Inventory Control

A technique based on reactive gas injection in the afterglow region of the divertor plasma is proposed for the suppression of tritium-carbon codeposits in remote areas of ITER when operated with carbonbased divertor targets. Experiments in a divertor simulator plasma device indicate that a 4 nm= min deposition can be suppressed by addition of 1 Pa Á m 3 s À1 ammonia flow at 10 cm from the plasma. These results bolster the concept of nonperturbative scavenger injection for tritium inventory control in carbonbased fusion plasma devices, thus paving the way for ITER operation in the active phase under a carbondominated, plasma facing component background.

Inventory management is crucial for organizational performance, yet a significant gap exists between theoretical models and practical applications. This study explores the inventory management practices and challenges among Jordanian companies across various industries. Given the limited empirical research on this topic in Jordan, a qualitative approach was adopted using semistructured interviews with representatives from 45 manufacturing, retail, and service companies. The findings reveal a significant gap between theoretical inventory management models and their practical application among participating companies. The common challenges of inventory management were also identified and found to include the lack of formal cost calculation methods, limited technological adoption, supply chain disruptions, and reliance on managerial intuition over data-driven decision-making. The study underscores the need to develop flexible, context-specific inventory models and enhance technological integration, particularly for Small and Medium-Sized Enterprises (SMEs). The study also found that company size significantly affects inventory management, where smaller companies lacked specialized personnel and relied on informal approaches using basic tools like spreadsheets, leading to inefficiencies and limited responsiveness to demand fluctuations. A set of recommendations for practitioners was provided, including adopting adaptable inventory strategies, investing in technology, improving demand forecasting, and strengthening supplier relationships. The study also highlights the importance of bridging the gap between inventory theory and practice, offering valuable insights for academics and practitioners aiming to optimize inventory management in developing economies like Jordan.

PT. MDS sejak awal berdiri senantiasa berkomitmen untuk menyediakan beragam produk pangan daging olahan yang bernilai tambah, bernutrisi, terjangkau, sekaligus membuka lapangan pekerjaan di Indonesia. Untuk menunjang pengadaan stok yang akurat dan tepat dibutuhkan pengelolaan Sparepart yang baik agar tidak terjadi downtime dalam penyediaan stok. Permasalahan penurunan pada produksi terjadi akibat dari sparepart pada PT. MDS. JIT atau yang lebih dikenal dengan istilah Just-in-Time , merupakan salah satu pendekatan yang berdampak pada suatu usaha yang akan dioperasikan. Tujuan Penelitian: Untuk mengetahui penerapan Just-in-Time (JIT) dapat diterapkan dalam pengelolaan sparepart untuk meminimalkan terjadinya stock-out dan downtime mesin produksi, untuk mengetahui metode pengendalian persediaan seperti Economic Order Quantity (EOQ), Safety Stock, dan Reorder Point (ROP) dapat mendukung implementasi JIT dalam penyediaan sparepart yang lebih tepat waktu
dan efisien, Untuk mengetahui penerapan Activity-Based Budgeting (ABB), membantu pengendalian biaya pengadaan sparepart agar selaras dengan prinsip Just-in-Time. Metode Penelitian: penelitian kuantitatif dengan pendekatan deskriptif persentase dan analisis Economic Order Quality, Safety Stock, Reorder Point dan Activity-Based Budgeting. Hasil perhitungan EOQ, Safety Stock, dan ROP yang didukung oleh analisis Activity-Based Budgeting menunjukkan bahwa pengelolaan sparepart dapat dilakukan secara lebih efektif dan efisien. Metode Activity Based Budgeting memiliki keterkaitan erat dalam meningkatkan efisiensi
(Just In Time) pada pengelolaan sparepart (suku cadang) melalui pendekatan berbasis aktivitas yang mendetail.

Tantangan geografis sering kali menjadi hambatan utama dalam distribusi logistik medis darurat, yang berdampak langsung pada tingginya angka mortalitas pasien. Penelitian ini bertujuan untuk menganalisis keberhasilan Rwanda dalam mentransformasi sistem kesehatan nasional melalui adopsi teknologi Unmanned Aerial Vehicle (UAV) atau drone, serta merumuskan relevansi dan strategi implementasinya di Indonesia. Menggunakan metode tinjauan literatur dari studi Wharton School dan The Lancet, hasil penelitian menunjukkan bahwa integrasi drone sejak tahun 2016 di Rwanda berhasil menurunkan angka kematian ibu akibat perdarahan pascapersalinan hingga 51% dan pasien trauma sebesar 30%. Selain itu, efisiensi manajemen inventaris meningkat secara signifikan dengan penurunan stok sel darah merah di rumah sakit sebesar 63% dan reduksi pemborosan darah hingga 40%. Berbeda dengan Rwanda yang telah mengintegrasikan layanan berskala nasional, pemanfaatan drone medis di Indonesia saat ini masih berada pada tahap proyek percontohan (pilot project) regional yang bersifat sektoral. Namun, Indonesia memiliki potensi besar untuk mereplikasi kesuksesan ini di wilayah terpencil seperti Papua, Maluku, dan Kalimantan. Penelitian ini menyimpulkan bahwa kunci akselerasi transformasi kesehatan berbasis drone di Indonesia terletak pada integrasi regulasi lintas lembaga, investasi infrastruktur lokal, dan sinergi bersama layanan telemedisin demi mewujudkan keadilan akses kesehatan yang berkelanjutan bagi generasi masa depan.

