Discussion on the Application of Information Systems in Spare Parts Inventory Management

(x) —— The probability distribution function of the company’s demand being x during time period t； d——Average daily demand for regional warehouse k during period t ； S — Reorder point for regional warehouse k ； S — Maximum inventory at regional warehouse k. Additionally: S = S – the maximum inventory level of the company. △＝S－s—The difference between the maximum inventory in region warehouse k and the reorder point. d = d—the average daily demand of the company during period t. D = d – the total demand for region warehouse k during time period t. Method 1: Under the traditional inventory management approach, the headquarters is unaware of the specific needs of each region; it merely makes purchasing and inventory planning decisions based on past demand forecasts for those regions ； Method 2: Under the new inventory strategy, and within an environment of information sharing, the headquarters carries out organized and coordinated inventory management. It can obtain at any time accurate data on the demand from its downstream regions across previous ordering cycles, thereby enabling it to formulate its own procurement and coordination plans. Since the total demand for spare parts by the company is the sum of the demands from the 7 regions, the probability distribution function for the company’s demand to be x at time t can be determined using methods related to discrete random variables and probabilities ； ?（x）＝P（x＝x）（6–1). Since all regions follow the (s, S) inventory control strategy, the piecewise distribution function of ?（x） can be determined as follows: Method 1: It is assumed that the probability that region k will place an order after a time period t since the last order is P（x）＝P（d≥△｜d＜△）（6–3). The probability distribution function for the order quantity x placed by region k within time period t is ?（x）＝. Method 2: Thanks to timely information transmission and sharing, the headquarters coordination center can keep track of the consumption of spare parts in each region on a daily basis. Assuming that the actual consumption in region k at time t is D, then the probability of an order being placed on day t+1 and the probability that the order quantity will be x is P（x）＝P（d≥△｜d＜△）（6–5）. ?（x）＝. It can be seen that by using the actual consumption value D instead of the previously predicted value q, the error is reduced; thus, the procurement plan becomes more aligned with actual demand, which helps to lower inventory costs while improving the level of inventory satisfaction. Discussion on the Application of Information Systems in Spare Parts Management. The overall goal of the spare parts management information system at XCMG Technology Marketing Company is to build a networked service system based on the Internet/Intranet, building upon the existing information infrastructure of the company. This system aims to integrate various business activities related to spare parts, such as service provision, allocation, sales, procurement, and inventory management, into a unified platform in order to standardize and regulate these operations ； At the same time, information technology is utilized to implement the company’s strategy of jointly managing spare parts inventory based on a coordination center, thereby enabling virtual centralized management of physically dispersed spare parts resources through information flows ； The implementation of information systems should enable the provision of real-time and historical, accurate, reliable, and aggregated information regarding a company’s overall spare parts management activities. By utilizing this information, companies can respond more quickly to users’ needs, offer more timely services, and ultimately improve the quality of service as well as customer satisfaction. This intelligence information can also be shared with other departments such as corporate strategy, planning, finance, marketing, and customer service, enabling these departments to obtain the information needed for their respective areas of work and helping them make better decisions ； At the same time, the system must enable XCMG Technology to achieve information integration throughout the entire product lifecycle. 1? Feasibility study: The main interfaces for the current spare parts management information system are the ERP production management system currently in use by the company, and the networked service system based on the Internet/Intranet that is to be developed. The spare parts management information system sends data requests to the ERP system; the ERP system retrieves the data and transmits it to the spare parts management information system via ODBC. The spare parts management information system then provides the required data to the WEB system. (1) From the perspective of business management, although spare parts management involves a wide range of aspects and is technically complex, with the support of modern management theories, technical economics, and market economics principles, it is possible to derive relevant calculation formulas by combining theoretical knowledge with practical experience as well as statistical methods. This not only meets the requirements of management but also provides a solid basis for the development of management software. (2) From the perspective of computer applications and development, the currently popular Internet enables the timely dissemination of various types of information around the world. Some developed countries as well as large domestic enterprises have adopted management models centered on computer networks, allowing organizations within these spare parts centers to exchange and share information through such networks, thereby providing the necessary foundation for enterprises to adapt to the developments of the market economy. (3) With the development of software technology, network applications based on file-sharing models have been replaced by those based on the client/server (C/S) model. (4) Development methods for management information systems are highly mature: systems that are easy to use and maintain can be developed by utilizing network and communication technologies, C/S structure planning and design techniques for networks and communications, database technologies, JAVA technology, and so on. Taking all these aspects into account, and considering both the current status of spare parts management and computer technology, it is feasible to use computer networks in equipment management departments to replace some manual management and decision-making processes. 