Spare Parts Inventory System Research Articles

UNJUK KERJA MESIN VERTICAL RAW MILL PLANT 10

PT X. is a cement manufacturing company. which experiences production losses due to the high value of downtime. Based on downtime data, the largest machine downtime value is found in the Raw Mill, so the Raw Mill machine becomes the object of research. This study aims to measure the effectiveness of the Raw Mill machine in the application of Total Productive Maintenance (TPM). The method used is Overall Equipment Effectiveness (OEE) to measure machine effectiveness and loss factor analysis with the Six Big Losses method. The results found that the average OEE value in 2022 was below the international OEE standard of 85%, namely 78% for Raw Mill Plant 10 in the 2019 period and 74% in the 2022 period. The low performance of the Raw Mill Plant 10 machine is caused by the breakdown loss factor and the reduced speed loss factor. The root causes found are lack of mechanical training and lack of control for spare parts. The recommendations given are the implementation of predictive maintenance, improving training facilities, and creating a spare parts inventory management system.

Optimization of multi-echelon spare parts inventory systems using multi-agent deep reinforcement learning

Multi-echelon inventory systems are commonly used in practice to satisfy widely distributed random demands of spare parts in an efficient and cost-effective manner. Optimization of a multi-echelon inventory system is a decision-making problem under uncertainties. Classic inventory policies (e.g. (s, S) and (R, Q)) that do not consider the inventory positions of other warehouses become suboptimal due to interrelationships among different warehouses caused by transshipment. The Markov decision process (MDP) is an effective tool for inventory optimization, which does not require a predetermined parameterized policy structure. Unfortunately, both the state and action spaces of MDP suffer from the curse of dimensionality when the number of warehouses increases. This paper optimizes the inventory of a large-scale multi-echelon inventory system using a new multi-agent deep reinforcement learning (MADRL) algorithm named EM-VDTD3 that is developed by introducing value decomposition and experience buffer modification into the twin delayed deep deterministic policy gradient (TD3) algorithm. Each agent in EM-VDTD3 manages a subsystem in the multi-echelon inventory system. Because different agents share the same network parameters, networks are customized to process subsystems with different parameters. Domain knowledge of inventory control is embedded in the learning process of EM-VDTD3 by adding expert experiences to the experience buffer. An efficient approximate method is developed to identify a teacher policy that generates expert experiences. Numerical studies about a spare part inventory system in the wind energy industry show that the proposed EM-VDTD3 outperforms benchmark methods.

Sistem informasi Inventory Iparepart Motor Pada Bengkel Al Motor

The development of information technology is growing so rapidly, making many people aware of the importance of information. At the AL motorcycle repair shop, the ongoing process is still not effective because the inventory data management is still using the manual method using Microsoft Excel which makes the process of searching for goods take quite a long time. Where sometimes it often happens that the data in the warehouse is wrong with the data in microsoft excel which makes the workshop party have to calculate the stock from the beginning again to match the data in microsoft excel with in the warehouse. The purpose of this research is to assist the workshop in recording stock which has been ineffective in terms of workmanship and the results of this study resulted in the application of this spare parts inventory information system to be a medium to increase accuracy in the suitability of data items with existing physical quantities.

