Spare Parts Demand Research Articles

Dynamic Prediction Method for Valuable Spare Parts Demand in Weaponry Equipment Based on Data Perception

Missile is an important weapon system of the army. The spare parts of missile equipment are significant effect on military operations. In order to improve the mission completion rate of missile equipment in wartime, thispaper introduces data sensing method to forecast the demand of valuablespare parts of missile equipment dynamically. Firstly, the information related to valuable spare parts of missile equipment was obtained by data sensing, and the sample size was determined by Bernoulli uniform sampling probability. Secondly, according to the data quality of multisource and multi-modal, the data requirement for dynamic demand prediction of valuable spare parts of missile equipment was obtained. Finally, according to the characteristics of the spare parts, the life of the spare parts was predicted, realizing the dynamic prediction of the demand for valuable spare parts of missile equipment. The results show that the demand of valuable spare parts of missile equipment can be predicted dynamically by using this method, the accuracy is higher than 95%, and the real-time performance is more excellent.

A data-driven methodology for the periodic review of spare parts supply chain configurations

Configuring supply chains (SCs) is critical to spare parts retailers’ success, entailing two key aspects: stock deployment into distribution centres (DCs) (i.e. inventory centralisation or decentralisation) and stock supply in each DC (how many spare parts to supply and how often). Given the unpredictability of spare parts demand, stock deployment and supply policies should be regularly reviewed, adapting to fluctuations in customer needs. A viable way to do this is to adopt a multi-criteria ABC criticality classification. However, the multi-criteria ABC criticality classification has often been used to plan stock supply policies in a single DC, but only once to plan spare parts deployment. Nevertheless, the available literature methodology presents major limitations, being not applicable in real companies. Therefore, this paper provides a novel methodology, called SP-LACE, which first reviews the configuration of spare parts SCs based on a multi-criteria criticality classification. Then, allows, for the first time, to evaluate the economic benefits of the reviewed SC configuration. SP-LACE was tested on two case studies and compared with the literature methodology. The results indicate that it provides economic benefits (in terms of total SC cost), overcoming the limitations of the literature methodology and ensuring high service levels.

Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet

Forecasting the demand of spare parts of vehicles in bus fleets is a vital issue. Vehicles must operate effectively and must have a high availability rate in the fleet. In maintenance operations, faulty parts or parts that complete their lifetime must be replaced with a new one. Spare parts needed must be in inventories with the required amount on time. In this sector, there are thousands of spare parts to manage. The maintenance and repair department must operate effectively. In order to accomplish this, accurate forecast of spare parts is required. In this study, demand forecasting was carried out with regression-based methods (multivariate linear regression, multivariate nonlinear regression, Gaussian process regression, additive regression, regression by discretion, support vector regression), rule-based methods (decision table, M5Rule), tree-based methods (random forest, M5P, Random tree, REPTree) and artificial neural networks. The forecasting model developed in this study includes critical variables such as the number of vehicles in the fleet, the number of breakdowns that cause parts to change, the number of periodic maintenance, mean time between failure and demand quantity in previous years. The application was carried out with real data of eight (2013–2020) years. 2013–2019 data was used for training and 2020 data was used for testing. In forecasts, support vector regression among regression-based methods, decision table among rule-based methods, M5P among tree-based methods gave the best results. It has been observed that the artificial neural network produced more accurate forecasts than all other methods. Artificial neural network forecasts give the highest forecast accuracy rate and the least deviation.

Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks

The proportion of the inventory range associated with spare parts is often considered in the industrial context. Therefore, even minor improvements in forecasting the demand for spare parts can lead to substantial cost savings. Despite notable research efforts, demand forecasting remains challenging, especially in areas with irregular demand patterns, such as military logistics. Thus, an advanced model for accurately forecasting this demand was developed in this study. The K-X tank is one of the Republic of Korea Army’s third generation main battle tanks. Data about the spare part consumption of 1,053,422 transactional data points stored in a military logistics management system were obtained. Demand forecasting classification models were developed to exploit machine learning, stacked generalization, and time series as baseline methods. Additionally, various stacked generalizations were established in spare part demand forecasting. The results demonstrated that a suitable selection of methods could help enhance the performance of the forecasting models in this domain.

