Sales Season Research Articles

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method

The research presents a novel approach to inventory modelling, emphasizing stochastic demand and dynamic pricing strategies for a seasonal sales framework. The methodology divides the sales season into intervals, each associated with distinct pricing strategies influenced by stochastic factors. The study employ exponential smoothing for demand forecasting, optimizing inventory replenishment and dynamic pricing strategies through developed algorithms. Notably, the study determine the optimal smoothing parameter for demand forecasting, balancing responsiveness to recent demand patterns with long-term stability. Proposed research achieves a comprehensive framework empowering businesses to enhance competitiveness and profitability by addressing challenges of stochastic demand and dynamic pricing. Dynamic pricing strategies outperformed classical strategies, allowing businesses to respond promptly to demand fluctuations and optimize profit margins during sales seasons. Incorporating stochastic demand models enabled organizations to implement safety stock and buffer inventory levels effectively, mitigating risks associated with uncertain demand patterns. Real-time data analysis was crucial for adjusting price dynamics and making effective management decisions, leading to improved financial performance. The iterative nature of dynamic pricing strategies emphasized the importance of continuous refinement to adapt to evolving market dynamics. This approach enables data-driven decisions, adaptation to market fluctuations, and improved inventory management despite unpredictable demand. Ultimately, this study provides valuable insights and methodologies applicable across industries for efficient and profitable inventory management.

Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective

Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regression, and gradient boosting. From the performance evaluation, gradient boosting displayed relatively superior performance compared to the other two regarding R2 score and accuracy. These results highlighted the capability of sales prediction through machine learning, offering vital insights for decision-making processes. The findings of this empirical research provide an extensive guideline for executing machine learning techniques in sales forecasting and addressing seasonality and trend detection, especially when working with large datasets. Furthermore, the study shed light on possible challenges and issues encountered in the process. By resolving these issues, retailers can reinforce the reliability and accuracy of their sales predictions, thereby enhancing their decision-making capabilities in the context of sales management.

SALES PREDICTION BASED ON SARIMAX TIME SERIES ALGORITHM

Businesses must perform time-series forecasting of seasonal item sales because it enables them to predict future demand and modify their inventory accordingly. This investigation compares the performance of three well-known machine learning methods for time-series forecasting of seasonal item sales: Support Vector Machine (SVM), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), and Multi-layer Perception (MLP). A dataset of historical sales data is used to evaluate the algorithms. The data is split into training and testing sets, and measures like Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Root Mean Squared Error (RMSE) are used to assess each algorithm's performance. The analysis's findings support the assertion that SARIMA- X provides greater accuracy than other methods incalculating the seasonal sales of the historical data. Keywords—, Time-series forecasting, SARIMAX, ARIMA, Machine Learning

The Role Of The Gresik Regency Regional Government On Of The Stakeholders In Songkok Small And Medium Indsustries (Ikm) In Gresik Regency

The number of small and medium-sized industries recorded today is 261 people, showing the dominance of the Songkok industry in Gresik Regency. Management with a family and conventional system results in the Songkok Small and Medium Medium Industry Business Actors in Gresik Regency not being able to market Songkok optimally and have not been able to compete in the national market, the involvement of local governments is needed in supporting the development of small and medium industries in Gresik Regency. The existence of a small -medium medium industry in Gresik Regency in addition to being one of the crafts that produces products that become part of the culture of the Indonesian people, is also one of the industries that contribute to economic growth in Kabuoaten Gresik. The research method used is a qualitative method with case studies, a small and medium -sided industry has existed in Gresik Regency since the 1970s, initially only in Gresik District, has grown, developed, and spread in several regions in Gresik Regency, so that it became a part From the icon of Gresik Regency as the largest Songkok Craftsman Center in Indonesia. The reality on the ground with existing experience does not necessarily make the business fears able to compete in the national market. Digging information from 9 informants, aims to identify the Songkok Small and Medium Industry Business Process (IKM) in Gresik Regency, identify the right marketing mix in Songkok Small and Medium Industries (IKM) in Gresik Regency, and see the extent of the role of the local government as one of the stakeholders In supporting the growth of Songkok Small and Medium Industries (IKM) in Gresik Regency. The results of the Songkok Small and Medium Medium Industry Research in Gresik Regency are a family -patterned business with a production and seasonal sales and running business processes conventionally and traditional. Publick Governance as one of the new components in the marketing mix (marketing mix) which also has an important role in the development of the small and medium industry of Songkok in Gresik Regency. Collaboration and Synergy of the Gresik Regency Regional Government as one of the stakeholders with business actors needs to be improved in order to encourage the growth of small and medium industry and songkok industries and efforts to preserve the culture and identity of the Gresik Regency.

