Spare Parts Inventory Research Articles

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.

Optimum utilization of on-demand manufacturing and laser polishing in existence of supply disruption risk

3D printing has moved from being a rapid prototyping tool to an additive manufacturing method within the last decade. Additive manufacturing can satisfy the need in dire situations where spare parts distribution is an issue but access to a 3D printer is much more likely and rapid than access to original parts. Managing inventories of spare parts can be tackled with more ease thanks to the reduced part types with additive manufacturing. While quality (in terms of reliability) of additively manufactured spare parts in terms of mechanical properties seem to be lower than original parts (particularly due to the inherent staircase appearance and the corresponding stress concentration zones that can lead to premature fatigue failure), use of post-processing subtractive techniques to correct such surface irregularities are found to improve reliability. While each process adds another layer of complexity to the cost minimization problem, demand uncertainty and risk of supply disruption represent the modern global problems faced recently. The problem tackled in this study is the joint optimization of the supply reliability considering the effect of laser polishing parameters and the demand uncertainty. In this problem, a condition of random breakdowns of identical products is considered. Also, the original supplier of machine components is subject to exogenous disruptions, such as strikes, raw material scarcity, or the COVID-19 pandemic. As a result, the optimum control policy with the right cost parameters was shown via numerical experiments originated from mathematical analyses. This optimality can be critical in managing the system in the best possible way, particularly during times of unforeseen circumstances such as pandemics.

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.

Failure Mode and Effect Analysis Implementation for Spare Part Classification and Inventory Management in Automotive Manufacturing (Case Study of Pt. XYZ)

Failure Mode and Effect Analysis Implementation for Spare Part Classification and Inventory Management in Automotive Manufacturing (Case Study of Pt. XYZ)

Predicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model

In this paper, a combined model is proposed to predict spare parts inventory in accordance with equipment characteristics and defect elimination records. Fourier series is employed to process the periodicity of the data, autoregressive moving average (ARMA) is used to deal with the linear autocorrelation of the data, and backpropagation (BP) neural network is used to settle the nonlinearity of the data. The prediction results, comparisons, and error analyses show that the combined model is accurate and meets the practical requirements. The combined model not only fully utilizes the information contained in the data but also provides a reasonable decision basis for the procurement of spare parts, making the inventory in a safe state and saving holding costs.

Usulan Perbaikan Pengendalian Persediaan Spare Part Utama Gondola Menggunakan Metode EOQ dan Min-Max

PT. Pola Gondola Adiperkasa is a company engaged in construction, whose main activities are gondola maintenance and gondola installation. According to an interview conducted with PT. Pola Gondola Adiperkasa, it is known that this company has problems in unstructured inventory. If the gondola spare part inventory is stored for a long time then the problem that occurs is the reduced quality of the goods. This causes the inventory cost to increase. The purpose of this study is to determine the calculation of the Economic Order Quantity (EOQ) method, to know the calculation of the Min-Max method and to propose improvements regarding inventory control. The results of calculations using the Economic Order Quantity (EOQ) method were found to be more efficient in terms of controlling the gondola spare part inventory compared to PT. Pola Gondola Adiperkasa and Min-Max method. The difference between the policies of PT. Pola Gondola Adiperkasa with Economic Order Quantity (EOQ) method is Rp. 384,787, while the difference between the policies of PT. Pola Gondola Adiperkasa with the Min-Max method is Rp.268.931. Researchers propose to PT. Pola Gondola Adiperkasa to use the Economic Order Quantity method.