Los estudios realizados a las pequeñas y medianas empresas del país son generalmente gerenciales, relegando sus sistemas de producción. El objetivo de esta investigación es elaborar a través de la Simulación Dinámica dos modelos que ejemplifiquen los sistemas de producción de empujar y de halar para notar la interrelación que hay entre los elementos que componen estas cadenas y monitorear que variables intervienen en las mejoras o desmejoras de los flujos de materiales, información e inventarios, con los resultados obtenidos de estos modelos se plantean alternativas para llevar el sistema tradicional de empuje lo más cercano a un sistema de halar, utilizando Mejora Continua y sistemas de información Kanban, considerando la menor inversión para dicha reestructuración. Para realizar estos modelos de simulación se consideró como muestra un grupo de pequeñas y medianas empresas del estado Carabobo, buscando la posibilidad de adaptarlos a cualquiera de estas PyMEs pues están concebidos como plantillas generales, así se podrán ajustar a la realidad de estas compañías buscando siempre la posibilidad de ahorrar tiempo y dinero antes de tomar decisiones de compra de materiales, puestas en marcha de fabricación y mantenimiento de inventarios. Este estudio es pionero en la Escuela de Ingeniería Industrial de la Universidad de Carabobo pues es el primero en utilizar Simulación Dinámica en su ejecución, a través del software Vensim, dejando las bases para próximos trabajos de grado o ascenso. De esta manera la Escuela estará a la vanguardia tecnológica que exigen estos tiempos.

Pharmaceutical Companies is a place for people to get health services. One unit that has an important role is the Pharmacy division. Pharmacy services include patient care and are responsible for the provision of medicines and other medical devices. This study aimed to find the control of Medicine supplies in sufficient quantities, at the time needed and at the lowest cost. Management of Medicine supplies can be done using the Always, Better, and Control (ABC) method and the Vital, Essential, and Non-Essential (VEN) method to determine priorities in Medicine procurement so that funds are used efficiently. Furthermore, the EOQ (Economic Order Quantity) calculation is carried out to obtain optimal inventory.

In this paper, we consider a continuous review inventory system of a slow moving item for which the demand rate drops to a lower level at a pre-determined time. Inventory system is controlled according to one-for-one replenishment policy with fixed lead time. Adaptation to the lower demand rate is achieved by changing the control policy in advance and letting the demand take away the excess stocks. We show that the timing of the control policy change primarily determines the tradeoff between backordering penalties and obsolescence costs. We propose an approximate solution for the optimal time to shift to the new control policy minimizing the expected total cost during the transient period. We find that the advance policy change results in significant cost savings and the approximation yields near optimal expected total costs.

This paper presents a case study of applying a Bayesian approach to forecast demand and subsequently determine the appropriate parameter S of an ( S-1,S) inventory system for controlling spare parts of electronic equipment. First, the problem and the current policy are described. Then, the basic elements of the Bayesian approach are introduced and the procedure for calculating the appropriate parameter S is illustrated. Finally, we present the results of applying the Bayesian approach in an innovative way to determine the stock levels of three types of circuit packs at several locations. According to the proposed method, a lower base stock than the one currently used is sufficient to achieve the desired service level.

In most multi-item inventory systems, the ordering costs consist of a major cost and a minor cost for each item included. Applying for every individual item a cyclic inventory policy, where the cycle length is a multiple of some basic cycle time, reduces the major ordering costs. An e cient algorithm to determine the optimal policy of this type is discussed in this paper. It is shown that this algorithm can be used for deterministic multi-item inventory problems, with general cost rate functions and possibly service level constraints, of which the well-known joint replenishment problem is a special case. Some useful results in determining the optimal control parameters are derived, and worked out for piecewise linear cost rate functions. Numerical results for this case show that the algorithm signi cantly outperforms other solution methods, both in the quality of the solution as in the running time.

For many maintenance organizations, on-condition maintenance tasks are the most important source of spare part demand. An uneven distribution of maintenance tasks over time is an important cause for intermittency in spare parts demand, and this intermittency complicates spare parts inventory control severely. In an attempt to partially overcome these complications, we propose to use the maintenance plan, i.e. the planned maintenance tasks, as a source of advance demand information. We propose a simple forecasting mechanism to estimate the spare part demand distribution based on the maintenance plan, and develop a dynamic inventory control method based on these forecasts. The value of this approach is benchmarked against state-of-the art time series forecast methods, using data from two large maintenance organizations. We find that the proposed method can yield cost savings of 23 to 51% compared to the traditional methods.