Forming and developing the regional market of cereals under the influence of substitutes

The paper examines the cereal market of the Republic of Crimea, which is characterized by the lack of testing of scientific approaches to assessing the regional market in terms of influence on substitutes’ demand and by pronounced seasonality of sales. The methodology is based on economic and statistical methods using correlation and regression analysis tools. The article determines the state of the Crimean market for goods based on official statistics, explores the dynamics of changes in production volumes of grain crops and their processing into cereals, and assesses the prices for goods of the group under study, presented in the retail trade network as well as the indicators characterizing the regional market of cereal products. The authors identify major Crimean manufacturers whose products are represented in the distribution network, and consider the factors influencing the development of the cereal regional market. Based on the results of the study, the study identifies the groups of substitutes that form the structure of consumer demand, including pasta and fresh vegetables. Drawing on correlation analysis, the authors establish the dependence of sales volumes of cereals on the prices of substitutes. Using regression analysis, the authors present the forecast for further development of the regional cereals market. The novelty of the study lies in determining the degree of dependence of consumer demand when purchasing cereal products on the prices of seasonal substitute goods. Finally, the study shows a direct effect of the demand for cereals on the prices of fresh vegetables, with a positive sales growth trend in the region, where sustainable economic and political situation will affect the prices for socially important food products.

Identifying Popular Products at an Early Stage of Sales Season for Apparel Industry

The early phase of launching a new apparel product is critical for gaining insights of its performance and classifying it into different categories such as fast selling, average selling, and slow selling. We propose a new ranking-based method to identify the product popularity that predicts regional and national rankings of products based on sales data at an early stage of a sales season. Our method enables companies to efficiently identify popular products within a remarkably short span of two to four weeks.

FUNCTIONING OF SELECTED BEEKEEPING FARMS IN POLAND DURING COVID-19 PANDEMIC

Aim: The purpose of this study was to examine the impact of the COVID-19 pandemic on selected beekeeping farms, as well as to compare the experience of chosen Polish beekeepers with the impact of the pandemic on beekeeping in other countries, as shown in the literature. Methods: The study was conducted using a literature review and questionnaire interview (n = 36) among beekeepers in the Mazowieckie and Warmińsko-Mazurskie provinces according to a 5-level Likert scale. Responses on industry topics were correlated with opinions on the positive and negative impact of the pandemic on beekeeping using the Pearson correlation. Results: Approximately 60% of respondents said that the pandemic had little or even no impact on their beekeeping activities. This may have been related to the peculiarities of Polish beekeeping, which is not dependent on seasonal labor. There were also moderate correlations between negative opinions on the impact of the pandemic and a lack of contact with other beekeepers and the seasonality of production and related sales as well as between opinions on the time-consuming nature of production and the positive impact of the pandemic on the beekeeping market. Conclusions: It was stated that beekeepers proved to be more resilient to supply chain breakdowns compared to beekeeper experiences in countries such as Canada and the UK. The nature of beekeepers’ marketing channels may affect the speed with which they can sell their goods. The impact of the pandemic on Polish beekeepers has not been previously studied.

