An Adaptive Spare Parts Demand Forecasting Method Based on Degradation Modeling

A joint preventive maintenance and spare parts provisioning strategy is suggested for a failure prone system. Replacements are carried-out at failure, if spare parts are available, and at spare parts replenishment delivery instants. Spare parts are provisioned according to an (s, Q) control policy. The ordering parameters, and preventive maintenance interval are derived from a mathematical model which aims at maximizing the system's availability under a budget constraint. The model takes into account the system lifetime distribution, the preventive and corrective maintenance costs and durations, as well as the total spare parts inventory management cost. Unlike classical inventory management models, the s-expected total cost is derived using the system lifetime distribution. Because each demand for spare parts is triggered by failure, the probabilities of shortage, and surplus will be evaluated based on the system failure distribution rather than the demand distribution during the lead-time. Numerical results have been obtained for an illustrative example.

Spare part demand forecasting for consumer goods using installed base information

The successive removal of units from a group of systems during phasing out leads to a variable requirement for stock level of spare parts. Repairable units from phased out systems can be reused for the remaining functional systems in the group. Hence, the stock level of spare parts increases and the demand for spare parts decreases. This can lead to excessive stocking of spare parts and high maintenance costs for spare parts. This paper proposes a methodology to determine the optimum time to stop repair of repairable units to minimize the maintenance cost. It uses a Poisson Distribution based on NHPP (Non-Homogenous Poisson Process) to predict the available number of units, while fulfilling the demand for spare parts for the remaining systems. A concept, called Minimal Margin is introduced to formulate the problem and nonlinear programming is proposed to obtain the optimum solution. Finally, a numerical example is presented to demonstrate the approach.

The focus of this study is on developing a decision support system (DSS) in order to forecast spare parts demand for a company producing high technology products in Turkey. The company is one of the world’s leading original design manufacturers in the field of consumer electronics and white goods. Accurate forecasts of customer demand for preliminary products and spare parts play an important role in order to reduce costs and increase customer satisfaction. Currently, the company’s forecasting system is based on personnel experience and a statistical approach, which lacks the ability of capturing demand data behaviour. The approach followed results in an increased forecasting error, thus increases production costs, results in lack of spare parts, and decreases customer satisfaction. The aim of this project is to develop a DSS to minimize the forecasting error; therefore help the company develop a policy for optimizing the stock levels kept, reducing costs and increasing customer satisfaction. In order to understand the behaviour of customer demand of spare parts, the company’s television products are chosen for the pilot study, since these products are highly influenced by rapid technological changes and changes in the product models. The spare parts are classified into different groups, using ABC analysis, in order to develop a forecasting model for each group. In the solution methodology part, three different statistical methodologies for the forecasting process were respectively studied; Winter’s, Double Exponential Smoothing and Moving Average Methods. Winter’s Method is used for the data which exhibit trend and seasonality, Double Exponential Smoothing is used for the data which exhibit trend and Moving Average Method is used for the data which exhibit stationary behaviour. In the DSS developed, the above-mentioned methodologies are coded, using Excel VBA programming language, historical data’s behaviour is analysed and forecasts for future spare parts demand are made. The forecasting results are compared based on the minimum error (PAE), to decide upon which is the most appropriate forecasting methodology to use according to the specific spare parts past data behaviour.

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.

ABSTRACTRedundancy allocation and spare parts logistics are among the primary strategies to achieve high operational availability of capital equipment. These methods are often implemented independently, with limited attention to their combined effect on system's lifetime cost. This paper proposes an integrated redundancy‐spares‐repair allocation model to maximize the availability and minimize the cost of electric vehicle (EV) supply equipment. The decision variables include the number of redundant chargers, the spare parts inventory, and the regular and the emergency repair capacity. When the spares inventory is depleted, failed EV chargers are immediately repaired through an expedited process to reduce downtime. Erlang‐B and Erlang‐C queueing models are used to characterize random parts demand and repair turnaround time. The levelized cost of EV chargers is used to compare the return on investment for redundant units versus spares stocking. The proposed model is demonstrated using Tesla's supercharger fleet, which consists of 17,004 DC fast chargers. The result shows that spare parts stocking, combined with regular repair, is a cost‐effective approach to achieving high availability of EV supply equipment. However, both redundancy and emergency repairs are essential when extremely high availability is required. Our analysis also reveals that inventory capital and holding cost have a limited impact on the levelized cost of the EV chargers.

By extracting the historical usage data of equipment spare parts in the equipment management platform, using Holt method to predict the demand of spare parts, and using MPG method, we can develop reasonable ordering strategy for the demand prediction of spare parts, and provide guarantee for the development of scientific spare parts reserve optimization strategy, and provide scientific decision basis for the improvement of equipment maintenance and guarantee effectiveness.

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.

In today's highly competitive manufacturing landscape, achieving Lean Manufacturing Excellence is critical for maximizing efficiency, minimizing costs, and enhancing customer satisfaction.This study examines the strategic integration of Total Productive Maintenance (TPM) and Value Stream Mapping (VSM) to optimize spare parts production.TPM enhances equipment reliability through proactive maintenance, while VSM streamlines material and information flows to eliminate waste and improve process efficiency.Conducted in the machining process of an Egyptian spare parts manufacturing company, this research develops a robust framework leveraging advanced analytical and improvement methodologies.A data-driven case study quantifies the impact of TPM-VSM synergy, demonstrating significant performance gains.Over three months, implementation increased Total Effective Equipment Performance (TEEP) from 58% to 68%, improved Overall Equipment Effectiveness (OEE) from 65% to 76%, and elevated value-added process efficiency from 37% to 54%.These results validate the effectiveness of integrating Lean methodologies with proactive maintenance strategies to drive productivity, minimize waste, and sustain operational excellence.This study provides a practical framework for industry professionals seeking to foster continuous improvement, data-driven decision-making, and Lean-driven optimization in spare parts manufacturing.

