Sales Demand Research Articles

Optimal dynamic allocation of rental and sales inventory for fashion apparel products

There is a growing trend towards renting rather than permanent ownership of various product categories such as designer clothes and accessories. In this article, we study an emerging retail business model that simultaneously serves rental and sales markets. Specifically, we consider a retailer that primarily focuses on renting while also selectively meeting incidental sales demand. Once a unit is sold, the firm forgoes potentially recurring rental revenues from that unit during the remaining periods. Therefore, it is critical for a retailer to dynamically decide how much of its inventory to allocate for sales and rentals at each period. We first develop a consumer choice model that determines the fraction of the market that chooses renting over purchasing. We characterize the optimal inventory allocation policy and explore how market characteristics and prices impact inventory allocation. We discuss the value of dynamic allocation and observe that the profit improvement can be substantial. In addition, we propose a simple and efficient heuristic policy. Finally, we extend our analysis to study the optimal allocation policies for (i) a retailer that is primarily a seller that selectively meets rental demand, and (ii) a retailer that does not enforce any prioritization between rental and sales demand.

Improving enterprise’s competitiveness on the basis of combined ABC/XYZ and cluster analyses of the commodity nomenclature

Subject. This article examines the relationship between marketing activities and the development of entrepreneurial structures in Russia. Objectives. The article aims to develop a methodological approach that helps optimize the product nomenclature and develop differentiated marketing strategies for managing sales in different segments. Methods. For the study, I used the methods of ABC/XYZ and cluster analyses. Results. The article offers a methodological approach to the analysis of the nomenclature of goods, tested on the data of one of the business structures of the Crimean wine industry. It identifies goods nomenclature clusters that have similar dynamic characteristics for changes in sales volumes and demand predictability, and provides recommendations for increasing sales, margins, and improving enterprise competitiveness. Conclusions. The developed methodological approach can be considered as an element of information and methodological support for the enterprise competitiveness management system.

676The impact of alcohol tax elimination policy on population health in Hong Kong

Abstract Background Extensive research has shown that taxes on alcohol reduces consumption and alcohol-related harm. Uniquely among high-income economies, the Hong Kong Government reduced alcohol taxes on wine and beer by half in 2007; and entirely waived all alcohol-related taxes in 2008. Methods We adapted the International Model of Alcohol Harms and Policies (InterMAHP) using Hong Kong data to evaluate the health impact of the tax elimination policy changes on alcohol pricing. Prevalence and consumption estimates were calculated from serial Behavioural Risk Factor surveys of health behaviours among the Hong Kong population. We applied the linear approximation of the Almost Ideal Demand System (AIDS) econometric model to estimate the elasticity of demand for on-trade and off-trade sales of alcohol (beer, wine, and spirits) using Euromonitor market sales data. We derived population attributable fractions and relative risks estimates from Hospital Authority morbidity and mortality records. Results Alcohol consumption and alcohol-related harms were higher for males. We found the policy change increased alcohol-attributable mortalities, predominately due to increases in cancer, cardiovascular disease, and injuries. The burden of disease was higher in older age groups. Conclusions Hong Kong pursued a policy of eliminating taxes on alcohol despite extensive evidence on the effectiveness of taxation as a policy tool. We find empirical evidence this policy change was associated with an increase in alcohol-related harms and detrimental to population health. Key messages The alcohol tax elimination policy in Hong Kong was associated with an increase in alcohol-related burden of disease. Reintroduction of alcohol taxes merits consideration.

PERENCANAAN DAN PENGENDALIAN PRODUKSI KANTONGAN PLASTIK MEREK NO.1 PADA PT. DUTA PLASTIK INDUSTRI

Consumer demand for plastic bags to PT. Duta Plastic Industries often run out of stock. PT. Duta Plastic Industries in order to maintain consumer trust always maintain product availability and product quality in good condition. This study focuses on the problem of product inventory ordered by consumers. This study applies the exponential method to predict the amount of plastic production needs to meet customer demand. The results obtained from the research, among others, the number of forecasting the average demand for plastic bags brand No.1 at PT. Duta Plastic Industry in the period January 2018 to December 2018 is size 15 is 43.44 tons/month, size 19 is 37.36 tons/month, and size 24 is 25.43 tons/month. The average amount of raw material inventory per month that must be provided to meet the demand for sales of No.1 brand plastic bags during the period January 2018 to December 2018 which consists of three sizes is 115 tons/month.

