Part Of Inventory Research Articles

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.

Model for Optimizing the Ratios of the Company Suppliers in Slovak Automotive Industry

The Slovak automotive industry consists of various companies and suppliers, with different positions in the supply chain for automotive manufacturers. The accuracy of component deliveries and their quality affect the ultimate competitiveness of the entire automotive industry. The creation of strategic partnerships and stable supplier–customer relationships is currently a necessity. The aim of the article is to design the model for optimizing the ratio of the company suppliers in the Slovak automotive industry. The basis for designing the model was the results of our own research focused on the quality and timeliness of component deliveries from various suppliers. Supply chain members work with multiple subcontractors, using multiple subcontractors to supply the same components. We analyzed the overall quality of delivered components at a certain stage of the supply chain. The quality of the supplied components was the sum of all items from all suppliers. The aim of the proposed model is to determine the optimal percentage of individual suppliers of a particular part so as to minimize the overall risk associated with the supply and inventory of a particular part for the customer. Research methodology was focused on identification of the key performance indicators and key risk indicators of components deliveries by different suppliers. Those indicators provide a basis for the effective results of further research. We designed and used an algorithm for preparing and evaluating the model for optimizing the ratio of the company suppliers in the Slovak automotive industry. This research used modeling methods, simulations, and optimization models. The proposed model was verified in the specific conditions of the automotive supply chain. Deliveries from two subcontractors were simulated. Based on the results of the simulations, the optimal supply ratio of the two subcontractors was determined for a specific component of a member of the supply chain at a higher level. The results of the research can be useful for different suppliers in the Slovak automotive industry. By partially modifying the model and adapting it for different conditions in other industries, the model of optimizing the distribution of supply from individual suppliers can be used for other supply chains.

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%.

A capacitated lot-sizing model with sequence-dependent setups, parallel machines and bi-part injection moulding

This paper aims to propose a model for solving a capacitated lot-sizing problem with sequence-dependent setups (CLSD) and parallel machines in a bi-part injection moulding (BPIM) context that consists of injecting two different parts or products into the same mould in separate injection cavities. These two parts require the same sequence order and available capacity at the same instant, but generate different part inventories. The addressed problem should be considered a multi-machine CLSD-BPIM one. We provide a mixed integer linear programming (MILP) model for it. The proposed model is based on a real-world case study from a second-tier supplier of the automotive sector. In sequence setup, inventory and resource setup costs terms, the benefits of the proposed multi-machine CLSD-BPIM model are validated by comparing it with a single-machine CLSD-BPIM model that replicates the current planning scenario carried out in the company under study. Additionally, random instances based on the real problem have been generated and tested. The computational results exhibit optimal solutions for the small instances with an average of 0.2-second solution time, the medium instances present average gaps of 0.47% with 2.6 h and large instances present average gaps of 4% with 6 h solution time.

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.

Redeploying excess inventories with lateral and reverse transshipments

Service parts management has the potential to generate high profits for companies that deliver superior service parts services in the after-sale market. However, a big challenge in managing service parts operations is to meet the high expectations of service levels and to reduce excess inventories caused by fluctuating demand and a complex service parts logistics network structure. By expanding the conventional inventory management that passively focuses on the forward and lateral flows of service parts deployment, we propose a crucial but overlooked practice of inventory redeployment as an integral part of the operations that allow the proactive management of lateral and reverse flows of service parts. We formulate the service parts inventory problem with the application of an excess inventory redeployment strategy in a multi-echelon service network as a multi-period integer programming model. This optimisation model is evaluated using a case study of an international company’s service parts operations and demonstrates a higher cost-saving potential. Our novel, integrated approach confers the advantage of redeploying excess inventories in a closed-loop service parts logistics network with a higher cost-saving potential that could not have been achieved in a conventional approach.

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.

An integrated location-inventory model for a spare part’s supply chain considering facility disruption risk and CO2 emission

