Analysis and Design of Inventory Control Information Systems With Forecasting Methods: Moving Average and Exponential Smoothing

Inventory inventory on CV. Mitra Marga Sejahtera often experiences stockpiling of goods so that it wastes more costs and the manual process of recording goods using excel, so that they often experience data corruption and loss of sales data. Forecasting methods are usually used by the sales department in planning (sales planning) based on the results of sales forecasts, so that forecasting information can be useful for Production which uses Moving Average and Exponential Smoothing. the program that has been made using the forecasting method can help manage the stock of goods that will be needed in the coming months, so that store managers can save costs in stock items that are not excessive

Assessing the Accuracy of Sales Forecasts Submitted by Pharmaceutical Companies Applying for Reimbursement in Austria

Objectives: Reimbursement decisions on new medicines require an assessment of their value. In Austria, when applying for reimbursement of new medicines, pharmaceutical companies are also obliged to submit forecasts of future sales. We systematically examined the accuracy of these pharmaceutical sales forecasts and hence the usefulness of these forecasts for reimbursement evaluations. Methods: We retrospectively analyzed reimbursement applications of 102 new drugs submitted between 2005 and 2014, which were accepted for reimbursement outside of hospitals, and for which actual reimbursed sales were available for at least 3 years. The main outcome variable was the accuracy ratio, defined as the ratio of forecasted sales submitted by pharmaceutical companies when applying for reimbursement to actual sales from reimbursement data. Results: The median accuracy ratio [95% confidence interval] was 1.33 [1.03; 1.74, range 0.15–37.5], corresponding to a median overestimation of actual sales by 33%. Forecasts of actual sales for 55.9% of all examined products either overestimated actual sales by more than 100% or underestimated them by more than 50%. The accuracy of sales forecasts did not show systematic change over the analyzed decade nor was it discernibly influenced by reimbursement status (restricted or unrestricted), the degree of therapeutic benefit, or the therapeutic area of the pharmaceutical product. Sales forecasts of drugs with a higher degree of innovation and those within a dynamic market tended to be slightly more accurate. Conclusions: The majority of sales forecasts provided by applicants for reimbursement evaluations in Austria were highly inaccurate and were on average too optimistic. This is in line with published results for other jurisdictions and highlights the need for caution when using such forecasts for reimbursement procedures.

Adoption of digital technologies and backshoring decisions: is there a link?

The present paper assesses whether the adoption of Industry 4.0 technologies can be related to backshoring. It does so by -firstly- investigating the implementation of such technologies by industrial firms with foreign production plants, the experiences and intentions of these firms regarding the location of production activities, and -secondly- by analyzing backshoring cases among them.It finds that backshoring is a rare phenomenon, and it is questionable whether there is a correlation, left alone causality, between the adoption of digital technologies in home-based manufacturing sites and backshoring hitherto. And while the future may hold more backshoring movements in store, they may not be primarily due to the adoption of Industry 4.0 technologies at home-based plants. Instead, other (foreign) location-specific factors seem to have greater weight in the decision-making processes around backshoring operations. I.e., deteriorating sales forecasts in offshore places where firms have production activities, increases in institutional uncertainty in such places, rationalization of global production apparatuses, and/or a lack of possibilities to deploy foreign manufacturing activities and output for third markets. Also against the backdrop of events like the outbreak of Covid19 and the uncertainty-raising effect it has on international business, the trade-off between producing off-shore or bringing manufacturing activities back home is not likely to depend on technology adoption levels at home and abroad either.

Web-Based Inventory Application Development for PT. Palugada Indonesia

PT. Palugada Indonesia still records their transactions and inventory of goods in traditional ways, which could result in some errors caused primarily by human interventions. The aim of this research is to assist PT. Palugada Indonesia to control their inventory through an information system. Specifically, a web-based application that can process product data, supplier data, customer data, user data and transaction data was purposefully developed in this study. In addition, the developed application has notification and sales forecast features. These features are expected to further assist PT. Palugada Indonesia to control their inventory optimally. Moreover, the application also has some other useful features, for instance in displaying more informative data, by bringing up sales forecasts and notifications. The developed application was modeled using UML diagram 2.0 and developed using HTML, PHP, and MySQL database. Index Terms—inventory; web-based application; saels forecast; PT. Palugada Indonesia

Forecasting the inventory levels of an industrial enterprise in conditions of demand volatility

The aim of the article is to provide tools for obtaining reliable forecasts of the level of inventories of the enterprise in conditions of volatility in demand for products. Most types of demand for industrial products are unstable, so it is important to form stocks based on demand forecasts to reduce logistics risks. The results of the analyses. Analytical tools for forecasting maximum level of inventories in conditions of volatility of demand for products of machine-building enterprises have been developed, which provides an opportunity to obtain the most reliable sales forecast and estimate the maximum required stocks for a certain type of demand. The method, which is obtained by analytical tools, is based on a three-stage algorithm: a) identification of trends in a time series of sales; b) obtaining optimal sales forecasting models; c) plotting of interval forecasts of product sales and risk assessment of the formation of its maximum stocks. The developed methodology identifies logistics risks, which depend on sales forecasts, for nine machine-building enterprises of Ukraine. A method for statistical assessment of logistics risks of machine-building enterprises by confidence intervals has been developed, in which maximum stocks are determined by two confidence intervals of sales forecasts, and the risk of error is associated with the appropriate levels of reliability of these intervals. It is proposed to build the upper limits of two confidence intervals, for example, 95% and 99%, according to the forecast inventory level estimates, and to consider them as maximum inventory level estimates with corresponding probabilities. The risk of stock shortages is defined as the probability of going beyond the upper limit of the corresponding interval. It is proved that the dynamics of monthly or quarterly sales of enterprises can be typed by four patterns: the presence of seasonal fluctuations and trends; the presence of purely seasonal fluctuations without a pronounced trend; no seasonal fluctuations, but the presence of a trend; no seasonal fluctuations and trends. Conclusions and perspectives for further research. It is proved that the volatility of monthly or quarterly sales volumes of enterprises can be typed by four patterns: 1) the presence of seasonal fluctuations and trends; 2) the presence of purely seasonal fluctuations without a pronounced trend; 3) no seasonal fluctuations, but the presence of a trend; 4) no seasonal fluctuations and trends. Based on this, the theoretical and methodological principles and analytical tools for forecasting the maximum stocks of an industrial enterprise in conditions of demand volatility were improved. Keywords: seasonality, volatility, inventory level forecasting, maximum stocks, demand forecasting.

The Design of ERP Intelligent Sales Management System

With the popularization of informatization, most companies have their own information management systems. However, faced with massive amounts of data, most companies cannot integrate and utilize its potential value. When traditional companies make sales forecasts, they usually purchase data completion, connect multiple data source channels, and then judge the future sales situation perceptually. This lack of data accumulation and analysis, and it is impossible to display the intrinsic value of the massive data in the system. Some companies will also build professional data analysis teams or seek help from third-party companies. But this will cause high expenses and greatly reduce sales expenses. This study combines artificial intelligence technology with ERP sales management system. We apply artificial intelligence, machine learning, cloud computing to the design and construction of intelligent ERP sales management system. It solves the core problems of enterprise product sales through the deep learning function of the sales system. It may predict enterprise sales and rationally allocate enterprise resources, increase product sales effectively, and help enterprises build effective modern management systems.

Use of Machine Learning Models in Sales Forecasts

In this paper, we present a brief survey of usage of various machine learning models and their role in retail sales forecasts. The purpose of this paper is to enlist a few popular approaches in retail sales and study their scope and areas of application. We analyze how these models have evolved over time stating the significance of each model in brief