Research on forecasting method of mechanical equipment spare parts demand based on LS-SVM

In view of the wide variety and quantity of mechanical equipment spare parts, the increasing difficulty of spare parts management, and how to accurately predict the demand for spare parts, the least squares support vector machine (LS-SVM) regression algorithm is proposed to predict the demand for mechanical equipment spare parts. Based on the analysis of the basic principle of least squares support vector machine, a prediction model of mechanical equipment spare parts demand is established. RBF kernel function is selected. LS-SVM is used to study the training samples, train its grid structure parameters, determine the optimal parameters through cross validation and grid search, and use the trained LS-SVM to predict the mechanical equipment spare parts demand, and carry out numerical simulation, The prediction methods such as first-order exponential smoothing, ARMA method and BP neural network are used for comparison. The results show that LS-SVM performs well in the demand forecast of mechanical equipment spare parts.

Mathematical support and software systems for spare parts management of grain harvesting machinery

Agricultural production is characterized by the seasonality of technological operations, the ability to carry out which in a strictly set time depends on many factors of the external environment and the reliability of agricultural machinery. The harvest of grain and other crops by combines occupies a special place among field works. Operative carrying out of these works without down time of the grain-harvesting techniques provides the minimum losses of the agricultural production. It is possible to minimize downtime of harvesters due to technical reasons if the enterprise has at its warehouse a seasonal reserve of spare parts that are in great demand during harvesting works. The methods of reserve calculation should consider not only the reliability of some harvester parts, but the cost damage of harvester downtime and extra costs of spare parts purchase and storage. With such a large number of external factors, traditional methods are difficult to calculate. With the development of computer technology, the methods of evolutionary calculations have been widely used, in particular, genetic algorithms that allow creating self-adjusting models capable of analysing the examined indicators for the past years, reacting to the changes of current external conditions, and making short-term forecasts of the values of optimised variables. The purpose of this research was to develop a genetic algorithm and software that will enable us to meet the seasonal demand for spare parts from combine harvesters. The software allowed us to identify 66 of the most in-demand items out of 2800 warehouse items that should be reserved for the season of harvesting crops. The efficiency of the proposed solutions is proved by the reduction of the downtime of combines due to technical reasons by 37% and the increase of their shift productivity by 11.4%; at the same time, the combine reliability index, the operational readiness factor, increases by 4.38%.

Procurement Issues in Indonesian Maintenance, Repair, and Overhaul (MRO)

Aviation Industry has grown tremendously in Indonesia over the last two-decade. Deregulation in early 2000 resulted in spectacular growth in the number of aircraft and passengers (Fahriza & Willey, 2018). This phenomenon caused Indonesia to become the most prominent airline market in Southeast Asia. Figure 1.1 shows in 2018, there are 1559 aircraft were registered in Indonesia, and the number slightly changed during the pandemic to 1497 aircraft remain registered. This industry has just recovered, and Indonesian Airlines will continue receiving orders for new aircraft, increasing the numerous aircraft in Indonesia. This fact is supported by the statement of the Lion Air General Director, Edward Sirait, that Lion Air, Indonesia's largest The aforesaid papers mainly address the problem either from an inventory point of view based on the past spare parts usage to forecast the future demand or from a maintenance point of view to find an optimal order quantity and PM interval considering the correlation between flying hours and failures. To the author's best knowledge, limited research handles failure-based procurement inventory management which is very common in practice. On the one hand, when demand is triggered by failures, the demand forecast result based on past consumption may not be accurate. For example, past low demand in many periods may indicate significant aircraft parts aging and therefore high impending demands, but the traditional replenishment system will scale back replenishment which is counter to the actual requirement. On the other hand, PM inventory management is different from failure-based inventory management. As the spare parts demand is uncertain, and some- times the part delivery time may be very long, it could lead to significant loss if a critical part fails but there is no spare to replace it. (Gu et al., 2015) Keywords: MRO, Procurement, Indonesia

SIMULASI PERBAIKAN LEAD TIME PROSES PRODUK BUMPER MENGGUNAKAN VALUE STREAM MAPPING DAN PROMODEL DI PT. SUZUKI INDOMOBIL MOTOR