We study spare parts inventory control for an aircraft component repair shop. Inspection of a defective component reveals which spare parts are needed to repair it, and in what quantity. Spare part shortages delay repairs, while aircraft operators demand short component repair times. Current spare parts inventory optimization methods cannot guarantee the performance on the component level, which is desired by the operators. To address this shortfall, our model incorporates operator requirements as time-window fill rate requirements for the repair turnaround times for each component type. In alignment with typical repair shop policies, spare parts are allocated on a first come first served basis to repairs, and their inventory is controlled using (s, S) policies. Our solution approach applies column generation in an integer programming formulation. A novel method is developed to solve the related pricing problem. Paired with efficient rounding procedures, the approach solves real-life instances of the problem, consisting of thousands of spare parts and components, in minutes. A case study at a repair shop reveals how data may be obtained in order to implement the approach as an automated method for decision support. We show that the implementation ensures that inventory decisions are aligned with performance targets.

Tam Zamanında Üretim (TZÜ) sistemi ilk kez Japonya'da Toyota firmasında uygulanmıştır. Bu sisteminin imalat firmaları arasında gittikçe tercih edilmesi, israfa duyarlı olması ve maliyetleri azaltma çabası içinde olması dolayısıyladır. Bu çalışmada, TZÜ'de tedarikçi ilişkisi konusu ele alınmıştır. Elektrikli ev aletleri üreten ve TZÜ yapısına uygun üretim yapan bir firma tespit edilerek tedarikçi ilişkileri değerlendirilmiştir. Seçilen bir ürün için daha önce 83 olan tedarikçi firma sayısı 19'a indirgenebilmiştir. Ayrıca, bu ürün için en iyi üretim parti büyüklüğü hesaplanarak, ürünün bileşen parça ihtiyaçları buna göre belirlenmiştir. Bunun sonucunda yıllık stok taşıma maliyetinde önemli bir düşüş sağlanabileceği tespit edilmiştir.

Operational efficiency, equipment reliability, workplace safety, and resource optimization are critical determinants of industrial productivity and sustainability. This paper presents a systematic operational weakness analysis of Premier 1888, focusing on electrical infrastructure, maintenance strategy, spare parts management, workforce capability, material logistics, occupational health exposure, and power quality monitoring. The study identifies several structural and operational deficiencies, including aging electrical systems, reactive maintenance culture, insufficient critical spare inventory, workforce dependency, delays in material support, open chemical dust cleaning exposure, and limited power quality monitoring. Based on engineering management principles, practical corrective strategies are proposed, including preventive maintenance systems, electrical modernization, strategic inventory planning, workforce development, logistics optimization, enclosed cleaning infrastructure, and electrical performance monitoring. The implementation of these recommendations is expected to improve operational reliability, reduce downtime, strengthen safety compliance, and enhance long-term cost efficiency. This paper serves as a practical framework for industrial operational improvement in manufacturing environments.

Computer spare parts (CSPs) inventory management (IM) is very important for many companies. Excess inventory leads to high storage costs. On the other hand, the lack of CSP has a strong impact on the quality of service. This study was conducted by Power-One Jordan Computer Hardware Software company (POJCHSC), Amman, Jordan. The focus area was the IM department, and the target sample was employees working in the management department. The results showed that factors influencing the management of slow moving CSPs include production costs, obsolescence and CSPs dependence availability, and transportation costs. By forecasting during the study, the results showed that the demand for adapters and chargers would increase by 20%. This demand forecast was performed using the economical order quantity (EOQ) model. The percentage of profits made by this company is 48%, and this requires some intervention to prevent losses. The results of this study are useful to the company, as well as to other similar industries that deal with slow-moving items. These results will help to simplify IM of slow-moving items. When we focused on POJCHSC manufacturers, the disadvantages of using the traditional ABC classification model were identified. Therefore, there is a need to have an ABC classification that is improved, and which takes into consideration the criticality of the slow-moving CSP.