OBJECT-ORIENTED PARTS INVENTORY INFORMATION SYSTEM MODELING USING UNIFIED MODELING LANGUAGE

The availability of spare parts in a vehicle service workshop needs to be correctly recorded and recorded, making it easier for workshop owners to ensure the complete availability of vehicle spare parts. From the results of the author's observations, the Rizki Prima workshop has several obstacles in recording the availability of spare parts where the business processes being carried out are still a hassle for the employees working in the workshop. The problem often occurs when recording is the incompleteness of data related to incoming and outgoing goods. So the recording made in the ledger needs to be more accurate and requires a relatively long time to search one by one in the register if the workshop owner needs it. As a result of poor recording of goods, it is difficult for the owner to order goods again, worried that duplicating data will record incoming goods. The above problems are the basis for the authors to conduct research by utilizing information systems and modeling with an object-oriented approach using a unified modeling language to design a spare parts inventory information system according to user needs. To provide an overview of the Rizki Prima workshop transforming from conventional spare parts data recording to digital spare parts recording. The above problems are the basis for the authors to conduct research by utilizing information systems and modeling with an object-oriented approach using a unified modeling language to design a spare parts inventory information system according to user needs. To provide an overview of the Rizki Prima workshop transforming from conventional spare parts data recording to digital spare parts recording. The above problems are the basis for the authors to conduct research by utilizing information systems and modeling with an object-oriented approach using a unified modeling language to design a spare parts inventory information system according to user needs. To provide an overview of the Rizki Prima workshop transforming from conventional spare parts data recording to digital spare parts recording. KEYWORDS: Modeling, Information Systems, Spare Parts Inventory, Availability, Unified Modeling Language

Analisa Dampak Disrupsi pada Permasalahan Spare Part Joint Replenishment

This study aims to minimize inventory costs by implementing a joint replenishment policy on the spare parts inventory system by considering disruption. Analysis was carried out on 11 spare parts related to the effect of disruption on the determination of T, kj and the total costs generated. The results showed that the implementation of joint replenishment policy can reduce inventory costs by up to 21.7%. The effect of disruption in the form of rising material prices affects the increase in total costs linearly. Based on this, companies can choose the right time to order spare parts inventory, so as to avoid excessive storage costs and ordering costs.

Perancangan Sistem Inventory Spare Part Elektronik Berbasis Web Application (Studi Kasus: Maspion Service Center)

This study aims to simplify and speed up the making of spare part stock reports at the Maspion Service Center and to avoid the risk of data recording errors that occur during spare part stock data collection. The method used in this research is the waterfall method. The researcher aims to design a spare part stock inventory system at the Maspion Service Center in order to simplify and speed up the making of spare part stock reports at the Maspion Service Center without recording in the ledger. The testing method used by the researcher is the Blackbox testing method. The results obtained in the design of this system are proven to be able to avoid the risk of data recording errors that occur during data collection of spare part stock, with the existence of a spare part stock database that is not possible with the same type of spare part twice

Perancangan Sistem Informasi Pengendalian Persediaan Sparepart dengan Metode Economic Order Quantity di Bengkel Mobil Sumber Jaya Probolinggo

: This research discusses the design of a spare part inventory control information system in a car workshop using the Economic Order Quantity (EOQ) method. This method functions as an optimization of inventory control by minimizing inventory and ordering costs, as well as avoiding excess or shortage of inventory. The author designed the information system using PHP programming language and MySQL as the database to facilitate the website development process. Testing was carried out by testing the functionality and performance of the system using real data. The results of this journal prove that the design of the information system can help car workshops to control spare part inventory more efficiently. By using the EOQ method, workshops can optimize the use of inventory, reduce inventory and ordering costs, and avoid excess or shortage of inventory. The designed information system also optimizes the performance of the workshop in improving its efficiency and productivity

ESG concept as a tool for optimising spare parts stocks in car service centres in the Republic of Cyprus

The article proposes a comprehensive analytical study of the potential for the use of the ESG concept as a tool for optimizing spare parts stocks in car service centers of the Republic of Cyprus. The study is based on the materials of the Statistical Service of the Republic of Cyprus, on the information provided in the relevant scientific literature, as well as on the results of the empirical research. An analysis of the management accounting data of 12 car service centers of the Republic of Cyprus was carried out, including 4 dealer networks, 4 independent multi-brand companies and 4 family businesses. The directions of implementation of the environmental part of the ESG concept at car service enterprises of the Republic of Cyprus are specified. A comparative assessment of the profitability of stocks and CO2 emissions in the business processes of managing auto parts stocks has been clarified. Expert assessments of the state of ESG management in the spare parts inventory management system in the analyzed car service centers are presented. It is recommended to conduct optimization of spare parts inventory management in car service centers of the Republic of Cyprus to multiply the economic and social effects from the development of the elements of the ESG concept based on the author’s CDMD model: Systematism - Decentralization - Monitoring (measurements) - Digitalization. The introduction of the proposed CDMD model should provide economic benefits, expressed in an increase in the profitability of inventories and various ESG gains.