The development of the industry in this globalization era is very rapid, causing complex problems. The era of globalization requires all aspects of life to adapt to the development of more advanced times with the sophistication of its technology. One of the ways to increase excellence is to minimize waste in the production line. Beginning by describing the actual condition of the production line and trying to eliminate the waste in order to realize a lean value stream. The method used is a value stream mapping that is useful for seeing the processing time detail so that it knows the value added and non value added as well and Promodel software to simulate the proposed improvement given to improve the process lead time. Based on the current stream map that has been made known waste activities that occur is over process, inventory, and waiting. The front bumper assy panel of the Model Y9J at PT. Suzuki Indomobil Motor is the product with the highest demand for spare parts, where the lead time for stock is 21.2 days, and the lead time for processing is 1.14 days so it is necessary to make improvements using a Value stream mapping simulation, so stock lead time to 6.1 days and process lead time to 0.6 day.

Data Classification and Demand Prediction Methods Based on Semi-Supervised Agricultural Machinery Spare Parts Data

Because of the continuous improvement of technology, mechanization has emerged in various fields. Due to the different suitable seasons for the growth of agricultural plants, agricultural mechanization faces problems different from other industries. That is, agricultural machinery and equipment may be used frequently for a period of time, or may be idle for a long time. This leads to the aging of equipment no longer becoming regular, the maintenance time of spare parts is not fixed, the number of spare parts stored in the spare parts warehouse cannot be too large to occupy funds, and the number cannot be too small to meet the maintenance needs, so the prediction of agricultural machinery spare parts has become particularly important. Due to the lack of information, the difficulty of labeling, and the imbalance of positive and negative sample classification, this paper used a semi-supervised learning algorithm to solve the problem of agricultural machinery spare parts data classification. In order to forecast the demand for spare parts of agricultural machinery, this paper compared the IPSO-BP neural network algorithm and BP neural network algorithm. It was found that the IPSO-BP neural network was used to forecast the demand for spare parts of agricultural machinery, and the error between the predicted value and the actual value was small and met the accuracy requirements.

Uçak Komponentleri İçin Talep Tahmini Ve Stok Kontrolüne İlişkin Bir Uygulama

Today, the management of the inventory used in the aircraft maintenance-repair industry is an important issue. Spare parts inventory, in other words component inventory, constitutes the main capital resource of this type of companies. For this reason, it is an important and sensitive issue for organizations engaged in aircraft maintenance to effectively manage their spare parts stock. Effective and rational management of spare parts inventory will provide companies with significant cost advantages. While trying to increase service levels, companies aim to keep their inventory costs at minimum levels. In order to effectively manage the spare parts inventory, first of all, the demand forecast for the future must be made correctly. For this, techniques suitable for the part structure should be used. The next step after forecasting is to keep stock at a sufficient level of confidence to avoid running out of stock in the future. Because of this, demands are formulated by fitting distributions. In this study, component data of a local company that provides maintenance and spare parts services to the aircraft of airline companies in the aviation maintenance and repair sector was used. Demand patterns related to the data set were examined and discrete forecasting methods were applied to them. Afterwards, comparisons were made by using various distributions to determine the amount of spares that should be kept in stock. The results were interpreted and evaluated. It is assessed that this study will shed light on and benefit organizations operating in the aviation sector and other sectors in terms of spare part stock management and demand forecasting.

Multiperiod integrated spare parts and tour planning for on-site maintenance activities with stochastic repair requests

Motivated by the problem facing many home-attended maintenance service providers, this paper considers the joint optimization problem of spare parts planning and the scheduling of a single technician to visit geographically distributed customers for on-site maintenance activities. We present and formulate the problem using stochastic mixed-integer programs, encompassing many important real-life characteristics, such as stochastic spare parts demand, stochastic customer requests, and return visits. We also propose an anticipatory solution method, which uses a look-ahead procedure to integrate stochastic information into the planning process and compare this with two myopic planning approaches. With a detailed numerical study, we show that anticipation can lead to cost reductions and service level improvements, especially when customers are sparely distributed. Furthermore, our results reveal that the repair kit and technician tour integrated planning helps to substantially reduce penalty costs for late service. We also provide insights into the value of having perfect information about spare part demand and future request locations.