Critical spares are the components that your operation must have in order to stay up and running. Without these parts, your production line will break down, and you won't be able to meet deadlines or deliver products. Inventory management is becoming a critical aspect of maintenance-repair businesses. The goal of companies is to minimize inventory costs while maximizing service levels. The ability to perform these two conflicting situations at the optimum level provides various benefits to enterprises. Critical spares management in mining requires identifying high-impact components, optimizing inventory levels using data analytics, and collaborating with suppliers to minimize downtime. Managing critical spare parts effectively is essential for ensuring the seamless operation of Maintenance, Repair, and Operations (MRO) inventory. However, the challenges surrounding critical spare parts management can often lead to inefficiencies and increased costs. In this blog, we'll explore best practices for managing critical spare parts, highlighting common pitfalls and offering innovative solutions, particularly through the integration of AI technologies MRO inventory management has always posed a significant challenge, particularly when it comes to predicting and managing stocking levels for critical spare parts. Many businesses have relied on supply chain software solutions originally designed for finished goods or raw materials, only to find these tools inadequate for MRO inventory. Several key issues contribute to this ineffectiveness

We study the Dynamic Programming Equation arising in the problem of stochastic control in discrete time over an infinite horozon. The data are unbounded, in which case the solution is not unique. Our results deal with the structure of the set of solutions. In particular we show that there exists a minimum and a maximum solution and we give the interpretation of both solutions. The solutions will be approximated by monotone approximation schemes, which also have an interpretation as the value function of a stochastic control problem. We give an application of the results and the methods to a model in the theory of production.

ResUmen Los inventarios representan las existencias de recursos que las organizaciones usan para cumplir con sus objetivos. Para los países occidentales los inventarios son un problema mientras que para la cultura japonesa son caretas que intentan cubrir una serie de problemas más graves en la organización. En ambos casos los inventarios acarrean graves complicaciones, que las organizaciones ansían evitar reduciendo al mínimo sus niveles. De ahí que las organizaciones se vean en la necesidad de utilizar un sistema de gestión de inventario que les permita la adecuada planificación y control de sus existencias. Las organizaciones pueden valerse de diversos modelos para administrar convenientemente los inventarios según el tipo de demanda a la que estén sujetos los diferentes artículos que los componen. Los modelos clásicos fueron desarrollados para tratar con demandas independientes de artículos, pero cuando las demandas dependen de las necesidades de otros artículos almacenados, surg...

The failure of Nigeria's manufacturing sector, which includes consumer and industrial goods companies, may be attributed to an inability to adequately manage their firms' working capital, which is the primary reason why the sector is deemed underperforming, with their contribution to the Nigerian economy falling below 10%. This study examined a comparative analysis of working capital management and financial performance across Nigerian listed consumer and industrial goods firms. The study used an ex post facto design. The study used secondary data from the annual reports and accounts of 10 consumer products and 10 industrial goods enterprises during a 10-year period (2015-2024). The failure of Nigeria's manufacturing sector, which includes consumer and industrial goods companies, may be attributed to their inability to adequately manage their firms' working capital, which in turn serves as the primary reason why the sector is deemed underperforming, with their contribution to the Nigerian economy dropping below 10%. This study conducted a comparative analysis of working capital management and financial performance in Nigerian listed consumer and industrial products firms. The study employed an ex post facto design. Over a ten-year period, the study relied on secondary data sources such as annual reports and accounts from ten consumer products and ten industrial goods businesses. According to the study, listed consumer goods businesses in Nigeria managed their working capital in such a manner that it had a significant influence on financial performance during the study period, but listed industrial products firms did not manage their working capital adequately. The research proposed, among other things, that both publicly traded consumer and industrial product companies work more to recruit the help of factoring agents, since this will minimize the frequency of bad debt losses and other credit-related costs.

Puji syukur kehadirat Tuhan Yang Maha Esa atas segala rahmatNYA sehingga makalah ini dapat tersusun hingga selesai. Terima kasih pada Bapak Dr. Wirmie Eka Putra, S.E., M. Si., selaku dosen mata kuliah penganggaran perusahaan. Terima kasih kepada pihak-pihak yang membantu dalam penyelesaian makalah ini. Tidak lupa saya juga mengucapkan banyak terima kasih atas bantuan dari pihak yang telah berkontribusi dengan memberikan sumbangan baik materi maupun pikirannya. Dan harapan saya semoga makalah ini dapat menambah pengetahuan dan pengalaman bagi para pembaca, Untuk ke depannya dapat memperbaiki bentuk maupun menambah isi makalah agar menjadi lebih baik lagi. Karena keterbatasan pengetahuan maupun pengalaman, saya meyakini bahwa masih banyak kekurangan dalam makalah ini, oleh karena itu kami sangat mengharapkan saran dan kritik yang membangun dari pembaca demi kesempurnaan makalah ini.

A non-stationary stopped decision process is investigated under rather weak convergence assumptions on The expected total rewards. Suffizicnt conditions are given for the approximation of the maximal conditiondl expected rewards from infinite stage pldy by the maximal conditional expected rewards from finite stage play. General criteri: sf optimality are derived. The results are essentially based on two lemmas given in this paper. The existence of optimal plans is.established using results of non-stationary dynamic programming.

A non-stationary stopped decision process is investigated under rather weak convergence assumptions on the expected total rewards. Sufficient conditions are given for the approximation of the maximal conditional expected rewards from infinite stage play by the maximal conditional expected rewards from finite stage play. General criteria of optimality are derived. The results are essentially based on two lemmas given in this paper. The existence of optimal plans is established using results of non-stationary dynamic programming.