A multi-tiered spare parts inventory forecasting system

A multi-tiered spare parts inventory forecasting system

Raw Mill Machine Effectiveness Measurement through the Total Productive Maintenance (TPM) Implementation

PT X is a cement manufacturer company that experienced a loss of production caused by the high value of downtime. Based on downtime data, the raw mill machine appears to have the most significant value in machine downtime. This study aims to measure the raw mill machine's effectiveness in applying Total Productive Maintenance (TPM). The method used in this research is Overall Equipment Effectiveness (OEE). This method measures engine effectiveness and loss factor analysis using the Six Big Losses method, as well as finding the root cause through a five why and cause and effect diagram to give the most suitable recommendations. The result of this study found that the average OEE value in 2021 was below the international OEE standard of 85%, which is 72% and 67% for Raw Mill Plant 9 and Raw Mill Plant 10, respectively. The low performance of Raw Mill Plant 9 and Raw Mill Plant 10 machines is a consequence of breakdown losses and reduced speed losses that had happened. The root causes were a lack of training for mechanics and no stock control for spare parts. The recommendations are to improve training programs and facilities and create a spare parts inventory management system.

IMPROVING SPARE PART INVENTORY SYSTEM MANAGEMENT BY USING 5S PRACTICES IN CONSUMER GOODS COMPANY

The spare part inventory is an important section that must be managed and controlled in the company, primarily because this section supports the provision of spare parts for the manufacturing process. An excellent spare part inventory management system matches the records in the SAP system in the warehouse and the actual physical evidence. The problem faced by the consumer goods company was the difference between recording in the SAP system and the physical evidence for non-consumable goods. By implementing improvements to the spare part management system for receiving and picking up goods in the warehouse, data discrepancies can be eliminated on non-consumable spare parts consisting of refurbished and new spare parts. The improvement was implementing the transit area and the 5S practices in the activities conducted in the spare part warehouse. The transit area is intended for comprehensive checking between warehouse personnel and other department involved with the goods stored or picked up. Refurbished and new items are more organized, making it easier to place and pick up items.

Information System for Honda Motorcycle Spare Parts Palembang

In practice, the industrial world that looks the same as the service industry requires the players to do a lot of quality improvement in order to compete. Its development is so rapid that many business actors use special tricks to run their business. Likewise with business actors from motorcycle service and service workshops, namely AHASS SPS Motor Palembang. The supply of spare parts that must always be there when customers need them is the main attraction for every workshop. But in reality, there is no special inventory management technique that can help partman to order spare parts in the following month. Therefore, this study will create a web-based spare part inventory information system that can control the amount of spare part stock and help predict the need for spare parts every month with the forecasting technique used, namely the Weighted Moving Average method. The results given from this system are suggestions of spare part stock forecast results with a user-friendly web-based display so that it can improve partman performance, maintain credibility and optimize workshop cashflow

The implementation of discrete-event simulation and demand forecasting using Temporal Fusion Transformers to validate spare parts inventory policy for petrochemicals industry

One of the important strategic factors of petrochemicals plant maintenance is the spare parts inventory policy, It is significant for the efficiency, reliability, and productivity of the petrochemicals industry. An unsuitable spare part inventory policy will lead to a loss in long engineering machinery downtime due to a shortage of spare parts. To implement the spare parts inventory policy which is able to fulfill the future demand for spare parts, calculation by various statistical theories and working processes is used to custom the spare parts inventory policy. However, to validate whether or not the customized spare parts inventory policy is suitable, the discrete-event simulation library SimPy is used to mimic the actual spare parts inventory system. It must be involved in the performance evaluation process of the customized spare parts inventory policy. The inventory simulation model consists of many events depending on the supply chain system. The crucial event which is the most complex for the simulation of spare parts inventory is the demand event. This work applies the demand forecasting technique to the simulation by using deep learning with a prebuilt architecture model called Temporal Fusion Transformers (TFT). The averaged MAE of the point predictions from a global model is 0.4874+/- 6.7744 on the validation dataset and 0.6424+/-3.4963 on the test dataset. Our method predicts a quantile forecast of the future demand which is able to handle the stochastic nature of the spare parts demand in the petrochemicals industry and the result from the simulation outcome is more accurate and close to the outcome from an actual inventory system. The information from the analysis of the simulation outcome is used by the inventory management team to make decisions about the custom inventory policy before deploying it to the actual system.

