Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Impact of uncertainties of lead times and expiration dates on the stability of inventory levels in a distribution system

International audienceIn this paper, we discuss the impact of uncertainties of lead times and expiration dates on the stability of the inventory regulation problem in productions systems using feedback control law structure, in the conception phase. The inventory control system is considered as an input-delay system with uncertainties on customer demands, and positive constraints due to the specifications of the agricultural supply chain. Also, the system is characterized by the presence of delay due to the process time and the distribution time, and the perishable products are modeled by a fixed preemption rate. We have first found the necessary and sufficient conditions that prove the existence and the admissibility of the control law. Secondly, a comparative analysis of impact of production delay and expiration date uncertainties on a robust design is given. Copyright c 2019 IFA

ISIS and META projects

The ISIS project has developed a new methodology, virtual synchony, for writing robust distributed software. High performance multicast, large scale applications, and wide area networks are the focus of interest. Several interesting applications that exploit the strengths of ISIS, including an NFS-compatible replicated file system, are being developed. The META project is distributed control in a soft real-time environment incorporating feedback. This domain encompasses examples as diverse as monitoring inventory and consumption on a factory floor, and performing load-balancing on a distributed computing system. One of the first uses of META is for distributed application management: the tasks of configuring a distributed program, dynamically adapting to failures, and monitoring its performance. Recent progress and current plans are reported

Design Guidelines For Digital Kanban Systems With High Service Level

One success factor of Kanban is its elegant simplicity for physical inventory control. However, especially in multi-variant productions inventory levels are digitally tracked. To maintain the high service levels of the Kanban systems, the digital representation in the ERP must reliably reflect the physical inventory levels and deviations should be detectable. The design of such a digitally tracked Kanban systems requires a booking logic and a method for deviation detection. Especially in multi-stage systems with several inventory levels, the design of a simple and robust Kanban logic is challenging. Thus, the paper first gives an overview of existing inventory booking strategies. Based on the strategies the effects of inventory deviations on logistical performance in classic Kanban and digitally controlled Kanban systems are discussed. Design guidelines summarize the analysis. Subsequently, three different design alternatives of a classical, digital and high resolution Kanban system are developed. These guidelines and design alternatives should enable practitioners to setup reliable Kanban systems including their digital representation

Подавление влияния ограниченных внешних возмущений в системе управления запасами цепи поставок

Предложен подход к решению задачи построения стабилизирующего управления запасами в цепях поставок. Для подавления влияния возмущений, моделирующих изменения неизвестного, но ограниченного внешнего спроса, одновременно с обеспечением устойчивости замкнутой системы, применена методика инвариантных эллипсоидов, которая позволила сформулировать задачу в терминах линейных матричных неравенств, а синтез управления свести к последовательности задач одномерной выпуклой оптимизации и полуопределенного программирования.An approach to solving the problem of stabilizing inventory control synthesis for supply chains is proposed. To disturbances rejection simulating unknown but bounded external demand, while ensuring robust stability of the closed-loop system, was used the invariant ellipsoids technique, which allowed to formulate the control problem in terms of linear matrix inequalities. As a result the control synthesis problem was reduced to a sequence of onedimensional convex optimization problems and semidefinite programming

Anticipatory Inventory Management For Realizing Robust Production Processes In Engineer-To-Order Manufacturing: A Modeling Approach

At ever shorter intervals, manufacturing and processing companies of all industries are confronted with external or internal disruptions and crises that need to be managed. Consequently, a corporate focus on robust supply chains and processes is essential. At the same time, crises and their impact on supply chains cannot be predicted. To be able to act anticipatively, it is necessary to link product and production system design to take suitable measures to safeguard production at an early stage. In this context, a monetary conflict of objectives arises concerning when a company should position itself robustly and when it is sufficient to react flexibly to disruptions. The production planning and control (PPC) task inventory management is an essential lever for realizing robust order fulfilment processes. Inventory management aims to ensure that production and assembly within the company are supplied in the right quantities and without lateness. In particular, companies that operate according to the engineer-to-order strategy (ETO) face specific challenges in dimensioning stocks for materials or components - for example, due to the low level of standardization or lack of supplier diversity. This paper presents an approach for anticipatory inventory management using product portfolio characteristics. A new modeling approach for dimensioning safety stocks under the increasing influence of crises is also developed and integrated into the process

Methodology for Analyzing and Characterizing Error Generation in Presence of Autocorrelated Demands in Stochastic Inventory Models

Most techniques that describe and solve stochastic inventory problems rely upon the assumption of identically and independently distributed (IID) demands. Stochastic inventory formulations that fail to capture serially-correlated components in the demand lead to serious errors. This dissertation provides a robust method that approximates solutions to the stochastic inventory problem where the control review system is continuous, the demand contains autocorrelated components, and the lost sales case is considered. A simulation optimization technique based on simulated annealing (SA), pattern search (PS), and ranking and selection (R&S) is developed and used to generate near-optimal solutions. The proposed method accounts for the randomness and dependency of the demand as well as for the inherent constraints of the inventory model. The impact of serially-correlated demand is investigated for discrete and continuous dependent input models. For the discrete dependent model, the autocorrelated demand is assumed to behave as a discrete Markov-modulated chain (DMC), while a first-order autoregressive AR(1) process is assumed for describing the continuous demand. The effects of these demand patterns combined with structural cost variations on estimating both total costs and control policy parameters were examined. Results demonstrated that formulations that ignore the serially-correlated component performed worse than those that considered it. In this setting, the effect of holding cost and its interaction with penalty cost become stronger and more significant as the serially-correlated component increases. The growth rate in the error generated in total costs by formulations that ignore dependency components is significant and fits exponential models. To verify the effectiveness of the proposed simulation optimization method for finding the near-optimal inventory policy at different levels of autocorrelation factors, total costs, and stockout rates were estimated. The results provide additional evidence that serially-correlated components in the demand have a relevant impact on determining inventory control policies and estimating measurement of performance