A non-stationary stopped decision process is investigated under rather weak convergence assumptions on The expected total rewards. Suffizicnt conditions are given for the approximation of the maximal conditiondl expected rewards from infinite stage pldy by the maximal conditional expected rewards from finite stage play. General criteri: sf optimality are derived. The results are essentially based on two lemmas given in this paper. The existence of optimal plans is.established using results of non-stationary dynamic programming.

In light of recent food safety crises and international trade concerns associated with food or animal associated diseases, traceability has once again become important in the minds of public policymakers, business decision makers, consumers and special interest groups. This study reviews studies on traceability, government regulation and consumer behaviour, provide case studies of current traceability systems and a rough breakdown of various costs and benefits of traceability. This report aims to identify gaps that may currently exist in the literature on traceability in the domestic beef supply chain, as well as provide possible directions for future research into said issue. Three main conclusions can be drawn from this study. First, there is a lack of a common definition of traceability. Hence identifying similarities and differences across studies becomes difficult if not impossible. To this end, this study adopts CFIA’s definition of traceability. This definition has been adopt...

This comprehensive academic reference explores the intersection of fuzzy
mathematics, graph theory, and optimization techniques in inventory
management systems. The book provides a rigorous treatment of fuzzy set theory foundations, fuzzy graph applications in decision systems, classical and fuzzy inventory models, optimization techniques, and advanced applications including sustainable inventory management and AI-enhanced systems.
Designed for graduate students, researchers, and practitioners in operations research, supply chain management, and applied mathematics, this monograph bridges theoretical foundations with practical applications. Each chapter follows a uniform structure comprising introduction, literature review, conceptual framework, technical analysis, visual representation, tabular data, and conclusion.
The reference list features seminal contributions from the field, with particular emphasis on the works of A.K. Sharma and collaborators, alongside foundational works by Lotfi Zadeh, Dubois and Prade, Rosenfeld, and other pioneers in fuzzy mathematics and operations research.

This study looks at the rising difficulty of inventory control for items that go bad. These items include drugs, fresh food, and tech parts that lose value over time. The main goal is to create a plan that makes order sizes better. This plan considers both physical decay and the need to meet time-based market demand. Using mathematical models, the study builds on the basic Economic Order Quantity (EOQ) method. It adds demand that changes over time and different rates of decay. The method uses math to find functions that lower costs. It also tests how well the model holds up when market conditions change. The results show that normal static inventory models greatly overestimate the best stock levels for things that spoil. This leads to higher holding costs and waste. But, the Smart Strategy presented matches purchase cycles with how fast the product breaks down. This lowers total system costs by 15-20%. The study finds that in markets where time is important, inventory control must change. Instead of just refilling stock, it needs to predict and sync with time. This work adds to the field by linking EOQ models with the real problems of fast product aging. It gives a solution that can grow for supply chain managers facing today's market changes.

Efficient management of inventory is essential for businesses handling perishable or timesensitive products. This study delves into advanced inventory models incorporating time-varying demand, pricing dynamics, and product deterioration. Drawing on a comprehensive review of research conducted by A. Sharma and collaborators between 2016 and 2024, the article presents strategic approaches to optimize inventory levels, reduce operational costs, and enhance overall supply chain performance. It emphasizes the role of dynamic pricing mechanisms, partial backordering, and accurate demand forecasting in developing responsive inventory control systems. The findings offer valuable insights for retailers, manufacturers, and logistics professionals while outlining promising avenues for future research in this evolving domain.

Spare part management and inventory control is the way how to manage and control spare part in optimum ways. The spare parts itself are needed for maintaining and repairing the machine in order to run the production smoothly. The variability of demand of spare parts are commonly happened that create uncertainty condition in spare part. It means, those conditions can be defined as multiperiod inventory system with probability approach. In PT.XYZ, the condition of spare part is usually become the causes of Line Stop that already know as prohibitively expensive conditions. The Line Stop that caused by spare part are recorded equal to 10.63% from the average total of Machine Stop percentage. In this case, Line Stop percentage can be reduced by providing an ideal spare part warehouse. An ideal spare part warehouse should have Right Part, Right Time, and Right Quantity. Those of criteria will be provided by using Vital-Essential-Desirable (VED) Analysis, Fast Moving-Slow Moving-Nonmoving (FSN) Analysis as a spare part management methods, and Periodic Review (P) system as a spare part inventory control methods. As a result, Spare part management and inventory control already success to reduce 10 % on average of Line Stop after proved in 14 main machines in assembling shop.

Due to the perishable nature of agricultural product the Inventory management of Agro-based industry is a challenging tax. EOQ model and EPQ model has its importance & role for proper management of this category of Inventory. The success of many industries is related to their ability to provide goods and services at right time and in right place. Different organization adopt different inventory control methods to manage their inventory to avoid stock-out and overstock. This paper analyses possible parameters of existing literature, concentration, description of characteristics and of EOQ inventory control model and EPQ model that have been developed and can solve the Problem in this field & will provide maximum profit with the customer satisfaction.