Decreasing e-waste through reliability enhancement encouraged by performance-based contracting

PurposeIn the last 20 years, e-waste has become a serious issue resulting from an overwhelming amount of electronics consumption. However, there has been limited research on how to decrease such waste in a structured manner. Toward study was to use a simulation methodology to investigate the dynamics of upfront investment in reliability enhancement promoted by performance-based contracting (PBC), based on the number of spare parts and duration of the contract.Design/methodology/approachThe present research first details the relevant mathematical equations and uses game theory to demonstrate the utility for supplier and buyer relationships. Next, the effects of reliability enhancement, spare partsPBC are analyzed using a BlockSim simulation model.FindingsThe results indicate strong relationships among system design cost, reliability, availability and service cost. The authors found that investment in reliability increases system availability while reducing total service costs. Furthermore, increasing the spare parts inventory was determined to have less influence on the readiness of highly reliable systems. The findings support the notion that PBC reduces e-waste by increasing system availability, incentivizing upfront investment in reliability growth.Research limitations/implicationsRecognition of these findings in the context of buyer–supplier relationships will help managers better understand the value of upfront reliability investment, reducing maintenance, repair and overhaul requirements, avoiding the need to plan for extra spare parts and minimizing volume and the resulting e-waste.Practical implicationsThis study also clarifies the uncertainty associated with upfront investment and provides potential incentives for suppliers.Originality/valueThe main contribution of this study is its use of PBC for e-waste reduction, highlighting the effects of upfront investment in reliability enhancement. The authors applied a game theory model to illustrate the relationship between incentives and upfront investment and demonstrate how increased levels of spare parts can be counterproductive to achieving readiness, reducing inventory and consequent e-waste.

Designing Sustainable Business Models to Reduce Spare Part Inventory

Additive manufacturing (AM) has long been recognized as a revolutionary technology. Throughout the last years, the number of applications of AM technologies has been growing, with a particular focus on the medical and aeronautical fields. Only recently have researchers and practitioners started to perceive the potential of AM in industrial maintenance, particularly in spare parts manufacturing. By allowing toolless manufacturing, and large design freedom, AM technologies can be used to produce spare parts “on-demand”, thus contributing to the elimination of costly/unnecessary stock. This notion of stocking 3d files instead of physical parts is often referred to in the literature as “digital warehouse”, and it is highly aligned with the principals of industry 4.0. The expected benefits are obviously focused on reducing costs, however, there are potential environmental and social gains to be achieved as a result of decentralizing manufacturing, reducing transport, and distributing labor. The main challenge lies on operationalizing this practice and understanding the full impact it will have on supply-chains, particularly in the people directly/indirectly affected (social aspects) and the overall environmental footprint (environmental aspects). While the potential benefits are relatively well documented both the from economic and to some extent environmental perspectives, changing manufacturing and supply-chain paradigms, will have a fundamental impact on the supply chain and everyone involved. To study these changes, this paper focuses on creating, presenting, and evaluating a business model to facilitate the transition from physical to digital warehouses. The authors have chosen to use the Business Model Canvas for Sustainability (BMCS) and the adjacent assessment framework to compare the business model to current practices and evaluate its economic, environmental, and social sustainability.

Investigating the Potential of 3D Printing Technology on Spare Parts Business for Supply Chain Management in Appliance Industry

3D printing is a process used to fabricate three-dimensional objects based on the digitally controlled deposition of successive layers of material until a final structure is created. This research presents a case study in which two models were built to estimate the operation cost of a spare parts business for an appliance manufacturing company: a base model and an alternate benchmark model in which spare parts are supplied by the traditional manufacturing method or 3D printing. With the developed models, this research compares the total inventory cost of ownership and the number of suppliers for two supply chain management scenarios. Further, a third model (hybrid cost model) was established where spare parts are supplied partially by traditional manufacturing and partially by 3D printing according to maximize cost savings. The cost analysis from the three models concludes that the mixed model shows the best outcome for simplifying supply management complexity and ultimately reducing total cost of spare parts inventory. This case study can help appliance industries to assess and decide if 3D printing is a feasible production method for spare parts in terms of supply chain management strategy.

WAREHOUSE OPERATIONS OF PASSENGER SERVICE CAR DEPOT WITH LOGISTICS ELEMENTS

The article discusses the issues of rational organization of the warehouse process of storing inventory, the sequential implementation of warehouse operations, the improvement of labor organization and technological solutions, the effective use of various equipment involved in the performance of technological operations at the warehouse of the carriage depot of the Joint Stock Company «Uzpasstrans». The issues of creating a database for storing goods, an effective system for organizing their account are considered. The ER-diagram of the database of warehouse inventory of spare parts and components has been developed. The functions and procedures for working with database data on components and spare parts located in the warehouse of the carriage depot have been determined.

Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era.

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

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.

Analysis and Control for Heavy Equipment Spare Parts Inventory in the Nickel Mining Industry

In the mining industry, analyzing, and controlling the spare parts inventory is important activities and need special attention from the company. The high production cost of the mining industry, including spare parts and shortages costs affects the financial and operational aspects of the company. Spare parts inventory management policy is critical to increase the service level and minimize spare parts cost. In this mining case study, the out of stock incidents in the heavy equipment spare parts were often occurring and significantly affecting the performance of maintenance and operations. The purpose of this study is to determine minimum stock (safety stock) and maximum stock to reduce overstocks and reduce the number of stocks out in spare parts inventory and as well as inventory cost. The method used to solve the problem is economic order quantity (EOQ) and continuous review control (S, s) to determine the minimum and maximum stocks. Determining the combination of S and s has a major impact on the success of inventory control. Substitution parts are subdivided into alphabetical values to determine their significance. As a result of calculating inventory value using EOQ and S, s will get the highest inventory value to reduce inventory costs.

Reducing Spare Part Inventory Cost with Shortage Elimination through Probabilistic Economic Order Quantity

Company Z had an inventory of fast moving spare parts where the demand pattern for these spare parts fluctuated every month and there were several months in which the demand was zero. In addition, company Z did not have a safety stock for these spare parts, so the risk of shortage for these spare parts was quite high. This study aimed to reduce fast moving spare part inventory costs and prevent the risk of shortage by determining the optimal order quantity and re-ordering time. The inventory control model used to determine the optimal order quantity was the Probabilistic Economic Order Quantity (Probabilistic EOQ). This model is appropriate to use in conditions of unstable demand and it requires the calculation of safety stock to prevent shortage. Research data in the form of data on demand, lead time, purchasing cost, ordering cost, carrying cost, and shortage cost were collected through company documents. The data that had been collected was then processed using an excel spreadsheet. Demand forecasting for the next 12 months was carried out using the Croston method. Forecasting results were used as input in calculating the optimal order quantity on Probabilistic EOQ. Furthermore, data processing was run by calculating the safety stock and reorder point. The total inventory cost with Probabilistic EOQ was calculated and compared with the company’s total inventory cost to determine whether Probabilistic EOQ is able to create inventory cost reduction. The results showed that Probabilistic EOQ enabled the company to reduce its inventory cost up to 57.85%.

Joint optimization of maintenance and inventory policies for multi-unit systems

A maintenance policy and the management of the inventory of spare parts and their joint optimization often challenge managers and researchers. In this paper, the first analytical joint optimization model is established. A simulation model is then developed for the system operating under the suggested condition-based maintenance to optimize the maintenance outline of mining dump truck motors based on oil monitoring. Our model is combined with a genetic algorithm to obtain the optimal response. In the presented model, the Inspection intervals ( $$T$$ T ) and the maximum stock level ( $$S$$ S ) are jointly optimized for minimizing cost. To build a sample and a simulation of various repair events, 11,000 oil analysis data is used. The deterioration of spare parts is shown with an increasing numerical variable over time, which follows a function. Using the existing datasets, the deterioration rate function is obtained. Another process required for simulation is the failure probability function. Due to the extent of deterioration with various breakdowns, there are uncertainties and different values. Condition-based maintenance is used to determine the deterioration level of failure. In the end, the results of the simulation are compared with the current costs resulting from the workshop policies.