Robust recurrent network model for intermittent time-series forecasting

This paper describes a deep-learning-based time-series forecasting method that was ranked third in the accuracy challenge of the M5 competition. We solved the problem using a deep-learning approach based on DeepAR, which is an auto-regressive recurrent network model conditioned on historical inputs. To address the intermittent and irregular characteristics of sales demand, we modified the training procedure of DeepAR; instead of using actual values for the historical inputs, our model uses values sampled from a trained distribution and feeds them to the network as past values. We obtained the final result using an ensemble of multiple models to make a robust and stable prediction. To appropriately select a model for the ensemble, each model was evaluated using the average weighted root mean squared scaled error, calculated for all levels of a wide range of past periods.

RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting

The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare parts/ car sales distribution is an important factor in inventory control. The valuation of the demand is challenging as the automobile spare parts/car sales demand are often recurrent. The renowned empirical method adopts historical demand data to create the distribution of lead time demand. Although it works reasonably well when service requirements are relatively low, it has difficulty reaching high target service levels. In this paper, we proposed Recurrent Neural Networks/ Long-Short Term Memory (RNN / LSTM) with modified Adam optimizer to predict the demand for spare parts. In this LSTM, weight vectors are generated respectively. These weights are optimized using the Modified-Adam algorithm. The accuracy of the forecast and the performance of the inventory are considered in the experimental result. Experimental results confirm that RNN / LSTM with a Modified-Adam works well with minimal error compared to other existing methods. We conclude that the proposed RNN/LSTM with Modified-Adam algorithm is well suited for the prediction of automobile spare parts.

Organization of innovative activities in telecommunications enterprises

In the context of the global pandemic, the rapid penetration of information technology in all spheres of economic activity, increasing demand for online sales of services and goods, leads to the important role of telecommunications companies in the national economy. The main role of the telecommunications sector is reflected in the essence of the impact of the national economy on the process of transition to innovative development. This, in turn, requires the development of innovative activities of telecommunications enterprises that meet the requirements of the current stage of development, taking into account the impact of internal and external factors that threaten the innovative development of the country. In general, the innovation process consists of obtaining and commercializing inventions, new technologies, types of products and services, production, financial, administrative or other decisions and other results of intellectual activity.

Can a Manufacturer Get Better off When Providing Product Sharing Services?

The emerging product sharing trend poses challenges for manufacturers. Although the cost of purchasing products is decreased by adopting product sharing, consumers must accept that their demand cannot be responded to instantly and must use unfamiliar shared products. There are unavoidable inconveniences that are inherent in product sharing that directly affect consumers’ choices and further affect manufacturers’ decisions about providing sharing services. However, few papers have focused on the impact of such factors. In this paper, we aim to study how the unavoidable inconvenience costs and operational costs affect the manufacturer’s decision on whether to provide sharing services. First, low inconvenience cost and operational cost encourage the manufacturer to provide sharing services. High product quality weakens the influence of these two factors, while strengthening the influence of the pooling effect on the manufacturer’s provision decision. Second, the sharing market cannibalizes the sales demand when inconvenience cost is low and product quality is high. Third, the provision of sharing services is a win-win situation for consumers and the manufacturers of low-quality products.