Purpose: Managing the inventory of spare parts is very difficult because of the stochastic nature of part’s demand. Also, only controlling the inventory of the spare part is not enough; instead, the supply chain of the spare part needs to be managed efficiently. Moreover, every organization now aims to have a resilient and sustainable supply chain to overcome the risk of facility disruption and to ensure environmental sustainability. This paper thus aims to establish a model of inventory-location relating to the resilient supply chain network of spare parts.Design/methodology/approach: First, applying queuing theory, a location-inventory model for a spare parts supply chain facing a facility disruption risk and has a restriction for CO2 emission, is developed. The model is later formulated as a non-linear mixed-integer programming problem and is solved using MATLAB.Findings: The model gives optimal decisions about the location of the warehouse facility and the policy of inventory management of each location selected. The sensitivity analysis shows that the very low probability of facility disruption does not influence controlling the average emission level. However, the average emission level certainly decreases with the increment of the disruption probability when the facility disruption probability is significant.Practical implications: Using this model, based on the cost and emission parameters and the likelihood of facility disruption, the spare part’s manufacturer can optimize the total average cost of the spare part’s supply chain through making a trade-off between productions, warehouse selection, inventory warehousing and demand allocation.Originality/value: Previous research focuses only on developing a framework for designing an efficient spare parts planning and control system. The inventory-location model for spare parts is not addressed in the sense of risk of facilities disturbance and emission. This research first time jointly considered the probabilistic facility disruption risk and carbon emission for modeling the spare part’s supply chain network.

Policy Analysis on Spare Part Inventory of Critical Asset with Life Cycle Cost Approach

Life Cycle Cost (LCC) is a method which is used to calculate all costs over the use’s period that can be seen from the economic aspect to determine the alternative design. This method is also used to help for making a decision. The purpose of this research is to design a strategy to assist companies for making decisions in purchasing and inventory of critical parts engine based on the lowest cost. The result of this research is the company’s current strategy of breakdown maintenance costs Rp. 508,574,008.40 for one year of production. Meanwhile the results of the proposal strategy are preventive maintenance with Monte Carlo simulation tool to predict the amount of parts and when the inventory parts of the engine need to be replaced. The proposal strategy costs Rp. 786,968,691.40 for three years of production. So, it can be concluded that by using LCC calculation with Monte Carlo simulation tool, it can help the decision maker to determine the minimum cost in long term period.

Use of proactive spare parts inventory management (PSPIM) techniques for material handling vis-à-vis cement industry

In the cement industry, the decision for the replacement of the Spare Parts Inventory is the more essential decision, especially, the proportion of the product collection, dedicated to replacement parts is always important. The unusual incidence of demand associated with these products & the consequent low contribution to an organization's overall turnover, this slower-moving SKUs rising to constitute up to 60 percent of the total stock value. Simple changes in their implementation may also be converted into significant cost savings. The current research is undertaken to measure and use a proactive methodology for keeping the inventory of Spare parts. In Cement companies, the methods used for High-value management of material, spare part material, carrying cost, and components of replenishment lead time with critical spare part inventory to be kept, is of greater significance. The methodology of the research includes gathering the views of 902 respondents of cement companies. The same was further analysed with the statistical technique multiple regression to develop the benefits model for the PSPI. The outcome of the research is in the form of aspects of Spare part material with six categories including Physical Stockroom, Computerization Assessment, Tracking enhancement steps for spare part inventory, Visual Inspection, Rules for effective spare part management and essential points regarding JIT.

ОСОБЛИВОСТІ ЗАСТОСУВАННЯ ТА ПОРІВНЯЛЬНА ХАРАКТЕРИСТИКА ОПЕРАЦІЙНИХ СИСТЕМ ОРГАНІЗАЦІЇ ВИРОБНИЦТВА

The article establishes and summarizes the application features, as well as provides a comparative description of the "traditional" production organization system and the "just in time" system. It is established that the "just in time" system, in fact, is a single set of measures that is carried out in order to achieve the maximum possible level of production with the use of minimum inventory of parts and components, semi-finished products and finished products, according to which the basic element of the system is to prevent any excess of raw materials, materials, equipment, etc. In order to ensure uninterrupted processes, the "just in time" system requires high quality products at every stage of the process, clear fulfillment of suppliers' contractual obligations and correct forecasting of demand for finished products. At the same time, special importance in the process of ensuring the continuity of flows and uninterrupted operation of equipment is attached to the creation of a system of scheduled preventive repairs. It is proved that the "just in time" supply system is a modern inventory management operating system, which has a number of significant advantages over the traditional approach. The article outlines the main obstacles to implementing and ensuring the effective application of the production organization system "just in time" by domestic enterprises. It is proved that the main condition for the full implementation and effective application of the operating system for organizing production "just in time" is to ensure flexible and as fast as possible restructuring of all enterprise processes. It is argued that the use of this system will allow achieving significant improvements by eliminating losses, preventing technological disruptions or failures, and eliminating warehouses of raw materials, materials, and finished products. At the same time, the implementation and further effective application of the "just in time" system will require strict and strict compliance with basic principles and approaches, provided that all employees of the enterprise are involved without exception.