Accurate spare parts demand planning and effective distribution planning is essential for providers of after-sales services in the machine and plant engineering industry to ensure high spare parts availability for maintenance and failure orders (callouts) at a reasonable cost.Low spare parts availability is primarily the result of high uncertainty in spare parts demand, leading to misallocation of parts within aftersales service networks.The lack of spare parts availability causes equipment downtime, resulting in customer dissatisfaction and possible penalty costs for after-sales service providers, if response times are contractually fixed.This paper proposes an approach and planning methods for integrating real-time status information about equipment utilization and service conditions to determine optimal spare parts stocking strategies.For this purpose, spare parts stocking strategies and ordering policies for application in after-sales service networks are analyzed.Furthermore, a binary linear optimization model is developed for the assignment of stocking strategies to spare parts based on real-time demand information of the equipment to be serviced.This method uses data provided by an internationally operating elevator company.

As is known to all, the life cycle of perishable hi-tech products(PHTP) is so short, and the update rate is so fast that market demand of older products is influenced, changed dynamically continuously. Generally, when the product goes wrong and better products continuously appear, customers will replace the failure components, but more and more of them tend to buy updated products now as time goes by, which influences the spare parts demand and brings more difficulties to forecast the demand. Under this background, we established a Spare Parts Forecasting Model Based on the Installed Base Information (IBI-SPFM) in this paper, which considered the influence of customer behavior on spare parts demand. Moreover, it can be showed that our IBI-SPFM not only can work out a better forecasting value, but can provide us the installed base of products and build-in components with different age categories both by theoretical analysis and numerical case. Our method is of much significance when companies are confronted with relative decision-making problems.

Di situasi saat ini dimana era pandemic operasional perusahaan berjalan dengan kekuatan organisasi dengan para pelaku manajemen yang berpikir keras bagaimana mengelola permintaan serta kebutuhan dan memasok kebutuhan kepada para pelanggannya. Penulisan artikel ini membahas tentang pengelolaan manajemen perusahaan dengan menggunakan metode peramalan permintaan dalam memenuhi permintaan para intermiten produk suku cadang pada sebuah perusahaan otomotif, dengan sebuah penelitian permintaan intermiten perusahaan yang menunjukkan pola fluktuatif, adanya permintaan dalam jangka waktu tertentu, dan penelitian ini menguji 2 metode peramalan dalam memprediksi permintaan suku cadang yaitu metode Metode ABC, Simple Exponential Smoothing (SES) dan Moving Average Exponential Smoothing dengan menggunakan data permintaan 2017-2020 untuk meramalkan permintaan pada tahun 2021. Kinerja metode peramalan ditentukan berdasarkan mean squared error, dan hasil penelitian ini menunjukkan bahwa penggunaan metode Simple Exponential Smoothing memiliki performansi yang paling baik. Tujuan utama dari penelitian ini adalah untuk membandingkan dan memilih metode peramalan terbaik untuk memprediksi kebutuhan dan permintaan kebutuhan suku cadang perusahaan. In the current situation where the pandemic era has caused many companies to experience crises, the company's operational activities are carried out with organizational strength that makes management actors think hard in managing the needs and supplying the needs of their customers. This study discusses the management of the company by using the demand forecasting method in meeting the demand for intermittent spare parts products in an automotive company. In this study, the company's intermittent demand trend shows a fluctuating pattern with demand within a certain period of time. This study examines 2 forecasting methods in predicting the demand for spare parts, namely ABC analysis, the Simple Exponential Smoothing (SES) method and the Moving Average Exponential Smoothing using 2017-2020 demand data to forecast demand in 2021. The performance of the forecasting method is determined based on the mean squared error, and the research results This shows that the use of the Simple Exponential Smoothing method has the best performance. The main purpose of this study is to compare and choose the best forecasting method to predict the needs and demands of the company's spare parts needs

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%.

This article explores measures to leverage IT technologies to expand the potential customer base of MASHINERY LLC, a Novocherkassk, Rostov Region, company engaged in the wholesale distribution of spare parts for mining and construction machinery and equipment manufactured in China to the Donskoy Kamen Group of Companies. Given the consistently high demand for spare parts, the possibility of expanding the customer base through the use of IT technologies is justified. The article examines the primary platforms for selling special-purpose machinery parts, including specialized classifieds platforms Avito and Drom, spare parts aggregators ZZap, and B2B platforms. A tactical action algorithm is developed, which includes a target audience and competitor analysis at stage 1. Stage 2 explores the possibility of creating digital channels in two areas: developing a specialized catalog website and registering on B2B platforms and marketplaces. Two website development options are proposed: an official company website and a single-page landing page for quickly collecting orders (Megagroup). A comparison of website development costs with SEO optimization and support for the VKontakte social network and landing page was conducted. The next step involved examining the potential of Yandex.Direct contextual advertising to reach the target audience. Projected profits and the cost effectiveness of advertising campaigns were calculated.

Spare parts in the aviation industry are repaired after removal from the aircraft and led back to stock, forming a closed-loop supply chain. Typically the demand of spare parts is assumed to follow a homogeneous Poisson distribution. The repair times in the shop with limited capacity are taken to be exponentially distributed. This paper investigates the effects of non-homogeneous demand and the resulting queue in the repair shop due to limited capacity in periods of high demand.