Spare Parts Demand Forecasting During Covid 19 pandemic

The research was conducted to analyze the calculation of demand forecasting and inventory of spare parts . The data collection method uses descriptive quantitative by conducting literature studies, interviews, and collecting sample data of 140 spare parts from June 2019 to June 2020. The data classification process with FSN method, obtained 13 fast moving spare parts use for calculating demand forecasting based on the Triple Exponential Smoothing method. The results from the trial error to get the smoothing value of alpha, beta and gamma errors prioritize to best MAPE <5%. Some MAPE errors above 5% are X000000 000254 MAPE = 5.68% , X000000 005566 MAPE = 21.12, X400 401 10 72 MAPE = 13.14, and X400 401 26 71 MAPE = 16.70. The calculation result of safety stock and reorder point find 4 spare parts have low level inventory have to reorder.

Optimal preventive replacement last policy for a successive random works system with random lead time

Most systems deteriorate with age and use, and eventually, fail from either or both causes in random environment. It is reasonable in commercial industries that only one spare item is available for replacement. Selecting a cost efficient and effective replacement strategy to improve system reliability and inventory of spare parts is crucial work. In this paper, we consider an operating system which works at successive random times and models an imperfect maintenance action under two types of age-dependent failures, repairable minor failures (R) and unrepairable catastrophic failures (U). R failures are followed by a minimal repair and U failures are removed by a corrective replacement. A notation of preventive replacement last policy is considered in which the system is replaced before any U failures at age T or at number N of working times, whichever occurs last. A spare unit for replacement can be delivered upon order and is available only when the random lead time is finished. The main objective is to determine an optimal schedule (T, N) of that minimizes the mean cost rate function in a finite time horizon. The existence and uniqueness of optimal PRL policy are derived analytically and computed numerically.

Managing Spare Parts Inventory by Incorporating Holding Costs and Storage Constraints

Abstract A key factor for motivating intending buyers of raw materials is vendor responsiveness. Therefore, to meet demand, a pre-approved level of stocks is often maintained. In contrast, the decision to keep an uncontrolled amount of stock could be counter-productive with cost components associated with holding often ignored unintentionally. In this study, the objective is to develop a spare parts inventory model that incorporates ignored holding costs with a storage constraint for a motorcycle assembly plant (MAP). The inventory policy, structure of holding costs, and spare parts sales reports were consulted for relevant data. The spare parts were categorized and selected using ABC analysis. A spare parts inventory model, which considers ignored holding cost, was formulated. The model was executed using Lingo optimisation software release 18.0.56 to determine the pair of the order quantity (Ɋ) and reorder point (Ɍ). 177 spare part items were identified using ABC analysis. The parts categorisation revealed that 21, 31, 125 part items belong to categories A, B, and C with 81, 15 and 4% of annual sales value, respectively. From category A, nine items contributed significantly to overall sales. The demand pattern for these items was probabilistic based on their coefficient of variation. The pair (Ɋ, Ɍ) for items N, Z, AY, K, AM, J, P, AL and AZ are (174,688), (71,147), (78,150), (86,163), (18,15), (88,170), (128,118), (33,43) and (87,152), respectively. These pairs yielded a total inventory cost of ₦2,177,363 when compared to the current total inventory investment of ₦6,800,000 resulting in a 67.9% cost reduction. A model to manage spare parts inventory with relevant holding cost components was developed for MAP to ensure the availability of items, maximize usage of storage space, and minimize total inventory cost.

Workforce training and operations planning for maintenance centres under demand uncertainty

Companies that provide repair & overhaul services to the users of complex technical systems are confronted with uncertain volume of demand when making tactical decisions such as workforce training and planning of repair operations over an annual planning horizon. Given the high importance of equipment availability (e.g. gas turbines) to the users (e.g, power plants), any delay in the delivery of repaired equipment caused by demand uncertainty would lead to significant penalties and loss of customer goodwill. In this paper, a two-stage stochastic programming model is proposed to obtain the optimal number of items to repair, spare part inventory, and the number of operators to train with the goal of minimising the total expected cost of maintenance operations and late delivery. Outsourcing and borrowing strategies are adopted as corrective measures to reduce the probability of late delivery in the emerge of demand uncertainty. Numerical findings illustrate the importance of integrating uncertainty into these operations planning decisions as well as the mitigation strategies in handling the cost of the system.