Daily Sales Research Articles

The COVID-19 Shock Through the Lens of the Spanish Sales Daily Data: Modeling and Seasonal Adjustment Challenges

We explore the impact of the COVID-19 shock on a very high frequency indicator: the Spanish daily sales data compiled by the Tax Agency. Firstly, we present a detailed list of the issues related to its modeling, its decomposition (trend, seasonality, and irregularity) and its final seasonal adjustment, which requires a set of deterministic factors linked mostly to calendar effects. Then, we assess the impact of the COVID-19 shock on these tasks. This assessment provides a timely perspective on the evolution of the shock and the challenges that it posed for modeling and seasonal adjustment, clearly related to its unusual and extreme features. The aim of the paper is eminently empirical and dominated by the need to square the unprecedented shock with the available methods and software in a computing environment centered on the R programming language. The methodology draws heavily on a structural approach and it is currently in use by the Tax Agency to compile and disseminate its daily sales data.

A repositioning nudge to encourage healthier snack purchases at a supermarket in a deprived area

Abstract Background Diet quality positively correlates with socio-economic position (SEP). While nudges are often proposed as promising policy tools for promoting health behavior among lower SEP groups, research addressing their impact within lower SEP groups is limited. This study aimed to 1) examine the efficacy of a checkout repositioning nudge in a supermarket in a deprived area in encouraging purchases of healthier snacks, and 2) describe customers’ preferences and needs regarding grocery shopping and healthy eating. Methods Daily sales data were collected over four-week control and intervention periods. During the intervention, unhealthy snacks at the checkout were replaced by healthier snacks. Additionally, questionnaires were administered among customers (N = 90). Results The intervention led to a significant increase in daily sales of healthier snacks (M = 3.14, SD = 2.45) compared to the control period (M = 0.54, SD = 0.96), t(27) = 5.62, p < .001. Despite a statistically significant nudge-effect, the absolute daily sales of healthier snacks remains extremely low. No difference in daily sales of unhealthy snacks was observed between both periods. Questionnaire data showed that customers report to seldomly purchase snacks at the checkout, find it important to eat healthily, think well about what they buy in a supermarket, and long for a healthier and more affordable food offer within the supermarket. Conclusions Interventions that are effective among higher SEP groups, such as repositioning nudges, may not be suitable for the circumstances and needs of lower SEP groups. To combat existing health inequalities, interventions must be tailored to lower SEP groups. More system-level interventions that target the healthiness and affordability of the offer may be needed. Key messages • Although nudging is often proposed as a promising policy approach for improving health behavior among lower SEP groups, repositioning nudges may not suit their circumstances and needs. • To combat health inequalities, interventions must be tailored to lower SEP groups and integrated with system-level policies (e.g., targeting the healthiness and price of the offer in supermarkets).

Leveraging Machine Learning to Optimize Pricing and Restocking Strategies for Fresh Vegetables

In the competitive and diverse retail market, restocking and pricing perishable vegetables is challenging. Using big data and machine learning to analyze sales data, this research can build predictive models to refine these strategies and predict trends[1][2]. This research analyzed sales data from 2020 to 2023, identifying key trends in sales volumes, seasonality, and consumer preferences. It incorporated these findings into a multi-objective programming model that also considered wholesale and loss rates. This research used statistical tests like the Kruskal-Wallis and Mann-Whitney U to detect significant sales differences across vegetable categories, while the Spearman correlation showed strong seasonal and culinary impacts on consumer buying behaviors. To optimize freshness and maximize sales and profits, this research utilized genetic algorithms and regression analyses, including an effective XGBoost model. This model had a Mean Absolute Percentage Error of 16.97 and an R² of 0.94, aiding in predicting daily sales and improving pricing strategies and restocking schedules. These methods helped enhance decision-making, ensuring vegetable freshness and maximizing profitability, with projected profits reaching ¥41,582.87. This study not only highlights the effectiveness of integrating big data and machine learning in the retail sector but also sets a benchmark for operational efficiency in managing perishable goods.