A more grounded view of ‘farmer entrepreneurship’: how Zimbabwean smallholder farmers fundamentally differ in their entrepreneurial behaviours

Purpose This paper aims to investigate the entrepreneurial behaviours exhibited by commercial smallholder farmers in Zimbabwe, focusing on their socio-economic characteristics, and considers their implication for outcomes of livelihood resilience in a resource-constrained and turbulent rural context. Design/methodology/approach The study used survey data collected from 430 smallholder farmers in Masvingo province, Zimbabwe. Using a two-step cluster analysis, the study constructed a typology of farmers based on their entrepreneurial behaviour and socio-economic characteristics. Findings The results revealed that commercial smallholder farmers are heterogeneous in terms of their entrepreneurial behaviours. Four clusters were identified: non-entrepreneurial, goal-driven, means-driven and ambidextrous. Beyond their entrepreneurial behaviours, these clusters significantly differ in the socio-economic characterises (gender, age, education levels, farm size, proximity to the market and social connection) and farm performance (seasonal sales per hectare and farm income per hectare). Research limitations/implications The typology framework relating farmers’ entrepreneurial behaviours to their socio-economic characteristics and business performance is important to tailor and therefore improve the effectiveness of farmer entrepreneurship programmes and policies. In particular, tailoring farmer entrepreneurship education is crucial to distribute land, finance and market resources in purposive ways to promote a combination of smallholder farmers’ effectual and causal behaviours at an early stage of their farm ventures. Originality/value Researchers still know little about which farmers’ behaviours are entrepreneurial and how these behaviours manifest in action during their commercial farm activities. This research leverages effectuation and causation theory to unveil previously overlooked distinctions on farmers’ entrepreneurial behaviours, thereby enhancing a more grounded understanding of farmer entrepreneurship in a resource-constrained context.

Pengaruh Promosi Penjualan H&M terhadap Keputusan Pembelian Konsumen

Sales promotion is important for every business in any field and industry. Because sales promotion has the benefit of increasing sales and also to introduce products from a brand. The sales promotion carried out by H&M is to create an Up to 50% End of Season Sale. This study aims to measure how much influence H&M Sales Promotion has on Consumer Purchasing Decisions. The main theory used in this study is which includes three dimensions, namely Consumer Promotion, Trade Promotion and Salesperson Promotion. The data collection technique used is Random Sampling. This research is indicated by the average value of respondents' answers to variable X of 4.42 where the value is classified as high (agree). and the average on variable Y of 4.32 which has a value classified as high (agree). the influence of H & M Sales Promotion on Consumer Purchasing Decisions can be seen from the (R) correlation of 0.785 which is fairly high and the R square value of 0.616 or 61.1% of the effect while the rest is influenced by other things.

Coordinated Supply Contracts for a Two-Echelon Supply Chain under Given Bargaining Powers

This paper addresses the supply contracts within a decentralized supply chain comprising an upstream component supplier and a downstream manufacturer. With the need to invest in production capacity before the sales season, suppliers often make conservative capacity decisions due to uncertain demand, which gives rise to the double marginalization problem. The distribution of supply chain profits is typically determined based on each member’s relative bargaining power, often exogenously provided in the market. While numerous studies have been conducted to design supply contracts for coordination in decentralized supply chains, most of them overlook the supplier’s capacity constraints and the existing bargaining power structure. To bridge this gap, this study proposes a supply contract scheme that achieves coordination under any given bargaining power structure. The key finding of this paper is that two contract types, namely capacity cost-sharing (CCS) and surplus capacity compensation (SCC), can address the entire spectrum of bargaining powers. This study demonstrates how to identify a specific threshold for a given bargaining power structure, from which the appropriate contract type is selected. Through numerical illustrations, we present how to select coordinated contracts for different bargaining power structures and examine the influence of contract parameters on the profit of each supply chain member. The primary contribution is that this study provides actionable insights for practitioners to effectively implement coordinated contracts by presenting a straightforward and practical methodology.

