning Cost of Production and Cost Volume Profit Analysis in Small and Medium Enterprises at ABC Bakery

Implementing family cooperative-based Small and Medium Enterprises (SMEs) activities in Indonesia is one of the priorities in sustainable national economic development. ABC Bakery SMEs also carries out this activity, which produces donut products. The implementation of operating profit planning in cost of production, which was applied during field research, aims to make ABC Bakery SMEs financial planning able to generate healthy profits without excessive worries about the condition of wheat stocks and the demand for sales of donuts in the market which has decreased and resulted in losses. The study’s results obtained the calculation of the Cost of Production (HPP), resulting in a unit price of IDR 2,018. In the second stage, the researcher carried out BEP calculations with the results of the number of donuts that had to be sold, namely 18 units of chocolate sprinkle donuts and nine units of cheese donuts. The BEP calculation is then followed by an analysis of the calculation of profit so that it can cover production costs, so the profit target that must be obtained is IDR 100,000 per day, with ABC Bakery SMEs having to sell 41 units of chocolate sprinkle donuts and 20 units of cheese donuts.

Oportunidade ou necessidade? O caso da ZM S/A

Abstract This teaching case portrays the dilemma of ZM S/A, an auto parts industry, regarding the disruption of B2B to B2C sales channels. With the growing demand for sales through online channels due to the COVID-19 pandemic, the company puts into perspective the feasibility of starting direct sales operations (B2C) through the creation of its own e-commerce or the promotion of its distributors’ online sales. The purpose of the case is to promote reflection on the strategic changes in sales channels and the potential conflicts of creating an e-commerce platform. This is a real case that uses the company’s name and replaces the names of individuals involved for privacy. The construction of the case relied on primary sources, through interviews with those involved, and on secondary sources, through sectoral data. The case can be applied in the final years of undergraduate programs in business administration and graduate programs in strategic marketing and digital marketing, specifically in courses related with business management, organization and marketing strategies, operations management, and innovation.

Penerapan Anggaran Penjualan dalam Memperkirakan Penjualan

This study aims to estimate the income that will be obtained for the months of June to December 2022 at the Panglong Tarclo Jaya business and to determine the preparation of the sales budget so that the sales level is in accordance with the sales demand desired by consumers. Data collection techniques used in this study are: observation, interviews, and literature study. Technical analysis of data in forecasting sales is the method of trend moment and least square. After the sales forecast results are found, the company makes a Forecasting Standard Error (SKF) to compare between the two methods which method is the most appropriate to use. To compare the two methods, the Standard Error Forecast (SKF) is used. The results of the research through the SKF that have been carried out show the trend moment method with an SKF value of 78.04, and the least square method with an SKF value of 77.8. The better method to use is the least square method because the difference in the SKF value is 0.24 smaller. So the company uses the least square method in forecasting the sales budget. The sales budget for bricks in Panglong Tarclo Jaya from July 2022 to December 2022 is Rp. 399,277,500 with total sales of 798,600 pieces.

Strategy and cost stickiness under different managerial abilities: Evidence from Southeast Asia

Firms’ strategies reflect their competitive advantages, such as the prospector focuses on innovation and defenders focus on cost efficiency. The differences in strategies’ competitive advantages imply different resource management to different magnitude of cost stickiness. This study examined whether prospectors and defenders exhibit different magnitude of cost stickiness. This study also examines cost stickiness differences in high and low managerial abilities for prospector and defender. The study investigates 24,362 firm-year observations in six Southeast Asian countries in 2013–2019. The results show that Southeast Asian firms exhibit cost stickiness. Both prospectors and defenders exhibit cost stickiness, likely because Southeast Asian firms mostly operate in raw goods trading and do not innovate much in high technology. Consequently, prospectors do not exhibit different cost behavior than defenders. Our findings document that firms led by less able managers exhibit greater cost stickiness, both for prospectors and defenders. The results suggest that managerial characteristics remain crucial in resource management, because less able managers tend to retain resources to cope with operational problems and meet future sales demands.