Research on the Application of Statistical Anomaly Detection in Tobacco Sales Anomaly Detection

With the rapid development of a new generation of information technology, digitalization and informatization have become key factors in enhancing the core competitiveness of the tobacco industry. Detecting and monitoring abnormal tobacco sales behaviors play a vital role in combating illegal transactions and ensuring the healthy development of the tobacco industry. Manual identification of abnormal sales behaviors presents practical issues, such as low efficiency, strong subjectivity, and a reliance on accumulated experience. Utilizing statistical probability theory to help the tobacco bureau determine the threshold for defining abnormal sales behavior can effectively improve the efficiency of anomaly detection. This study applies relevant theories of statistical anomaly detection in combination with practical production needs, establishing four threshold criteria for identifying abnormal sales behaviors: the number of days without retail data, the ratio of daily sales volume to the sales volume of the previous seven days, the number of days of available sales, and the number of days with scanning activity per week. These criteria make it possible to automate and dynamically identify abnormal sales behaviors in subsequent sales situations.

Pricing and Replenishment Decision System Based on LSTM

Fresh supermarket vegetables have a very short shelf life, and the quality of vegetables will also be affected by the storage time. If most of the goods are not sold on that day, they will not be sold on the next day. From the demand side, the sales volume of vegetables is related to the supply side of vegetables from April to October. At the same time, the sales space of the supermarket is limited, which makes a reasonable sales portfolio extremely important. The commercial overpricing replenishment decision system based on LSTM analyzes the relationship between daily sales volume and cost-plus pricing for each vegetable category and single product over three years. It predicts the sales volume and wholesale price of each category for the next week, determining replenishment volume and pricing strategy based on forecasted sales volume and average loss rate. The system considers limited sales space and uses the entropy method to calculate weights for key indicators like total profit. It employs the TOPSIS method to sort items, considering forecasted wholesale prices, to select items for pricing decisions. The system aims to maximize total profit while ensuring the supermarket meets minimum display conditions. By assisting supermarkets in formulating more reasonable pricing strategies, it aims to reduce overstock, backlog, or stock shortages, ultimately improving sales volume and operational efficiency.

Pemanfaatan Media Sosial Instagram Dalam Meningkatkan Hasil Penjualan Angkringan Pojok Sate Taichan Jl. Letjend Suprapto Kota Bengkulu

This research aims to explore how Pojok Sate Taichan business on Jl. Letjend Suprapto, Bengkulu City utilizes Instagram social media to enhance sales performance. This qualitative study employs an inductive thinking approach to understand the reality of the subject matter. The subjects of this study are the operators of Pojok Sate Taichan on Jl. Letjend Suprapto, Bengkulu City. The object of the study is the utilization of social media by the operators of Pojok Sate Taichan on Jl. Letjend Suprapto, Bengkulu City. The findings reveal that Pojok Sate Taichan in Bengkulu effectively utilizes Instagram's Story and Reel features, resulting in a significant increase in daily sales by 39-50% and daily revenue by 15-20%. A comparison before and after using social media indicates a perceived increase in sales results by up to 133% and revenue by approximately 80% for the business operators. Therefore, the effective integration of Instagram social media by Pojok Sate Taichan on Jl. Letjend Suprapto, Bengkulu City has positively impacted the growth of SMEs, such as increased sales, revenue, and opportunities for investment and further expansion.

No need for meat as most customers do not leave canteens on Veggie Days

Switching to a diet lower in red meat has the potential to reduce greenhouse gas emissions. Using a unique time series of daily sales data from three German university canteens from 2017 to 2019, we analyse the effects of a monthly Veggie Day in a food-away-from-home context. We find that the temporary ban on meat dishes did not lead to a widespread boycott – as the heated public debates might have suggested. In our setting, a Veggie Day could reduce greenhouse gas emissions by up to 66%. However, especially at the site with a higher share of meat eaters on regular days, up to 22% of customers bypassed the meat-free main dishes on Veggie Days and ate at other on-site alternatives where meat was available. However, total on-site sales did not decrease significantly. Students were less likely to switch to alternatives than staff and guests. A less stringent implementation of a Veggie Day where only beef dishes were removed from the menu, did not result in a significant shift to alternatives but could reduce emissions by up to 51%.