Inventory control strategy: based on demand forecast error

PurposeThis study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.Design/methodology/approachThis study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.FindingsThe study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.Practical implicationsThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.Originality/valueThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Inventory Commitment and Monetary Compensation Under Competition

Problem definition: Inventory commitment and monetary compensation are widely recognized as effective strategies in monopoly settings when customers are concerned about stockouts. To attract more customer traffic, a firm reveals its inventory availability information to customers before the sales season or offers monetary compensation to placate customers if the product is out of stock. This paper investigates these two strategies when retailers compete on both price and inventory availability. Methodology/results: We develop a game-theoretic framework to analyze the strategic interactions among the retailers and customers and draw the following insights. First, both inventory commitment and monetary compensation may lead to a prisoner’s dilemma. Although these strategies are preferred regardless of the competitor’s price and inventory decisions, the equilibrium profit of each retailer could be lower in the presence of inventory commitment or monetary compensation because they intensify the competition between the retailers. Second, we find that market competition may hurt social welfare compared with a centralized setting by reducing the product availability in equilibrium. The inventory commitment and monetary compensation strategies further intensify the competition between the retailers, therefore causing an even lower social welfare. Managerial implications: Our study shows that, although inventory commitment and monetary compensation improve retailers’ profit and social welfare under monopoly, these strategies should be used with caution under competition. Funding: F. Zhang is grateful for the financial support from the National Natural Science Foundation of China [Grants 71929201, 72131004]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grant 14502722] and the National Natural Science Foundation of China [Grant 72293560/72293565]. Y. Yu is grateful for the financial support from the National Natural Science Foundation of China [Grant 71921001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0411 .

Marketability level of potato in Armenia: potato functional properties

Background: Functional food is becoming more and more important for professionals and also for consumers. On the other hand, it is known that the main factors affecting the sale of agricultural products are the general market dynamics, legislation, product range, seasonal sales dynamics, competitors, pricing, buyers and distribution channels. What’s also important is the profile of the company's personnel and the work of personnel in the market, advertising related to the quality of the product, which in recent times is especially based on the "natural" origin and functional value of the product․ Objective: The aim of the current study is to quantify the factors influencing the level of potato marketability through econometric analysis in Armenia. One of the objectives was also to observe whether the population appreciates the functional value of potatoes, which may affect future marketability and yield of potatoes. Methods:Quantitative assessment of the factors influencing the level of potato marketability was carried out with a number of indicators: the yield of the sowing area, the gross harvest and the volume of export and import. Observations were carried out during 10 years (2009 - 2018). A seven-item questionnaire was developed to explore consumer preferences for purchasing potatoes. Results:The level of marketability of potatoes had an increasing trend with the certain fluctuations (years: 2010, 2014, 2016 and 2018). The data on the correlation coefficients on the factors influencing the level of potato marketability indicated that the relationship between the marketability level and potato planting area is inverse to the average level (ryx1 = - 0.615). The correlation between the level of commerciality and yield is direct in the lower middle range (ryx2 = 0.461). The latter shows that increasing the level of yield contributes to the level of marketability. The effects of gross harvest, export and import volumes have a negative impact on the level of commoditization, but the relationship is extremely weak with ryx3 = - 0.214 and ryx4 = - 0.0532 and ryx5 = - 0.105. Interview studies show that factors affecting potato marketability and productivity are subject to change. Conclusion: The increase in metabolic/non-communicable diseases, increased consumer awareness of these diseases and their prevention, as well as developments in organic agriculture and functional food production require changes in economic research and calculations, also related to functional foods. Keywords: potato, market, marketability, organic agriculture, functional food