A method is proposed for forecasting composite time series such as the market shares for multiple brands. Its novel feature is that it relies on multi-series adaptations of exponential smoothing combined with the log-ratio transformation for the conversion of proportions onto the real line. It is designed to produce forecasts that are both non-negative and sum to one; are invariant to the choice of base series in the log-ratio transformation; recognize and exploit features such as serial dependence and non-stationary movements in the data; allow for the possibility of non-uniform interactions between the series; and contend with series that start late, finish early, or which have 1 We wish to thank Ward's Auto Group ( ) for making the data used in this paper available. values close to zero. Relying on an appropriate multivariate innovations state space model, it can be used to generate prediction distributions in addition to point forecasts and to compute the probabilities of market share increases together with prediction intervals. A shared structure between the series in the multivariate model is used to ensure that the usual proliferation of parameters is avoided. The forecasting method is illustrated using data on the annual market shares of the major (groups of) brands in the U.S. automobile market, over the period 1961 -2013.

A method is proposed for forecasting composite time series such as the market shares for multiple brands. Its novel feature is that it relies on multi-series adaptations of exponential smoothing combined with the log-ratio transformation for the conversion of proportions onto the real line. It is designed to produce forecasts that are both non-negative and sum to one; are invariant to the choice of base series in the log-ratio transformation; recognize and exploit features such as serial dependence and non-stationary movements in the data; allow for the possibility of non-uniform interactions between the series; and contend with series that start late, finish early, or which have 1 We wish to thank Ward's Auto Group ( ) for making the data used in this paper available. values close to zero. Relying on an appropriate multivariate innovations state space model, it can be used to generate prediction distributions in addition to point forecasts and to compute the probabilities of market share increases together with prediction intervals. A shared structure between the series in the multivariate model is used to ensure that the usual proliferation of parameters is avoided. The forecasting method is illustrated using data on the annual market shares of the major (groups of) brands in the U.S. automobile market, over the period 1961 -2013.

Modern supply chain management requires a replacement of fixed Economic order quantity (EDO) models with dynamic systems that take into consideration the physical and financial realities of the contemporary market. The current study summarises the current development in inventory modelling in terms of the most critical issues of deterioration, the changing demand patterns, and financial factors. Combining a time-varying demand with reliability and price sensitivity, this article is one that elucidates the usefulness of mathematical optimisations and strategic payment schemes in improving the performance of the supply chain.

Inventory modeling is key to running an efficient supply chain. This paper looks at how to combine the Economic Order Quantity (EOQ) model with ways to guess demand, to control stock amounts for common and perishable items. The main aim is to link older, simple models with today's fast-changing markets. The way this study works is by comparing different things. It mixes math models with methods of predicting future patterns using things like ARIMA to test resupply plans. The data shows that the standard EOQ model gives a good starting point, but it gets much better when demand changes based on time and how sensitive prices are. In particular, using forecasts helps to lower both storage costs and the chance of running out of stock, especially when demand rises slowly or follows a pyramid shape.
Therefore, a combined forecasting-inventory method is needed to stay competitive. This study helps the area by giving a clear view of how predictions can make classic inventory ideas better, giving a solid plan for people in the field who want to lower total costs when the economy changes.

Effective inventory management is crucial for businesses dealing with perishable or deteriorating goods. This research article examines various inventory models that account for time-dependent demand, price fluctuations, and product deterioration. By synthesizing findings from multiple studies by Sharma and colleagues (2015-2024), this paper explores key strategies for optimizing stock levels, minimizing costs, and improving supply chain efficiency. The analysis highlights the importance of dynamic pricing, fractional backlogging, and demand forecasting in inventory control systems. Practical implications for retailers, manufacturers, and logistics managers are discussed, along with future research directions.

This study explores a math system for improving the stock of things that break down over time, all while dealing with current environment rules and what people think. The main aim is to balance making money with being eco-friendly, paying close attention to how prices change with stock levels and limits on carbon emissions. Unlike older plans that guess demand is steady or don't think about the environment cost of keeping and throwing away items that go bad, this looks at the whole picture. We use math equations to show how stock goes down because of sales and decay over time. The way we things look at the total cost, like carbon taxes on keeping goods and getting rid of waste. Computer tests show that a way of thinking where having stock on display boosts sales needs to be carefully balanced against faster decay and carbon fines for having too much stock. The results show that using prices that can change allows for more freedom in how much profit can be made, mainly when dealing with changes in demand. The work ends by saying that an eco-friendly plan doesn't just meet rules but makes the whole supply chain stronger by cutting down on physical losses. This gives useful ideas for managers in medicine and food who have to keep service levels high while meeting green business goals.