Kinks as Goals: Accelerating Commissions and the Performance of Sales Teams

Piecewise-linear reward schemes are very common in workplaces; by definition, they divide the set of possible performance levels into disjoint intervals that could have psychological value to workers. When these schemes take the form of lump-sum bonuses, any psychological rewards associated with attaining a performance interval are hard to distinguish from material ones because both material and psychological incentives predict bunching at the output needed to earn the bonus. To disentangle these effects, this paper studies a common but understudied class of piecewise-linear pay—accelerators—in which the mechanisms’ predictions conflict. Using the distributions of daily sales in 103 small retail teams, we find that daily sales bunch at accelerators, which is inconsistent with models in which only material rewards matter. Combining simple theoretical models, institutional evidence, and heterogeneity analyses, we argue that this unexpected bunching results from teams’ decisions to use the convenient, salient kink points as shared goals, which yield symbolic utility to their members when the points are attained. This paper was accepted by Lamar Pierce, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01661 .

Heat of the Moment: How Temperature Influences the Search and Purchase of Energy-Using Appliances

Decisions with significant and long-lasting consequences can be influenced by conditions at the moment of choice, such as weather. Using administrative data from an online retailer, we examine whether temperature and other weather variables affect the search and purchase of energy-using durables, namely, air conditioners (ACs) and dryers. We observe more sales of ACs on hot days and fewer sales of dryers on hot, windy days. We find no impact for appliances whose usefulness is not affected by the weather. For AC, weather-induced searches and purchases are in lower-efficiency energy classes. Product search data allow us to look into the process leading up to purchase. Prospective AC buyers search less intensively when the temperature is higher, and the opposite holds for buyers of dryers when temperature and wind speed increase. Models of memory and attention can explain these behavioral patterns. Understanding these dynamics is important for designing adaptation and mitigation policies, given the energy needs of cooling technologies and their increased demand and usefulness in a rapidly warming world.

Aplikasi Pemesanan Kue dan Roti Berbasis Mobile Pada Lamonde Palembang

Technology in buying and selling can be utilized to increase company profits. PT. Palembang Berkah Bersama was established in April 2017 with the business brand Lamonde Palembang, a business venture making cakes and bakeries. The product ordering process takes a long time because consumers must come to the Lamonde Palembang store or type via WA or telephone. Hence, the shop assistant has a little difficulty in recapitulating data if there is an order simultaneously. Based on interviews conducted by researchers with Mr. Erik, Marketing Manager of Lamonde Palembang, the daily sales turnover is around 60-80 pieces of cake and bread. Therefore, what is needed by Lamonde Palembang is an ordering system where the system is used to handle the ordering process online. With this system, it will be easier for consumers to order products because they can directly choose the products provided, where there are also prices for each product. The total cost of the order also appears to be well and can be determined quickly. Shop assistants can also more easily see and list orders and product sales

Empowerment Setbacks and Coping Strategies in the COVID-19 Crisis of Female Food Vendors in Tanzania

The Government of Tanzania has established a conducive environment for gender equity, as reflected in the Millennium Development Goals(MDGs). Female food vendors operating in Tanzania's urban settings face multifaceted socio-economic and cultural challenges, with these factors significantly influencing how they are perceived and treated within their communities. This, in turn, can impact their ability to negotiate and make decisions in business settings, particularly during epidemics. However, the Covid-19 pandemic exacerbated the challenges. This study delved into the effects of COVID-19 on food vending businesses and coping mechanisms amidst its raging consequences. The study sequentially employed qualitative and quantitative approaches to gather data from female food vendors using in-depth interviews and questionnaires. Of all the collected questionnaires, 304 were sufficient for descriptive and paired t-test analyses. Thematic analysis was applied to qualitative data. The findings underscore the significant adverse impact of the COVID-19 pandemic on the business performance of female food vendors. Indicators such as the number of employees, average daily sales, average daily profit, and daily working capital exhibited statistically significant declines during the pandemic compared to pre-pandemic levels. Coping strategies employed by female food vendors included strict adherence to COVID-19 protocols, workforce reductions, trimming daily allowances for assistants, cutting down on working capital, streamlining food items on the menu, and avoiding expensive menu options. The study highlights the negative impact of the pandemic on business performance, emphasizing the need for government intervention through subsidies, especially during pandemics. Additionally, the study recommends business formalization to access subsidies and micro-credits better. Following the analysis, the study offered detailed policy recommendations to address specific issues identified during the research. Additionally, suggestions for further studies are presented, indicating potential avenues for future research to deepen the understanding of the subject matter.

New Estimates on the Cost of a Delay Day in Drug Development.