The implementation of discrete-event simulation and demand forecasting using Temporal Fusion Transformers to validate spare parts inventory policy for petrochemicals industry

One of the important strategic factors of petrochemicals plant maintenance is the spare parts inventory policy, It is significant for the efficiency, reliability, and productivity of the petrochemicals industry. An unsuitable spare part inventory policy will lead to a loss in long engineering machinery downtime due to a shortage of spare parts. To implement the spare parts inventory policy which is able to fulfill the future demand for spare parts, calculation by various statistical theories and working processes is used to custom the spare parts inventory policy. However, to validate whether or not the customized spare parts inventory policy is suitable, the discrete-event simulation library SimPy is used to mimic the actual spare parts inventory system. It must be involved in the performance evaluation process of the customized spare parts inventory policy. The inventory simulation model consists of many events depending on the supply chain system. The crucial event which is the most complex for the simulation of spare parts inventory is the demand event. This work applies the demand forecasting technique to the simulation by using deep learning with a prebuilt architecture model called Temporal Fusion Transformers (TFT). The averaged MAE of the point predictions from a global model is 0.4874+/- 6.7744 on the validation dataset and 0.6424+/-3.4963 on the test dataset. Our method predicts a quantile forecast of the future demand which is able to handle the stochastic nature of the spare parts demand in the petrochemicals industry and the result from the simulation outcome is more accurate and close to the outcome from an actual inventory system. The information from the analysis of the simulation outcome is used by the inventory management team to make decisions about the custom inventory policy before deploying it to the actual system.

An IoT-based maintenance framework for irrigation and drainage water management system at regional scale

The management of the geographically distributed assets of irrigation and drainage water grids compels a challenging alignment of operations like maintenance plans, resource allocation, and spare parts inventory management systems. We propose an IoT-based maintenance framework to optimize reliability, management costs, and logistics and increase the resilience and sustainability of the irrigation and water management network. The proposed framework provides an interface between the centralized inventory system and the maintenance and logistics operations carried out throughout the network. Information exchange is supported by bar code and RFID technologies integrated within the IT system of the company. A Maintenance Management Software (MMS) able to store assets' physical hierarchy data and inventory data is the supporting tool that fuels the framework. The framework is applied to a regional drainage water system in the Emilia-Romagna region (Italy), improving the visibility of the infrastructural network and enabling tailored and data-driven preventive maintenance plans. The combination of the components' availability, logistics and maintenance costs, failure trend, and impact on the overall network contribute to establishing new methods for assessing the threshold of service level (i.e., reliability) to guarantee for each material.

Expediting in Two-Echelon Spare Parts Inventory Systems

Problem definition: We consider dual sourcing in a distribution network for spare parts consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types of capital goods. The repair shop at the central warehouse has two repair options for each repairable part: a regular repair option and an expedited repair option. Irrespective of the repair option, each repairable part uses a certain resource for its repair. In the design of these inventory systems, companies need to decide on stocking levels and expedite thresholds such that total stock investments are minimized while satisfying asset availability and expediting constraints. Academic/practical relevance: Although most companies have the possibility to expedite the repair of parts in short supply, no contributions have been made that incorporate such dynamic expediting policies in repairable investment decisions. Anticipating expediting decisions that will be made later leads to substantial reductions in repairable investments. Methodology: We use queueing theory to determine the performance of the central warehouse and subsequently find the performance of all local warehouses using binomial disaggregation. For the optimization problem, we develop a greedy heuristic and a decomposition and column generation based algorithm. Results: Both solution approaches perform very well with average optimality gaps of 2.38 and 0.27%, respectively, across a large test bed of industrial size. The possibility to expedite the repair of failed parts is effective in reducing stock investments with average reductions of 7.94% and even reductions up to 19.61% relative to the state of the art. Managerial implications: Based on a case study at Netherlands Railways, we show how managers can significantly reduce the investment in repairable spare parts when dynamic repair policies are leveraged to prioritize repair of parts whose inventory is critically low.