IMPLEMENTATITON OF RANDOM FOREST ALGORTIHM ON SALES DATA TO PREDICT CHURN POTENTIAL IN SUZUYA SUPERMARKET PRODUCTS

Concentration of sales that are focused on products that are in great demand and are popular is one of the supermarket sales techniques. Seasonal sales techniques like this sometimes have an impact that can be seen obviously by the imbalance in sales of existing products in supermarkets. Sales imbalance can be the initial cause for a product to lose interest and become a product that is eventually removed from store. With a classification model made to predict which products will be eliminated or churn, it can assist staff in distributing the sales of each product. The more products are churn due to lack of enthusiasts which can affect the overall sales of the supermarket. The purpose of this study is to assist staff in classifying potentially churn products. The classification model consists of 3 models with different algorithms and the results show that the application of the Random Forest algorithm is more effective for predicting data with 96% accuracy compared to 81% for the Logistic Regression algorithm and 46% for the Support Vector Machine algorithm.

#Sponseredathlete: the marketing of image and performance enhancing drugs on Facebook and Instagram.

This article sets out to investigate the marketing of image and performance enhancing drugs (IPEDs) on the social media platforms Facebook and Instagram. Drawing upon a 'connective' ethnographic exploration of IPED use and supply, the paper first outlines a supplier typology on these platforms, before shedding light on the marketing strategies employed by sellers in order to overcome the inherent distrust of online sales and build a trustworthy brand. Techniques identified include athlete sponsorship, the sharing of bodybuilding fitspiration content, self-objectification, posting images showcasing transformation photos and customer feedback, and seasonal sales and promotions. Analysis encompasses the centrality of product branding, the overlaps between licit and illicit market advertising strategies, and the affordances of the platforms under study. Finally, conclusions relating to the implications of these findings to scholarship, policy, and regulation are offered.

Optimal combination of platform channel contract and guarantee financing strategy in e-commerce market

With the development of economy and technology, e-commerce platforms can connect consumers and online channel suppliers providing transportation services through third-party logistics (3PL). To help these suppliers, especially small- and medium-sized enterprises (SMEs) with financing dilemmas, this paper proposes evidence on the members of the e-commerce supply chain providing procurement and financial assistance. Many partner enterprises offer various innovative financing schemes, such as guarantee financing. To examine the advantage in practice, we study the guarantee financing in the online channel of e-commerce market, which includes capital constrained SME suppliers, e-commerce platforms, 3PLs and banks. We analyze the equilibrium of the optimal combination strategy with channel sales mode and guarantee financing scheme, and explore the conditions under which this innovative financing scheme will benefit all members. It is more feasible for the SME supplier to seek the e-commerce platform (or the 3PL) guarantee financing with high (low) platform service construction cost compared with bank loans without guarantee. This preference comes from the trade-off between the potential market demand risk in sales season and the financing risk of the debtor. We further determine how the release on the sales mode of platform is affected by the guarantee financing scheme. No matter who guarantees, the platform tends to provide the reselling (marketplace) mode when the market demand risk is flat (high). With the increasing of investment in platform’s service construction, the platform is more (less) willing to switch the role from the non-guarantor to the guarantor in the reselling (marketplace) mode. The reason is that the e-commerce platform and the 3PL prefer to act as non-guarantor to obtain free-riding benefits, especially in the marketplace mode. Results of this paper provide theoretical support for the innovative financial practice of the e-commerce industry. The insights are relevant to the e-commerce platform and the 3PL managers who aim to promote the applicability of financing scheme to debtors under two channel modes and increase the revenue through construction investment and business expansion.