Two frequently cited figures by clinical research insiders and observers - the cost of missing a day to generate prescription drug sales and the cost of a day to conduct a clinical trial - are outdated and based on anecdotal evidence. In late 2023, the Tufts Center for the Study of Drug Development conducted empirical research to gather more accurate and granular estimates and to test whether average sales per day have changed over time. 645 drugs launched since 2000, and 409 clinical trial budgets were drawn from commercially available and proprietary data sets and analyzed. The results indicate that a single day equals approximately $500,000 in lost prescription drug or biologic sales, with daily prescription sales for infectious, hematologic, cardiovascular, and gastrointestinal diseases among the highest. The results also show that each year, the average sales per day of prescription drugs and biologics has decreased by approximately $80,000-$100,000. The estimated direct daily cost to conduct a clinical trial is approximately $40,000 per day for phase II and III clinical trials, with those in respiratory, rheumatology, and dermatology having the highest relative daily direct costs.

Analisis Penerapan Biaya Volume Laba terhadap Perencanaan Laba Perusahaan (Studi Kasus pada Franchise C’bezt Fried Chicken Gedangan, Sidoarjo)

This research aims to analyze and calculate profit planning for the Cbezt Fried Chicken business in Gedangan, Sidoarjo. The research method used is descriptive qualitative. Primary data and secondary data were used in this research. Primary data was obtained through interviews and direct observation at Cbezt Fried Chicken. The research results show that to achieve the expected profit, the business unit needs to sell 14,557 pcs of fried chicken in 3 months or achieve revenue of Rp. 87,341,090. This means that daily sales must reach a minimum of 202 chickens or a daily sales target of Rp. 1,213,083 assuming selling days are 24 days a month. In conclusion, this research provides a clear view for business owners in planning sales strategies to achieve the expected profit targets. Keywords: CVP, Operating Leverage, Contribution Margin, Safety Margin

Research on Automatic Pricing and Replenishment Decision of Vegetable Products Based on Machine Learning

In fresh food supermarkets, due to the short shelf life of vegetable products and the deterioration of products over time, unsold products on the same day cannot be sold the next day. Therefore, supermarkets need to make automatic pricing and replenishment decisions on a daily basis based on historical sales and demand. All product data for this study were provided by the 2023 Higher Education Society Cup National College Student Mathematical Modeling Competition.This study aims to explore the relationship between commodity sales and wholesale prices and pricing using five machine learning methods: neural networks, linear regression, decision tree regression, random forest regression, and XGBOOST regression, in order to predict sales and wholesale prices for the next week. By comparing the performance of five machine learning methods, it was found that the XGBOOST regression prediction model is the most suitable for predicting the daily sales of goods. The linear regression prediction model is the most accurate for predicting the wholesale prices of "chili", "flower and leaf", "edible fungi", "cauliflower", and "eggplant" goods, while the random forest regression prediction model is the most accurate for predicting the wholesale prices of "aquatic rhizome" goods. By constructing a dynamic programming model and using genetic algorithms to solve, automatic pricing and replenishment decisions for various goods within the next week can be obtained.

Replenishment Pricing of Vegetable Commodities in a Supermarket as an Example in China

In this paper, for the automatic pricing and replenishment decision-making problem of vegetable commodities, it is found that there are certain seasonal characteristics in the sales of vegetables in each category, and wavelet analysis is carried out on the daily sales data of single products to get the sales cycle of each category. For the vegetable single product with large amount of data, all single products are divided into four categories of common household vegetables, packaged vegetables, seasonal special vegetables and special vegetables by systematic cluster analysis, and the time distribution characteristics of single products are described by the sales time series of typical members in each cluster. Based on the market demand theory, the cost-plus pricing and sales volume data of the categories were fitted, and the relationship was obtained to be negatively correlated. Taking the purchase quantity and pricing of each category as the decision variables, maximizing revenue as the objective function, and taking the historical maximum purchase quantity of each category and the historical maximum purchase quantity of the superstore as the constraints, a nonlinear programming model was established to formulate the pricing and replenishment strategies for vegetable products. By solving the model, the optimal strategy of replenishment volume and pricing for the future seven-day category is derived, corresponding to the maximum profit of ￥3124.75.