How to React to Internal and External Sharing in B2C and C2C

The sharing economy has developed rapidly in recent years using both business‐to‐customer (B2C) and customer‐to‐customer (C2C) models. This has exerted a profound impact on incumbent firms that follow a traditional sales model. Although the effects of B2C or C2C sharing in certain scenarios have been studied by prior literature, the effect of external B2C sharing has not been considered. Furthermore, the possible distinction between the two sharing effects as well as incumbent firms’ decisions on the sales and sharing models under the internal and external environments have not been addressed. This study compares the effects of B2C and C2C sharing in an internal sharing scenario where an incumbent firm can extend into the sharing business. Due to the difference in sharing agents, we also consider an external sharing scenario where an independent entrant firm can provide B2C or C2C sharing and strategically set price. From the perspectives of product cost and sharing transaction cost, we present several new managerial insights to expand on existing literature. First, under the internal sharing scenario, interestingly, the incumbent firm benefits from extending into the B2C or C2C sharing business only when the product cost is above a threshold, and it prefers to extend into the C2C sharing business unless the per‐period transaction cost of C2C sharing is much higher than that of B2C sharing. In addition, it is shown that the incumbent firm may need to produce more products for sales, or maintain lower sharing supply when it extends into the B2C sharing business than those when it extends C2C sharing, which is somewhat counter‐intuitive. Under the external sharing scenario, we observe that the B2C sharing business benefits the incumbent firm merely when the product cost is high, similar to the impact of C2C sharing. Meanwhile, if the per‐period transaction cost of C2C sharing is much higher than that of B2C sharing, then the positive impact of external B2C sharing on the incumbent firm’s profitability should be stronger than that of external C2C sharing. Moreover, external B2C sharing actually increases the sales demand of the incumbent firm under the conditions of low product cost and high sharing transaction cost, while external C2C sharing might increase it as well in the condition of high product cost. Both external B2C and C2C sharing may lead to a higher rental price than internal sharing in the presence of high product cost. Furthermore, even though the product cost is low, there should be higher customer surplus and total social welfare in the external B2C or C2C sharing scenarios. For constructing a comprehensive framework of sharing scenarios, we also extend our model to a setting where the incumbent firm could extend both B2C and C2C sharing in the internal and external environments. It is shown that the incumbent firm always extends into the sharing business to compete against the entrant firm in the external sharing scenario.

Sales Demand Forecasting Using One of Multivariate Markov Chain Model Parameter

The imbalance between demand and supply is frequently occurred in a market. This is due to the availability of goods that cannot match with the demand or the growth rate of customer. This is not preferable since the profit is not on the track. In contrast, the goods are probably over supplied so that company has to expense additional cost for extra storage. Both situations can be anticipated if the demand is precisely estimated. Therefore, in this study we will estimate demand in market situation by implementing multivariate Markov chain model. Multivariate Markov chain model is popular model for forecasting by observing current state in various applications. This model is compatible with 5 data sequences (product types) defined as product A, product B, product C, product D and product E, with 6 conditions (no sales volume, very slow-moving, slow-moving, standard, fast moving, and very fast moving). As the result, the highest transition probability value for the sales demand in a company is found at the transition probability matrix from product C to product C, from very fast moving to very fast-moving condition, which had the highest probability value 0.625 with the highest frequency 105 times.

Newsvendor Decisions with Two-Sided Learning

A substantial collection of work in operations management considers settings where a firm faces uncertain demand that depends on parameters that are ex ante unknown but can be learned by observing historical data. This work typically assumes that future demand is unaffected by the firm’s learning process. However, in the new era of social media, it is increasingly the case that the information used by the firm to gauge future demand (for example, sales and stockouts or consumer feedback) is now also observable to the consumers and may influence their purchase decisions. In this paper, we consider a newsvendor model where a product of ex ante unknown value is sold in an environment where learning is “two-sided” in that both the firm and the consumers learn the product’s value over time by observing the same information. The analysis establishes a consequential insight: When learning is two-sided, the value of information is often negative for the firm; as a result, the firm’ optimal stocking quantity is often lower than that under one-sided learning. Moreover, under certain conditions that we identify, we show that the optimal stocking quantity can be even lower than the critical fractile policy, in stark contrast to the recurring prescription found in existing literature of “stocking more” in the presence of learning. Additional results and numerical experiments suggest that the loss for the firm from failing to account for the two-sidedness of the learning process can be significant. This paper was accepted by Charles Corbett, operations management.

Forecasting Demand Produk Batik Di Tengah Pandemi Covid-19 Studi Pada Usaha Batik Fendy, Klaten

The objectives of this study are forecast the demand for batik cloth and batik clothing in May 2020 and analyze the accuracy of forecasting demand for batik cloth and batik clothing.
 This research is a descriptive study with a quantitative approach. Using data by interviews with Batik Fendy Business Managers and actual sales data from November 2019 to April 2020. There are two stages of data analysis that is calculating demand forecasting of batik cloth and batik clothing for May 2020 with the Linear Exponential Smoothing method uses a combination of α 0,8 / β 0,1 and α 0,9 / β 0,2 constant. While the second stage is to analyze the accuracy of the method of demand forecasting using the MAPE Technique.
 The results of this study are for batik cloth products, predicted sales demand for May 2020 is 316 pieces with 30% MAPE. As for the batik clothing forecasting demand for May 2020 is 432 pieces with a MAPE of 19,8%.