How to Change a Running System—Controlling the Transition to Optimized Spare Parts Inventory Policies

Inventory optimization approaches typically optimize steady‐state performance, but do not consider the transition of an initial state to the optimized state. In this study, we address this transition. Our research is motivated by a company that implemented an improved inventory policy for its spare parts division. The improved policy suggested new base stock levels for the majority of the parts. For parts with increased base stock levels, inventory increases were realized after the part lead times, but for low‐demand parts with decreased base stock levels, inventory reductions were slow. As a result, inventory cost increased over the first months after the new inventory policy had been introduced and exceeded the inventory budget substantially. To avoid such undesirable effects, base stock level changes must be phased in. We consider a multi‐item spare parts inventory system, initially operating under an item approach inventory policy that achieves identical fill rates for all parts. Our approach addresses the transition to a superior system approach inventory policy that maximizes the system fill rate. We model the inventory transition as a finite‐horizon optimization problem and apply column generation and a marginal analysis heuristic to determine transient base stock levels for all parts. Using data from the company that motivated our research, we illustrate how the transition can be controlled to quickly improve fill rates without exceeding the initial inventory budget.

Inventory Cost Minimization of Spare Parts in Aviation Industry

Due to high competition in the aviation industry, cost reduction comes into prominence and becomes more critical each day. An efficient maintenance and spare part inventory management system that operates based on a reliable demand forecasting mechanism can significantly reduce operation costs. However, the conventional forecasting methods in the literature are insufficient to estimate the quantity and occurrence times of the non-smooth demand. When cost minimization is directly targeted, these traditional forecasting methods often give misleading results. This study aims to provide an inventory cost management framework for irregular demand in the aviation industry. It compares recent non-smooth demand forecasting techniques with conventional ones by employing cost-based performance measures to minimize cost. The research with 100 different stock keeping units from the inventory of a commercial airline revealed that non-traditional methods are still in their development phases and need further improvements.

Rationing policies in a spare parts inventory system with customers differentiation

We consider a spare parts inventory system controlled by a base-stock policy. We assume several demand classes, where the demands of each class follow a compound Poisson process, and a stochastic replenishment lead-time common to all classes. For this inventory system, we investigate two rationing policies that can be employed when the inventory level is critically low. The first policy is the reservation policy, where a part of the stock is reserved for high priority demand classes, whereas the second one is new, which we call a threshold policy, and accepts demands of a given class until the pipeline stock caused by the particular class (plus the amount of the incoming order) reaches a critical level. On average, for 8% (12%) of all instances, the threshold policy performs better than the reservation policy under the full (partial) lost sales case and when the objective is to minimise the average on-hand inventory subject to fill rate service requirements. Furthermore, in many cases where the reservation policy outperforms the threshold policy, the gap between the two policies is small under both service requirements and total cost minimisation criteria. This suggests that a trade-off between ‘fairness’ and ‘cost’ is required, and that when the performance of two policies are similar, the threshold policy is generally favoured.

Research on Consumable Spare Parts Inventory Management System Based on Forecasting Method

Based on the analysis of actual consumption data of a large number of consumable spare parts and the application effect of the basic random inventory model, the current statistics of equipment spare parts consumption data in China, four forecasting methods are selected to obtain the forecasted demand and the standard deviation of the corresponding forecast error in each cycle and then find the smallest forecast error standard deviation and the corresponding forecast demand quantity. Finally, the forecast demand and the standard error of the forecast error are used to replace the corresponding values in the basic stochastic inventory model to obtain the demand for spare parts in the next cycle, which provides a basis for the preparation of spare parts procurement plans. A large number of application examples show that this method is more simple and practical, and the spare parts inventory level can be reduced by a large proportion.