Pricing and replenishment strategy formulation for the fresh produce market

To improve the profit margin of the fresh supermarket and optimize the pricing and replenishment strategies of goods, this article provides a time series analysis of the sales volume of various categories and individual items of vegetables. The sales volume and the relationship with time are analyzed on a monthly, quarterly, and annual basis. The article discovers the seasonal and cyclical patterns in the sales volume of vegetable categories and individual items. Using the Spearman correlation coefficient and hypothesis testing, the study reveals strong positive correlations in the sales volume of cauliflower and leafy flower products, as well as strong negative correlations in the sales volume of eggplant and aquatic root and stem products. To formulate pricing strategies for the fresh supermarket in the coming week, the article uses the ARIMA model to forecast the daily total sales volume of various categories. It also constructs a planning model for maximizing the revenue of the fresh supermarket, taking expected profit margin, discount factors, and daily replenishment total as independent variables. The simulated annealing algorithm is employed to solve this model, and the result indicates that the maximum revenue for the fresh supermarket in the coming week is 4269.6 yuan. The article provides procurement and pricing strategies for the fresh supermarket in the next week based on this analysis.

Automated pricing and replenishment decisions for vegetable products based on evaluation optimization models

Based on the commodity information of the supermarket in the annex, the detailed data of historical sales flow, the wholesale price of vegetable commodities and the recent loss rate of vegetable commodities, and through the data analysis of each category and each single product, the automatic pricing and replenishment decision-making model of commodities is established. Use the optimization evaluation algorithm to formulate the total daily replenishment and pricing strategy of each category and each single product. In order to solve the first problem, firstly, the outliers in the original data of Annexes 2 and 3 are cleaned, normalized, feature selected and dimensionally reduced. Secondly, a quarter is taken as a sales cycle of supermarkets, so as to find the proportion of sales volume of a certain category in the same quarter of three years to the total sales volume, and give the distribution law of sales volume of different categories, the results are shown. Considering different periods again, the daily sales volume distribution law is calculated by taking one day as a sales cycle, and the results are shown. Finally, the Pearson grade correlation coefficient is used to judge the relationship between the processing indicators, and the matrix heat map is obtained. According to the two results, it was concluded that there was a significant positive correlation between the sales volume of mosaic and cauliflower vegetables, and a significant negative correlation between the sales volume of nightshade and aquatic root vegetables. In view of the second problem, firstly, considering the functional relationship between the total sales volume and the cost pricing, the correlation analysis and linear fitting were carried out to obtain the linear relationship between the sales price of each category and the maximum value of the sales volume of each category in July of the previous year can be described as Through further nonlinear fitting and optimization problem solving, the total daily replenishment volume and pricing strategy of each vegetable category in the coming week (July 1-7, 2023) are shown in Table 1 and Table 2, which makes the supermarket have the largest revenue In response to the third question, based on the known data, we can analyze the data requirements for each data: we need to know the sales volume of various vegetables during this period, we need to determine the purchase cost of each vegetable, we need to understand the past pricing strategy and response, and we need to know the inventory of various vegetables on June 30. On this basis, a multi-objective dynamic programming model is established, and the total number of saleable items is 30 by using the greedy algorithm to obtain the replenishment quantity of single items on July 1, and the pricing strategy is further solved by using the linear equation fitted in problem 2. In response to the fourth problem , on the basis of the existing sales, wholesale price and loss rate data, in order to better formulate the replenishment and pricing decisions of vegetable products, supermarkets also need to consider and collect the following 12 aspects of relevant data to assist in planning the pricing and replenishment decisions of vegetable products, such as: customer preference and satisfaction survey, seasonality and availability of vegetables, competitor information, inventory costs and storage conditions, historical sales data and trend analysis, customer flow and purchase period, nutritional value and health benefits of vegetables, Socio-economic factors, external environmental factors, policy and regulatory factors, technological and innovation factors, and supply chain and logistics information to ensure more comprehensive and accurate decision-making. Among them, the analysis of historical sales data and trends is mainly carried out.

The impact of tightened prescribing restrictions for PBS-subsidised opioid medicines and the introduction of half-pack sizes, Australia, 2020-21: an interrupted time series analysis.