The Value of Social Media Data in Fashion Forecasting

Problem definition: How to use social media to predict style color and jeans fit sales for a retailer. Academic/practical relevance: Neither retail practice nor the academic literature provides a method for using social media to predict style color and jeans fit sales for a retailer. We present and validate a systematic approach for doing that. Methodology: Demand forecasting in the fashion industry is challenging due to short product lifetimes, long manufacturing lead times, and constant innovation of fashion products. We investigate the value of social media information for color trends and jeans fit forecasting. We partner with three multinational retailers, two apparel and one footwear, and combine their proprietary data sets with web-crawled publicly available data on Twitter and the Google Search Volume Index. We implement a variety of machine learning models to develop forecasts that can be used in setting the initial shipment quantity for an item, arguably the most important decision for fashion retailers. Results: Our findings show that fine-grained social media information has significant predictive power in forecasting color and fit demands months in advance of the sales season, and therefore greatly helps in making the initial shipment quantity decision. The predictive power of including social media features, measured by the improvement of the out-of-sample mean absolute deviation over current practice ranges from 24% to 57%. Managerial implications: To our knowledge, this study is the first to explore and demonstrate the value of social media information in fashion demand forecasting in a way that is practical and operable for fashion retailers. With consistent results across all three retailers, we demonstrate the robustness of our findings over market and geographic heterogeneity, and different forecast horizons. Moreover, we discuss potential mechanisms that might be driving this significant predictive power. Our results suggest that changes in fashion demand are driven more by “bottom-up” changes in consumer preferences than by “top-down” influence from the fashion industry. Funding: This work was supported by Wharton School Fishman-Davidson Center for Service and Operations Management, the Wharton School Baker Retailing Center, and the Wharton School Risk Management Center Russell Ackoff Doctoral Student Fellowship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1193 .

Working Capital Needs Assessment due to Business Seasonality in the Logistics Company

The growth of the company's business is generally followed by an increase in the company's revenue. However, to achieve revenue, the company require capital to finance the cost of goods sold, salary expenses, and other expenses before the sales happened. The cost of capital is generally the cost that the company first incurs compared to revenue or cash collection. The impact of costs must be paid in advance or early, while on the other hand cash collection from a customer is received later. If this situation continues over a long period and a bigger amount over time, this condition will make the company hampered to maximize the revenue due to the lack of cash. In this analysis using an international forwarding company in the export and import logistics industry, this company is still small and medium and has limited capital. In the last 5 years, the company has continued to experience an increase in sales from year to year, but due to limited capital, the company cannot fulfil all requests from customers due to maximizing the revenue because of the high cost of goods sold that should be paid first or early. In addition, in this industry, the profits that are generated by companies are tight, so companies tend to avoid bank loans in funding their operational activities. In the operational activities of this company, there is also a difference in the timing of receiving money from customers, which tends to be longer than the difference in payment times. The impact is that even though the company appears to have high liquidity, the company still cannot accept all customer requests because the company's assets are receivables. This is also exacerbated because the logistics business has seasonal sales that depend on global economic activity. In a high sales season, the company tends to have no cash, while in a low sales season, the company has a lot of cash but is not productive. Furthermore, if the company decided to invest the money for expansion, currently the company did not have any calculation of cash required to take all potential revenue in the future.

Optimal decisions on price and inventory for a newsboy-type retailer with identifiable information and discount promotion.

Consider a retailer selling a seasonal item, new items are stocked at the beginning of sale season and no inventory replenishment is permitted. Assuming the initial price is exogenous and the information about demand becomes more accurate as the sales season progresses, the retailer is allowed to make an in-season price adjustment after conducting a review. After the review time, if the price is adjusted to be lower than the initial price, demand increases more quickly with price decreasing which reflects the promotional effect of discount. Given the initial inventory, an optimal price adjusting model is proposed to maximize the retailer's revenue. Taking decisions on inventory into account, the proposed model is extended to maximize the retailer's profit rather than revenue. Numerical examples are also illustrated to test the proposed model. The results show that the optimal in-season price mainly depends on the proportion of the remaining demand, the price sensitivity, and the effect of sales promotion. An important managerial implication is that the retailer should gather the demand information about the price and raise the in-season price as soon as possible to gain more revenue when the price elasticity is small enough. Otherwise, when the price elasticity is larger, the retailer should maintain or decrease the price to gain more revenue.