Blockchain for Secure-GaS: Blockchain-Powered Secure Natural Gas IoT System With AI-Enabled Gas Prediction and Transaction in Smart City

The traditional natural gas Internet-of-Things (IoT) system has many problems, such as centralized management of resources, noncirculation of data between stations, insecurity of transaction information or account books, and lack of contract consensus. In order to ensure data security and reliable transaction, this article introduces artificial intelligence (AI) and blockchain technology and constructs an AI-enabled and blockchain-powered natural gas IoT system in a smart city. In this article, the natural gas output prediction model based on temporal pattern attention-based LSTMs (TPA-LSTMs) is used to enable the system to sense the change of natural gas deliverability. In addition, we establish a blockchain-based secure natural gas transaction scheme, which dynamically matches the purchase contract and sale contract to maximize the interests of the buyer and the seller and obtain a transaction contract. The experimental results show that our model can predict the output value of natural gas in real time and select the appropriate transaction matching scheme according to the dynamic demand for sales.

Optimal production and inventory control of multi-class mixed backorder and lost sales demand class models

Manufacturers usually face customers with different service requirements. The challenge they face is how to determine which customers to serve when the inventory supply is limited. We consider the problem of a manufacturer serving two types of customers: those with long term commitment, divided into several backorder classes; and those without commitment consisting of a single lost sales class. We treat the problem within a continuous time integrated production and inventory control framework. We propose a two-step strategy to fully characterize the optimal policy. In a first step, we use the concept of L-natural convexity to partially describe the optimal policy. Based on these results, in a second step, we reformulate the problem and fully characterize the optimal policy. We show that the latter is characterized by state-dependent multidimensional thresholds. Due to the computational complexity of the problem, we propose three heuristic policies: The first uses linear thresholds that mimic the optimal policy. These thresholds are computed through a decomposition of the original problem into a series of single-lost sales, single-backorder class problems. The second heuristic treats all demand classes equally. The third heuristic uses static thresholds to control production and inventory rationing among demand classes. Extensive numerical results show that, for problems with one lost sales and two or three backorder classes, the first heuristic outperforms the other two with a cost deviation less than 1.25%, from the optimal. Furthermore, computing the thresholds of this heuristic is orders of magnitude faster than computing the optimal policy.

Research on Marketing Model of Green Agricultural Product under the Background of Internet Plus

With the continuous development of network communication technology in recent years, China's socialist market has been continuously improved, and the level and standards of modern science and technology have also achieved a remarkable improvement. Coupled with the gradual integration of economies worldwide, China's Internet technology is also rapid developing. In the meantime, this ushered the development and formation of online marketing market in China. In particular, the business of by-products from the agriculture industry can also be promoted through the new marketing medium of the network, representing a major change in the market economy. The agricultural product marketing model using Internet Plus helps farmers to expand the sales of the agricultural products, and also provides a new opportunity for the further development of the network marketing. The author discusses the Internet Plus-based agricultural product marketing model, encompassing an analysis of the advantages of marketing green agricultural products in the Internet Plus era and the status quo of China's agricultural product marketing activities. Considering the sales demand of green agricultural product in the current market and the development of e-commerce under the Internet Plus model, it will further promote the development of network marketing activities of green agricultural product in China.

The effects of overproduction on future firm performance and inventory write‐downs

AbstractThis study aims to examine the effects of current overproduction on future operating efficiency and inventory write‐downs. Using electronic manufacturing companies listed on NYSE/AMEX/NASDAQ during the period of 2003–2013, this study: (i) employs the interaction term between relative fixed cost levels and excess quantity of inventory produced to measure overproduction, (ii) utilizes the dynamic slacks based measure model to estimate operating efficiency, and (iii) examines inventory write‐downs. Our findings show that overproducing firms experience improved but unsustainable subsequent operating efficiency, as overproduction is a result of optimistic sales forecasting, which leads to higher subsequent inventory write‐downs. Overall, this paper suggests that production managers should be prudent in anticipating future sales demand, as short product life cycle might cause higher inventory obsolescence on excess production.