To evaluate the impact of the tightened Pharmaceutical Benefits Scheme (PBS) prescribing rules for immediate release (IR) and controlled release (CR) opioid medicines (1 June 2020), which also eliminated repeat dispensing without authorisation for codeine/paracetamol and tramadol IR and introduced half-pack size item codes for IR formulations. Population-based interrupted time series analysis of PBS dispensing data claims for a 10% sample of PBS-eligible residents and IQVIA national opioid medicine sales data (PBS-subsidised and private prescriptions), 28 May 2018 - 6 June 2021. Mean amount of PBS-subsidised opioid medicines dispensed per day and mean overall amount sold per day- each expressed as oral morphine equivalent milligrams (OME) - overall, by formulation type (IR, CR), and by specific formulation. During the twelve months following the PBS changes, daily PBS-subsidised opioid medicine dispensing was 81 565 OME lower (95% CI, -106 146 to -56 984 OME) than the mean daily level for 2018-20, a decline of 3.8% after adjusting for the pre-intervention trend; the relative reduction was greater for IR (8.4%) than CR formulations (2.6%). Total daily sales of all, IR formulation, and CR formulation opioid medicines did not change significantly after the PBS changes. Repeat dispensing of prescriptions comprised 7.4% of PBS-subsidised opioid dispensing before 1 June 2020, and 1.3% after the changes. Half-pack sizes comprised 8.4% of PBS-subsidised IR opioid medicine dispensing and 2.8% of all opioid medicines sold in the twelve months after the PBS changes. The introduction of new PBS rules for subsidised opioid medicines was followed by a decline in PBS-subsidised dispensing. Some people may have bypassed the new restrictions by switching to private prescriptions, but our findings suggest that opioid medicine use in Australia declined as a result of the new restrictions.

The impact of website performance on business sales

Abstract In this study, we aimed to investigate the financial implications of website performance on restaurant visitor traffic. It is crucial to address the current challenges faced by the restaurant industry, such as decreasing diner numbers due to rising prices, which can have a negative impact on the financial results of companies. Recognizing the significance of maximizing profitability, especially for small businesses operating in a highly competitive industry, we sought to explore the potential of website performance as a driver of increased visitor traffic and daily menu sales. We conducted a two-month field experiment in which we measured morning website visits and daily lunch menu sales for a restaurant with a slower website and one with a quicker website. However, we did not find any statistically significant increase in visits to the restaurant as a result of improving the website’s speed. We conclude that there may be other ways to improve daily menu sales beyond website speed. The restaurant industry is highly competitive, and small businesses in particular need to carefully consider how to allocate their resources in order to maximize profitability. The results of our study suggest that investing in website redesign as a means of increasing visitor traffic may not be the most effective tactic for small restaurants. Our research highlights the importance of conducting experiments and gathering data to inform decision making, as it can help small businesses in the restaurant industry to make more informed choices about how to allocate their resources. By understanding the factors that do and do not impact sales, small restaurants can make more informed decisions and achieve their business goals.

Analisis Penerapan Target Coting Dalam Upaya Efisiensi Biaya Produksi Dan Peningkatan Laba Usaha Pada Usaha Kecil Menengah

UD. Adon Jaya which is located at Jl. Bima, Ds. Kebonsari RT 05 RW 01, Kec. Candi, Sidoarjo Regency, East Java is a Small and Medium Enterprise (UKM) owned by Pak Sulaiman which operates in the food sector, namely salted eggs. Salted eggs produced by UD. Adon Jaya has an interesting innovation where the salted eggs have different flavors and processing methods, such as original, fried, grilled and oven flavoured. Daily sales can reach 400 to 500 salted eggs with an income of IDR 16,000,000 to IDR 18,000,000 per month with a profit percentage of 31%. The research objective to be achieved in accordance with the research questions explained above is to find out, analyze and answer how the application of target costing affects production cost efficiency and increasing business profits at UD. Adon Jaya. This research took informants from UD owners. Adon Jaya, namely Mr Sulaiman and production employee Mas Zaky. In its implementation in the field, this research took secondary data including company history, financial and sales report data, as well as an explanation of the salted egg production process. The type of research that will be used is qualitative methods. The analysis technique used is comparative analysis between manual calculations used by UD. Adon Jaya with calculations that apply target costing carried out by the author. These results show that implementing target costing can streamline production costs by 12%, so that the resulting profit also increases 37%. The results of the analysis findings conclude that the application of target costing at UD. Adon Jaya has a positive influence by streamlining production costs so as to increase profits.