2. Implementation of information simulation – Achieving multi-level, barrier-free sharing of information through information systems is an important means to ensure scientific management of spare parts. Information systems can integrate all aspects related to spare parts management into an enterprise’s information management platform, enabling various entities such as spare parts suppliers (including the manufacturing department), spare parts centers, regional spare parts centers, and service stations/marketing centers to operate on the same platform, thus allowing seamless flow of information among all these components. Through the real-time aggregation of data information, the spare parts center is able to keep track in real time of the demand information from regional spare parts centers and spare parts agents, as well as of the current inventory levels, thereby enabling it to make timely decisions based on these demands and inventory conditions. Such an approach enables resource sharing and integration on a spatial level, turning physically dispersed inventory into a virtual, centralized inventory, and allowing for centralized management of the inventory through information flow. At the same time, centralized storage of information can reduce inconsistencies in data, thereby minimizing losses caused by such inconsistencies. With historically stored data, scientific analysis can be used to predict the future demand for a certain material more accurately, thereby reducing errors in demand forecasting. The \"bullwhip effect\" – whereby demands amplify at each stage of the process due to the inability to share demand information among enterprises at various stages and the independent operation of inventories – can be effectively controlled. The sharing of demand and inventory information can reduce the volatility of overall demand, as high demand in one regional center can be offset by low demand in another; by transferring materials from locations with low demand to those with high demand, overall inventory costs can remain relatively stable. The following uses a simulated budget to illustrate the role of information systems in inventory forecasting: Assume that the equipment component system consists of a central warehouse and 7 regional warehouses, with each warehouse implementing an (s, S) inventory control strategy. First, list the symbols that appear in the model and their meanings (where K = 1, 2, 3, 4, 5, 6, 7), as follows: I — the initial inventory level of region warehouse k at time period t ； P(x) is the probability distribution of the demand for region warehouse k at time period t being x, with ∑P(x) = 1 ； ?（x）——The probability distribution function of the ordering demand being x at time period t in region warehouse k ； x —— Order volume generated by region warehouse k during period t ； ?（x）——The probability distribution function of the company’s demand being x during time period t ； d——Average daily demand for regional warehouse k during period t ； S — Reorder point for regional warehouse k ； S — Maximum inventory at regional warehouse k. Additionally: S = S – the maximum inventory level of the company. △＝S－s—The difference between the maximum inventory in region warehouse k and the reorder point. d = d—the average daily demand of the company during period t. D = d – the total demand for region warehouse k during time period t. Method 1: Under the traditional inventory management approach, the headquarters is unaware of the specific needs of each region; it merely makes purchasing and inventory planning decisions based on past demand forecasts for those regions ； Method 2: Under the new inventory strategy, through real-time aggregation of data and information via information sharing, the spare parts center can keep track in real time of both the demand information from regional spare parts centers and spare parts distributors, as well as the current inventory levels, allowing it to make timely decisions based on these demands and inventory conditions. Such an approach enables resource sharing and integration on a spatial level, turning physically dispersed inventory into a virtual, centralized inventory, and allowing for centralized management of the inventory through information flow. At the same time, centralized storage of information can reduce inconsistencies in data, thereby minimizing losses caused by such inconsistencies. With historically stored data, scientific analysis can be used to predict the future demand for a certain material more accurately, thereby reducing errors in demand forecasting. The \"bullwhip effect\" – whereby demands amplify at each stage of the process due to the inability to share demand information among enterprises at various stages and the independent operation of inventories – can be effectively controlled. The sharing of demand and inventory information can reduce the volatility of overall demand, as high demand in one regional center can be offset by low demand in another; by transferring materials from locations with low demand to those with high demand, overall inventory costs can remain relatively stable. The following uses a simulated budget to illustrate the role of information systems in inventory forecasting: Assume that the equipment component system consists of a central warehouse and 7 regional warehouses, with each warehouse implementing an (s, S) inventory control strategy. First, list the symbols that appear in the model and their meanings (where K = 1, 2, 3, 4, 5, 6, 7), as follows: I — the initial inventory level of region warehouse k at time period t ； P(x) is the probability distribution of the demand for region warehouse k at time period t being x, with ∑P(x) = 1 ； ?（x）——The probability distribution function of the ordering demand being x at time period t in region warehouse k ； x —— Order volume generated by region warehouse k during period t ； ?（x）——The probability distribution function of the company’s demand being x during time period t ； d——Average daily demand for regional warehouse k during period t ； S — Reorder point for regional warehouse k ； S — Maximum inventory at regional warehouse k. Additionally: S = S – the maximum inventory level of the company. △＝S－s—The difference between the maximum inventory in region warehouse k and the reorder point. d = d—the average daily demand of the company during period t. D = d – the total demand for region warehouse k during time period t. Method 1: Under the traditional inventory management approach, the headquarters is unaware of the specific needs of each region; it merely makes purchasing and inventory planning decisions based on past demand forecasts for those regions ； Method 2: Under the new inventory strategy, and within an environment of information sharing, the headquarters carries out organized and coordinated inventory management. It can obtain at any time accurate data on the demand from its downstream regions across previous ordering cycles, thereby enabling it to formulate its own procurement and coordination plans. Since the total demand for spare parts by the company is the sum of the demands from the 7 regions, the probability distribution function for the company’s demand to be x at time t can be determined using methods related to discrete random variables and probabilities ； ?（x）＝P（x=x）（6–1). Since all regions follow the (s, S) inventory control strategy, the piecewise distribution function of ?（x） can be determined as follows: Method 1: It is assumed that the probability that region k will place an order after a time interval t since the last order is P（x）＝P（d≥△|d<△）（6–3). The probability distribution function for the order quantity x placed by region k within time interval t is: Method 2: Thanks to timely information transmission and sharing, the headquarters coordination center can keep track of the consumption of spare parts in each region on a daily basis. Based on this, if the actual consumption in region k at time t is D, then the probability of an order being placed on day t+1 and the probability that the order quantity will be x is P（x）＝P（d≥△|d<△）（6–5). ?（x）＝ It can be seen that by using the actual consumption value D instead of the previously predicted value q after making adjustments, the error is reduced, and the procurement plan becomes more aligned with actual demand. This not only lowers inventory costs but also improves the inventory fulfillment rate