Spare part demand forecasting for consumer goods using installed base information

Forecasting Spare Part Demand with Installed Base Information: A Review

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.

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.

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.

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.

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.

On a method to improve your service BOMs within spare parts management

Spare parts demands usually occur due to the need to replace spare parts on damaged equipment. Uncertain demands and many types of spare parts require complex management. Therefore, the management of spare parts is very challenging. In this study, we developed a spare part inventory method by considering priorities based on customer class. Customers will be grouped according to the number of demands. The number of requests becomes a fill rate priority. Any request that cannot be fulfilled will have backorder risk. This customer prioritization aims to minimize costs and backorder risks. The difference in customer class aims to minimize total inventory costs. Spare parts will be issued based on the queue discipline which is first in first out. The inventory model is solved by the Lagrange multiplier method. Model verification is carried out in real practice in illustrative examples. The results of the research show that customer priorities that are carried out in the provision of the fulfilment of spare parts can reduce costs and backorder risks.

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

"This paper considers the problem of changing the value of selected characteristics for the operation of a two-level inventory system of maintainable parts depending on the flow of regenerated components in the inventory system. We analyzed the influence of the flow of regenerated components on the value of the coefficient of peripheral storage fill-rate, total costs and the average waiting time inthe queue in peripheral warehouses, at different nominal values of the stocks in central and peripheral warehouses. The demand for spare parts in the peripheral warehouses is dependent on seasonal changes, modeled with an appopriate probability distribution."

This study proposes a spare parts demand prediction model based on ensemble learning. By integrating the advantages of multiple algorithms, the model significantly improves prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 12.1%. Experimental results show that the model exhibits excellent performance in various scenarios, including high-value spare parts, seasonal spare parts, and long-term trend prediction, with a prediction accuracy rate of over 88%. Compared to traditional methods, the model not only has stronger non-linear pattern handling capabilities but also possesses good interpretability and scalability. The research results provide a new solution for the field of spare parts demand prediction and have important practical significance for improving inventory management efficiency.

Reliability and operations: Keys to lumpy aircraft spare parts demands

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.