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 Improved Stacking Model for Equipment Spare Parts Demand Forecasting Based on Scenario Analysis

The purpose of spare parts management is to maximize the system’s availability and minimize the economic costs. The problem of cost availability trade-off leads to the problem of spare parts demand prediction. Accurate and reasonable spare parts demand forecasting can realize the balance between cost and availability. So, this paper focuses on spare parts management during the equipment normal operation phase and tries to forecast the demand of spare parts in a specific inspection and replacement cycle. Firstly, the equipment operation and support scenarios are analyzed to obtain the supportability data related to spare parts requirements. Then, drawing on the idea of ensemble learning, a new feature selection method has been designed, which can overcome the limitations of a single feature selection method. In addition, an improved stacking model is proposed to predict the demand for spare parts. In the traditional stacking model, there are two levels of learning, base-learning, and meta-learning, in which the outputs of base learners are taken as the input of the meta learner. However, the proposed model brings the initial feature together with the output of the base learner layer as the input of the meta learner layer. And experiments have shown that the performance of the improved stacking model is better than the base learners and the traditional stacking model on the same data set.

Arena Simulation of Spare Parts Inventory Management: A Case of A Telecom Managed Service Provider

Telecommunications and utilities companies, among other large service organisations, confront a slew of decision-making challenges, many of which are connected to the management of a variety of different resources. The management of spare parts and inventories is one of the most difficult resource management concerns that an organisation has to deal with on a regular basis. Their prompt availability may have a significant influence on the overall quality of the service and the level of client satisfaction. The purpose of the study was to assess the Spare Parts Inventory Management of XYZ company operating within the Ghanaian telecommunication ecosystem. Data on demand and inventory stock level was collected from the procurement department of XYZ company. The study adopted the use of the Arena Simulation model to analyze data collected from the XYZ company. The results of the analysis indicated that the procurement department is unable to meet the spare parts demand of the Passive department where the average order received from the department in a day is 6.08. Based on the findings of the study, it is recommended that the procurement department put in measure to receive and process orders in time. The reason for the delay in processing orders could be the less poor inventory management practices of the company. The study recommends that systems be adopted to track the inventory level of the company. Due to the stochastic nature of the demand from the passive department, the company should be proactive in the management of the demand by keeping the right stock level in order to meet the demand of the department in order to reduce the long down time. Key words: Spare parts, inventory, Arena Simulation, telecommunication DOI: 10.7176/EJBM/14-12-10 Publication date: June 30 th 2022

Optimasi Desain gating system dan riser pada Pengecoran Produk Blok Silinder Modifikasi untuk Motor Bensin 4 Langkah

The growth of the automotive industry has made the market demand for motor vehicle spare parts soar, especially motorcycles, including the demand for cylinder blocks. Procedure of this research was carried out to analyze the product of 4 stroke motor cylinder block castings, which include; manufacture of casting product designs, manufacture of gating systems and riser systems. Results and Discussion based on the results of the shrinkage simulation test that has been carried out through the type of sand casting casting with the casting material, namely FCD-600-3 cast iron (1290oC) and the impression material used is silica sand (20oC) with a gravity casting type of casting, it can be concluded that the results of design testing 1 (one) obtained a filling velocity of 1.111 m/s, with a filling time of 3.8 seconds and a required solidification time of 6 minutes. In design 1 (one) there is a shrinkage defect due to shrinkage located on the sprue and riser of 0.336%. While the test results from design 2 (two) obtained a filling velocity of 1.09 m/s, with a filling time of 3.9 seconds and a required solidification time of 5 minutes. In design 2 (two) there is a shrinkage defect due to shrinkage located on the riser and in the cast product of 0.404%. The conclusion of the test results on both designs, it was found that design 1 (one) is the most optimal design for cylinder block casting planning.

A Comparative Study of Demand Forecasting for Aftermarket Parts in Heavy Equipment Industry (PT XYZ Case Study)