Integration of parts scheduling, MRP, production planning and generalized fixed-charge transportation planning in the design of a dynamic cellular manufacturing system

In this paper, to integrate the decisions of parts scheduling, Material Requirement Planning (MRP), Production Planning (PP) and Transportation Planning (TP) for designing a Cellular Manufacturing System (CMS) under a dynamic environment, a Mixed-Integer Nonlinear Programming (MINLP) mathematical model is formulated. The proposed mathematical model integrates extensive coverage of significant manufacturing characteristics in designing a CMS to be implemented in a three-layer supply chain. The considered features include markets demands, heterogeneous vehicles, raw materials requirements planning, parts due dates, cell size limits, machines capacity, intra/inter cell material handling time/cost, transportation time/cost, operation time, alternative processing routes in addition to the main decisions of parts scheduling, PP, TP and dynamic cell formation. Also, some novel characteristics are incorporated based on a three-layer supply chain that make the presented model remarkable respect to the literature including (1) In the first layer, planning the orders of raw materials with different lead times and usage coefficients is performed, (2) In the second layer, decisions of dynamic cell formation and parts scheduling are made, and (3) In the third layer, optimal vehicles are selected as a generalized fixed-charge TP based on transportation time and cost to satisfy multi-markets with different demand volumes. The components in the objective function to be minimized include total costs of holding the parts inventories in the markets, backorders, tardiness, transportation of the parts from the plant to the markets, purchase of raw materials, keeping raw materials in the plant warehouse, intercellular/intracellular movements and machine relocation. An illustrative numerical example is solved by the CPLEX solver to illustrate the achievements obtained by the incorporated characteristics in the integrated model. Furthermore, a sensitivity analysis is performed to assess the effects of important parameters on the model performance. Since the proposed model is NP-hard, a Simulated Annealing (SA) algorithm is improved by an elaborately-designed matrix-based chromosome representation is applied to represent all decision variables, as well as a sequential procedure generating initial solutions. Several test problems either generated randomly or taken from the literature with various sizes are solved and the results are compared with the solutions gained using CPLEX solver. The comparisons results show that the designed SA is capable of evolving optimal or near-optimal solutions with reasonable relative gaps in a computationally satisfactory manner.

Evaluation model for cooperative inventory pooling-systems

Spare parts management is the basic requirement for ensuring the continuous availability of technical systems, but uncertainties in demand and delivery times can necessitate a large spare parts inventory. It is possible to reduce the required inventory levels and costs by using a cooperative inventory pooling-system, but there is as yet no suitable method of estimating the benefits and requirements. To address this deficiency, an evaluation model was developed, allowing interested companies to examine cooperative inventory pooling-systems. The model presented qualitatively evaluates the aptitude of specific companies and environments and subsequently demonstrates the economic potential based on the data provided.

Operational Disruption Exposure and Part Inventory Replenishment

High impact / low probability supply chain disruptions represent a major challenge for firms. Such disruptions represent a classic bottleneck shifting problem. When the firm can backlog some portion of its unmet customer orders, its disruption exposure is driven by (i) lost demand in the disruption period due to part constraints and (ii) lost demand in the recovery period due to capacity constraints. Although holding inventory has been shown to mitigate a firm's disruption exposure, it is considered costly to do so. We address this complication by showing that the firm's disruption exposure is decreasing at a decreasing rate with the inventory quantity of the disrupted part. More surprisingly, the firm's disruption exposure is decreasing at a decreasing rate with the inventory quantity of non-disrupted parts. We exploit these findings to further show that the firm can materially reduce its disruption exposure across all parts by holding a small amount of incremental inventory of selected parts. Using these insights and the detailed supply chain and operational data from our research partner, a large manufacturer of material handling equipment, we show how targeted changes to the firm's inventory policies across many parts can achieve a significant reduction in its disruption exposure, at no cost. This strategic pool of parts serves as a means for the firm to effectively store its otherwise unused disruption period production capacity.

Analisa Pengendalian Persediaan Suku Cadang Pada PT. XYZ Dengan Metode Analisis ABC

Inventory can be interpreted as a stock of goods to be sold or used at a certain time,without the inventory the company will run the risk and can not meet costomer demand. This research was conducted to analyze spare part inventory using ABC analysis method and EOQ method at PT. Adiprima Suraprinta, Gresik.The results of this study are there there are 4 spare parts inventory items in group A with a cumulative percentage of 8,59% by absorbing a budget of 56,78%, there are 5 spare parts inventory items in group B with a cumulative percentage of 18,47% by absorbing a budget of 24,15%, there are 17 spare parts inventory items in group C with a cumulative percentage of 72,92% by absorbing a budget of 10%.