In this paper, we address the problem of dynamic pricing to optimize the revenue coming from the sales of a limited inventory in a finite time-horizon. A priori, the demand is assumed to be unknown. The seller must learn on the fly. We first deal with the simplest case, involving only one class of product for sale. Furthermore the general situation is considered with a finite number of product classes for sale. In particular, a case in point is the sale of tickets for events related to culture and leisure; in this case, typically the tickets are sold months before the event, thus, uncertainty over actual demand levels is a very a common occurrence. We propose a heuristic strategy of adaptive dynamic pricing, based on experience gained from the past, taking into account, for each time period, the available inventory, the time remaining to reach the horizon, and the profit made in previous periods. In the computational simulations performed, the demand is updated dynamically based on the prices being offered, as well as on the remaining time and inventory. The simulations show a significant profit over the fixed-price strategy, confirming the practical usefulness of the proposed strategy. We develop a tool allowing us to test different dynamic pricing strategies designed to fit market conditions and seller's objectives, which will facilitate data analysis and decision-making in the face of the problem of dynamic pricing.

Persediaan merupakan elemen penting dalam menjaga keberlangsungan produksi industri manufaktur. Kekurangan stok dapat menghentikan proses produksi dan menimbulkan kerugian finansial, sementara kelebihan stok menyebabkan pembengkakan biaya penyimpanan dan inefisiensi modal kerja. Buku ”Manajemen Stok Cerdas (Optimasi Bahan Baku dengan Metode Periodic Review System)” membahas secara sistematis tantangan pengelolaan persediaan di era industri modern, mencakup konsep dasar manajemen persediaan, jenis dan biaya persediaan, faktor yang memengaruhi tingkat stok, serta peran peramalan dalam menghadapi ketidakpastian permintaan dan lead time. Buku ini menguraikan penerapan metode Periodic Review System dalam pengendalian bahan baku melalui studi kasus pengelolaan ZA (Amonium Sulfat) pada produksi pupuk NPK. Pembahasan meliputi karakteristik bahan baku, perhitungan interval pemesanan, penentuan tingkat persediaan maksimum, analisis biaya, serta evaluasi kebijakan persediaan. Struktur penyajian disusun secara sistematis dan aplikatif sehingga memberikan gambaran komprehensif mengenai pengelolaan stok berbasis pendekatan kuantitatif di lingkungan industri manufaktur.

The objective of this article is to propose an improvement for all those companies that have problems of inventories or of maximum stock; Allowing to implement or develop other inventory techniques based on statistical tools. A convenient control of the inventory in an original company or organization an easy procedure of study of the products and to manage the whole level of supply of the products finished to the clients. In this sense, this article proposes the realization by means of the Pareto diagram and the classification ABC, the improvement of inventory in a case of study (a manufacturing company and Comercializadora) that allows seeing the problem of pending orders, sum of quantities Outstanding and the sum of total losses. With this case will identify products that are the little vital and the many trivial in a company fulfilling the theory of Vilfredo Pareto or also known as the 80-20 and the distribution ABC of the products

Scientific Machine Learning is built on the science-of-counting, is deductively solvable, and is well-suited to business and human applications that naturally involve count. From the Gibbs formalism, Scientific Machine Learning produces unique and exact scientific measurements that define the state of the time-series. Time-series itself defines a geometric structure tailor made for geometric projections, optimization and decision making. Inventory sales management decisions will demonstrate Scientific Machine Learning without introducing models or model bias.

Inventory management is an extremely difficult task. It has become usual practice for a provider during the last few decades to provide a retailer with a credit term. In this article, a non-instantly degradable product inventory system is built with a price-sensitive demand and a Weibull credit term allocation reduction rate. Some backlogged deficiencies are permitted. The aim is to maximize the total profit by taking three cases into account. Numerical examples, graphical representations and sensitivity analysis demonstrate the application of the approach developed in this study.

Supply chain disruptions have intensified dramatically in recent years, exposing critical vulnerabilities in traditional procurement and production planning systems. Organizations increasingly recognize that isolated, siloed enterprise resource planning modules cannot adequately respond to volatile demand patterns, supplier uncertainties, and operational complexities. This research examines how integrated synchronization between SAP Production Planning (PP) and Procurement modules enhances supply chain resilience through improved visibility, coordinated decision-making, and adaptive responses to disruptions. Through a mixed-methods study involving implementation analysis at five manufacturing organizations and quantitative assessment of key performance indicators over 18 months, this research demonstrates that integrated SAP PP-Procurement synchronization reduces stockout incidents by 42%, decreases emergency procurement costs by 38%, and improves on-time delivery performance by 31% compared to traditional siloed approaches. The study develops a comprehensive integration framework encompassing master data harmonization, real-time information exchange, collaborative planning processes, and exception management protocols. Findings reveal that successful integration requires not only technical configuration but also organizational change management, cross-functional collaboration, and continuous process refinement. The research identifies critical success factors including executive sponsorship, clear data governance, standardized workflows, and balanced automation with human oversight. This work contributes both a validated integration methodology for SAP PP-Procurement synchronization and empirical evidence of its impact on supply chain resilience, providing practical guidance for organizations seeking to strengthen their operational capabilities against disruptions.