A cash‐strapped creator's reward‐based crowdfunding strategies with spot sales

AbstractReward‐based crowdfunding with the all‐or‐nothing mechanism helps cash‐strapped creators raise funds from potential consumers to develop new products. However, this mechanism may hurt the creator in the long run because possible buying frenzies of strategic consumers will cannibalize the demand for spot sales if the project succeeds with overfunding. Through a two‐period model incorporating a crowdfunding period and a spot sales period, we find that strategic consumers' purchasing decisions depend on the probability that they will like the product in spot sales. Moreover, we show that crowdfunding cannot be used to finance when the setup cost that a creator needs to pay for the production is sufficiently high. In addition, for creators who can use crowdfunding to finance, contrary to the intuition that they should not take risks when the market uncertainty is high, we find the opposite results when we take the joint effect of the setup cost and market uncertainty into consideration. To be specific, when the market uncertainty is high and the setup cost is higher than a threshold, the creator can optimally choose the risky strategy. Furthermore, the creator may benefit from market uncertainty when a high setup cost is required to launch the product.

Outcomes Following Taxation of Sugar-Sweetened Beverages

More than 45 countries and several local jurisdictions have implemented sugar-sweetened beverage (SSB) taxes to improve nutrition and population health, and evidence on their outcomes to date is essential to inform policy discussions. Responding to this need, the World Health Organization commissioned a systematic literature review on the outcomes of fiscal policies, including SSB taxes. To assess the associations of implemented SSB taxes with prices, sales, consumption, diet, body weight, product changes, unintended consequences, health, and pregnancy outcomes. Searches of 8 bibliographic databases (Business Source Complete, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, CINAHL, EconLit, PsycINFO, PubMed, and Scopus) were performed from database inception through June 1, 2020, with no language or setting restrictions. Grey literature was assessed using 14 sources and government websites. The review included primary studies of implemented SSB taxes. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. For prices, sales and consumption, results were meta-analyzed using a 3-level random-effects model. Study quality was assessed at the outcome level. Tax pass-through rate for prices, percentage reduction in SSB demand, and price elasticity of demand for sales and consumption. Heterogeneity was assessed using τ2 and the I2 statistic. A total of 86 articles were eligible, with 62 studies contributing to the meta-analysis. The overall tax pass-through rate was 82% (95% CI, 66% to 98%; P < .001, I2 = 99%), suggesting tax undershifting. The demand for SSBs was highly sensitive to tax-induced price increases, with the price elasticity of demand of -1.59 (95% CI, -2.11 to -1.08; P < .001; I2 = 100%) and a mean reduction in SSB sales of 15% (95% CI, -20% to -9%; P < .001; I2 = 100%). There was no evidence of substitution to untaxed beverages, and changes in SSB consumption were not significant. The narrative synthesis found reformulation and reduced sugar content of taxed beverages for tiered taxes, cross-border shopping in most studies of local-level taxes, and no negative changes in employment. Data on the heterogeneity of SSB tax outcomes across subpopulations were limited. In this systematic review and meta-analysis of implemented SSB taxes worldwide, SSB taxes were associated with higher prices and lower sales of taxed beverages.

Multivariate Markov Chain Model for Sales Demand Estimation in a Company

Estimation of the number of demands for a product must be done correctly, so that the company can get maximum profit. Therefore, this study discusses how to estimate the amount of sales demand in a company correctly. The model that will be used to estimate sales demand is the Multivariate Markov Chain Model. This model can estimate the future state by observing the present state. The model requires parameter estimation values ​​first, namely the transition probability matrix and the weighted Markov chain, where in previous studies an estimation of the transition probability matrix has been carried out, so that in this study we will continue to estimate the weighted Markov chain parameters. This model is compatible with 5 data sequences (product types) defined as product 1, product 2, product 3, product 4, and product 5, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the state probability for product 1, product 2 and product 3 in company 1 are stationary at state 6 (very fast moving), product 4 and product 5 are stationary at state 2 (very slow moving).

A Poisson-Regression, Support Vector Machine and Grey Prediction Based Combined Forecasting Model Proposal: A Case Study in Distribution Business

Demand forecasting is a complicated task due to incomplete data and unpredictability. Accurate demand forecasting has a direct impact on the performance of a company. The goal of the study is to present a new two-stage combination model named Hybrid-2-Best, for accurate demand forecasting. The model combines three forecasting models in a single combined forecast. The Hybrid-2-Best model uses a two-stage algorithm to achieve better-performing forecasts. Case study showed that the proposed Hybrid-2-Best model performs the best forecast performance among other combination techniques and individual methods. Furthermore, GP integration in the first and second stages gives flexibility. Experimental results indicate that the proposed Hybrid-2-Best model is a promising alternative for sales demand forecasting. MAPE of the proposed model is 0,13. This is a good result and better than compared other models. Proposed model performed better than other compared models in MASE and MSE as well