Analysis of multiple drivers of air pollution emissions in China via interregional trade

Abstract Severe haze problems in China have attracted substantial attention. End-of-pipe measures have been implemented to mitigate air pollutant emissions in different industrial sectors. However, interregional trade can lead to geological separation of the stages of supply chains (i.e., primary input, production, final sale and consumption), and thus, emissions can be driven by cross-regional drivers (i.e., primary inputs, final sales and consumption), and pollutants can transfer among regions via supply chains. Inequity exists in production-side measures of emission reductions, which do not account for the effects of cross-regional drivers. In this study, we use a multiregional input-output model in the year 2012 to calculate the emissions driven by cross-regional drivers and to trace emission flows along supply chains in China, aiming to inform policymaking on fine particulate matter (PM2.5) mitigation and to provide additional information about shared responsibilities among provinces. We find that the Central (including Anhui and Henan) is the largest emitter outside the Central Coast in order to meet the demand for sales within the Central Coast. Specifically, the cross-provincial sale of products from the construction sector drives massive emissions, especially in Jiangsu province (located in the Central Coast). Therefore, the Central Coast should bear some responsibility to help provinces located in the Central reduce emissions. About half of emissions driven by primary inputs in Beijing-Tianjin are emitted in other regions. However, in general, emissions (except for ammonia) are more likely to be driven by final sales of other provinces than by primary inputs. The supply chain perspective can help gain a better understanding of the impacts of trade-related drivers on emission patterns and advocate just and effective policies considering cleaner production and shared responsibilities.

Demand stimulation in finished-goods inventory management: Empirical evidence from General Motors dealerships

Automobile dealerships are key players in the automobile industry. Their inventory policies are crucial to automobile supply chain management, yet they are largely unexplored empirically. Using data from General Motors dealerships, this paper proposes two simultaneous equation modeling (SEM) systems to examine the interactive and simultaneous effects of sales demand, order quantity and inventory level in dealerships. Our empirical results indicate that high demand leads to high inventory levels (sales effect), while high inventory levels stimulate sales demand (demand stimulation effect) in a dynamic and uncertain environment. We also find that inventory cannot stimulate demand infinitely. This study is among the first empirical investigations to experiment with a SEM system applied to finished-goods inventory management in the automobile supply chain. Together, our methodology and results provide several avenues for developing finished-goods inventory policies in business management.

대중교통 이용이 소매업 매출액에 미치는 구조적 매개효과 분석

Such characteristics as population, employment, and block are factors that can have effects on both demand for public transportation use and retail sales. Therefore, the purpose of this study is to demonstrate the structurely mediating effect of public transportation use between retail sales and the neighborhood environment. For the purpose, factor analysis method and the structural equation model are employed as the model for it. The major findings are as follows: The mediating effects in the commercial area were not significant. And, it was proved that there is an indirect effect of transit ridership caused by neighborhood environment factors on the retail sales in the entire area as well as the residential area. It was analyzed that retail sales are varied with about 9.2% median effect in the entire area and about 5.5% in residential area by transit riders. In conclusion, this study empirically demonstrates the important role of public transportation use on increasing retail sales in the Seoul metropolitan area, especially in residental area. In addition, we confirmed that the increase in public transportation use could lead to the revitalization of local residential areas. Therefore, the result of this study suggests that the linkage of public transit accessibility and commercial facilities is important for the economy of a region to be activated.

Inventory growth cycles with debt-financed investment

We propose a continuous-time stock-flow consistent model for inventory dynamics in an economy with firms, banks, and households. On the supply side, firms decide on production based on adaptive expectations for sales demand and a desired level of inventories. On the demand side, investment is determined as a function of utilization and profitability and can be financed by debt, whereas consumption is independently determined as a function of income and wealth. Prices adjust sluggishly to both changes in labour costs and inventory. Disequilibrium between expected sales and demand is absorbed by unplanned changes in inventory. This results in a five-dimensional dynamical system for wage share, employment rate, private debt ratio, expected sales, and capacity utilization. We analyze two limiting cases: the long-run dynamics provides a version of the Keen model with effective demand and varying inventories, whereas the short-run dynamics gives rise to behaviour that we interpret as Kitchin cycles.