Global supply chains increasingly operate under persistent supply shortages driven by geopolitical disruptions, pandemics, and capacity constraints. During such periods, allocation decisions directly influence revenue realization, service continuity, and customer trust. From our research and simulation work, we observed that many organizations still rely on manual or rule-based allocation methods, which often lead to inconsistent decisions and lost revenue during shortages. In this paper, we present a prioritized revenue, AIdriven global allocation framework designed to optimally distribute constrained inventory across regions. Based on our direct implementation and simulation experience, the proposed approach integrates demand forecasts, historical revenue contribution, and strategic location priorities into a mathematically grounded optimization model. We used simple weighting and proportional allocation so that the results are easy to understand, explain, and audit. Simulation results across multiple shortage scenarios show that the proposed method consistently improves revenue realization compared to traditional allocation approaches, while maintaining fairness and operational feasibility.Through this research, we provide a practical and implementable allocation model that organizations can directly apply within their existing planning and ERP systems to manage product shortages more effectively.

Disusun Oleh:
Niya Zelva (23050061) Zeti Dwi Padila (23050092) Nur Hadiah Ritonga (23050065) Azizah Putri (23050015) Nur Baiti (23050063),
Dosen Pengampu:
Apt. Cory Linda Futri Hrp, M. Farm,

Abstrak: Salah satu pelayanan unggulan di RSMH adalah pelayanan Kemoterapi Terpadu. Sebagai salah satu sumberdaya dalam mendukung pelayanan tersebut adalah persediaan farmasi, yang termasuk di dalamnya adalah obat Kemoterapi yang relative mahal. Di RSUP Dr. Mohammad Hoesin Palembang, didalam melakukan pengendalian persediaan obat, belum mengklasifikasikan obat berdasarkan nilai pemakaian, nilai investasi dan kekritisan obat melalui metode tertentu. Maka dalam penelitian ini bertujuan untuk melakukan pengendalian obat kemoterapi melalui pendekatan Analisis ABC Indeks Kritis di ruang pencampuran Kemoterapi Instalasi Farmasi RSUP Dr. Mohammad Hoesin Palembang. Penelitian ini menggunakan desain riset operasional dengan analisis deskriptif kuantitatif dan kualitatif. Hasil penelitian menunjukkan bahwa dari 83 item obat, untuk kelompok A(60%-30%-10%) item obat kemoterapi adalah Paxus 100 mg injeksi. dengan nilai investasi sebesar Rp. 3.220.525.650,- dari total investasi Rp 33.509.826.356,-. Analisis Indeks Kritis kelompok A (70%-20%-10%) dengan nilai investasi sebesar Rp 11.045.150.780,- dari keseluruhan nilai investasi Rp 33.509.826.356,- terdiri dari 6 jenis item yaitu: Holoxan 1 gram injeksi, Leocovorin injeksi, Doxorubicin 50 mg injeksi, Brexel 20 mg injeksi, Brexel 80 mg injeksi dan Paxus 100 mg injeksi. Sedangkan kelompok A (80%- 10%-10%) atau senilai Rp. 12.472.877.428,- dari total nilai investasi Rp. 33.509.826.356,- terdiri dari 8 item yaitu: Carboplatin 150 mg injeksi Holoxan 1 gram injeksi, Leocovorin injeksi, Doxorubicin 50 mg injeksi, Brexel 20 mg injeksi, Brexel 80 mg injeksi dan Paxus 100 mg injeksi, dan Taxotere 20
mg injeksi. Metode Analisis ABC Indeks Kritis ini dapat membantu rumah sakit dalam merencanakan kebutuhan obat dengan mempertimbangkan pemakaian, nilai investasi, kekritisan obat untuk melakukan efisiensi biaya rumah sakit.

This paper studies the computation of order up to levels for a stochastic programming inventory problem of a perishable product. Finding a solution is a challenge as the problem enhances a perishable product, fixed ordering cost and non-stationary stochastic demand with a service level constraint. An earlier study [6] derived order-up-to values via an MILP approximation. We consider a computational method based on the so-called Smoothed Monte Carlo method using sampled demand to optimize values. The resulting MINLP approach uses enumeration, bounding and iterative nonlinear optimization.

Most of the countries are surrounded by ocean. In ocean, various sea foods are available. Sea foods are very useful for human body. It is necessary to supply sea foods to each and every person from sea area to the various destination in the country. In this article, supply chain coordination model for seafood was developed and sales revenue, material cost, holding cost, profit for the supply chain with an illustrative real-life example. .