The global economic crisis has reached the world today, forcing many customers to become more cost aware in their search for better quality and service, and forcing corporate organizations to discover more effective and efficient ways to compete among them. The main objective of this research is to choose the best forecasting method to predict the demandfor spare parts at PT. XYZ highly fluctuating, and to avoid or minimize stockouts. The demand for high-priced spare partsand capital goods is considered discontinuous if it is random and contributes a large part of the inventory value. Fluctuating demand for goods will be difficult to predict, and inaccurate estimates can cause huge losses for the company due to obsolescence of spare parts or unfulfilled demand for spare parts. Running a successful company operation today requires organizational strength to supply the needs of its customers. This study discusses the appropriate demand forecasting method for the fluctuation demand for spare parts products at an Indonesian companynamed PT. XYZ. This study compares four forecasting methods to predict the demand for spare parts at PT. XYZ is the ABCAnalysis, the Moving Average, the Simple Exponential Smoothing (SES) and Exponential Smoothing (ES) with TrendAdjustment. This study uses demand data for 2017-2020 to forecast demand in 2021 and uses the optimum alpha value of0.4065 for SES which is obtained by calculating using MS Excel Solver and uses alpha value of 0.5 and beta value 0.3 for ES with Trend Adjustment. The performance of this forecasting method is determined based on the smallest mean absolute percentage error and the level of forecasting accuracy (tracking signal) which is close to zero, and the results of this study indicate that the use of the Exponential Smoothing with trend adjustment method has the best performance compared to the other three methods.
 

An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control

Spare parts inventory management is complex due to the combined impact of intermittent and variable demand patterns. It becomes even more challenging if the spare parts demand distribution is highly complex due to strong interdependent demand intermittency and extremely irregular demand. The research literature proposes many analytical methods for forecasting spare parts demand. But, due to their limited flexibility in modeling complex demand patterns, existing forecasting methods may not produce satisfactory results for a spare parts portfolio displaying extremely complex demand patterns. This study proposes a novel nonparametric Bayesian forecasting approach with its roots in the empirical Bayes paradigm. The method is subject to few performance constraints and is highly flexible in dealing with a rich diversity of demand patterns, including extreme demand complexity. We assess the relative performance of this new approach with several prominent methods in the literature using an automotive parts distributor's empirical demand data for 46,272 stock-keeping units. This dataset is representative of typical spare parts portfolios that are characterized by a wide variety and extremely complex demand patterns. The experimental findings show the new Bayesian approach achieves the best overall performance in terms of inventory efficiency and minimal backorders for meeting specified target service levels. This favorable performance reflects the approach's flexibility to accommodate disparate and complex demand patterns, including interdependence of demand intermittency, irregular demand distribution, and even nonstationary demand distribution to some extent, and provide robust solutions.

Spare parts demand forecasting method of modern enterprises based on digital twin model

Accurate forecast of spare parts demand is of great significance for modern enterprises to provide accurate support and improve market competitiveness. In most studies, mathematical laws are used to forecast, without enough consideration of the actual operation of equipment and the fact that the accuracy of spare parts demand forecasting is not high, which cannot adapt to the new characteristics of complex equipment use environment and fierce market competition in modern enterprises. The digital twin model can be used to forecast the demand for spare parts more accurately and guide modern enterprises to carry out accurate support. By analyzing the current spare parts demand of modern enterprises, the paper puts forward the forecasting ideas of spare parts demand based on the digital twin model by using the digital twin model of equipment maintenance management in modern enterprises. In the digital twin model, the theoretical demand forecasting model of spare parts based on life distribution of replaceable units is introduced, and the sensitivity coefficient system of spare parts demand of replaceable units to operation and environment is constructed. The digital twin model is used to feedback train the sensitivity coefficient to obtain the reliable spare parts demand rules. Based on the theoretical demand and sensitivity coefficient of spare parts, the forecasting method of spare parts demand is given, and the spare parts demand forecasting model is established. Through case analysis, the feasibility and accuracy of the forecasting model are verified.

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

Demand forecasting is a difficult field of study for intermittent demands. Spare parts demand structures also have an intermittent demand structure. Therefore, for companies operating in this field, this situation causes various problems (holding cost or cost of lost sale). Intermittent demands are inherently difficult to predict. Demands with a smooth structure provide a more suitable working environment for businesses. Because the more accurately the relevant demand is forecasted, the more smoothly the works that depend on demand forecasting are carried on. In this study, a randomly generated demand series with intermittent demand structure is examined. The estimation difficulty of intermittent demand is illustrated by an estimation made in Matlab. In order to avoid this difficulty, the costs were tried to be minimized by determining the inventory levels. An inventory model is proposed that determines stock levels using intermittent demands and calculates average profit by calculating costs. The related model was solved with Genetic Algorithm in Matlab and the results were recorded.