CONTROL-RELEVANT DEMAND MODELING FOR SUPPLY CHAIN MANAGEMENT

Simulation-based optimization of process control policies for inventory management in supply chains

Comparing the value of information sharing under different inventory policies in construction supply chain

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

With the acceleration of globalization and the increasingly fierce competition in the market, demand forecasting and inventory management in supply chain management have become particularly important. Traditional supply chain management methods are difficult to cope with the complex and changing market demand, resulting in the frequent occurrence of inaccurate forecasts, excessive or insufficient inventory, and other problems. This paper aims to explore the application of intelligent technology in supply chain demand forecasting and inventory management, focusing on analyzing how technologies such as big data, artificial intelligence, and machine learning can improve forecasting accuracy and inventory management efficiency. Through the analysis of actual cases, this paper demonstrates the implementation effect of intelligent demand forecasting technology and proposes corresponding inventory management optimization strategies. The study shows that intelligent supply chain management can not only effectively reduce operating costs, but also improve service levels, thus enhancing the competitiveness of enterprises in a dynamic market environment

The rising cost of quality healthcare is becoming an increasing concern. A significant part of healthcare cost is the pharmaceutical supply component. Improving healthcare supply chains is critical not only because of the financial magnitude but also because it impacts so many people. Efforts such as this project are essential in understanding the current operations of healthcare pharmacy systems and in offering decision support tools to managers struggling to make the best use of organizational resources. The purpose of this study is to address the objectives of a local hospital that exhibits typical problems in pharmacy supply chain management. We analyze the pharmacy supply network structure and the different, often conflicting goals in the decisions of the various stakeholders. We develop quantitative models useful in optimizing supply chain management and inventory management practices. We provide decision support tools that improve operational, tactical, and strategic decision making in the pharmacy supply chain and inventory management of pharmaceuticals. On one hand, advanced computerized technology that manages pharmaceutical dispensation and automates the ordering process offers considerable progress to support pharmacy product distribution. On the other hand, the available information is not utilized to help the managers in making the appropriate decisions and control the supply chain management. Quantitative methods are presented that provide simplified, practical solutions to pharmacy objectives and serve as decision support tools. For operational inventory decisions we provide the min and max par levels (reorder point and order up to level) that control the automated ordering system for pharmaceuticals. These parameters are based on two near-optimal allocation policies of cycle stock and safety stock under storage space constraint. For the tactical decision we demonstrate the influence of varying inventory holding cost rates on setting the optimal reorder point and order quantity for items. We present a strategic decision support tool to analyze the tradeoffs among the refill workload, the emergency workload, and the variety of drugs offered. We reveal the relationship of these tradeoffs to the three key performance indicators at a local care unit: the expected number of daily refills, the service level, and the storage space utilization.

The context and importance of inventory management and production planning -- The importance of inventory management and production planning and scheduling -- Frameworks for inventory management and production planning and schedul-ing -- Forecasting models and techniques -- Replenishment systems for managing individual item inventories within a rm -- Order quantities with level demand -- Lot sizing with time-varying demand -- Individual items with probabilistic demand -- Special classes of items -- Managing the most important inventories -- Managing slow-moving and low-value (Class C) inventories -- Style goods and perishable items -- Managing inventory across multiple locations and multiple rms -- Coordinated replenishments at a single stocking point -- Multiechelon inventory management -- Coordinating inventory management in the supply chain -- Production management -- An overall framework for production planning & scheduling -- Medium-range aggregate production planning -- Material requirements planning and its extensions -- Just-in-time, optimized production technology and short range pro-duction scheduling -- Summary.

Contributors

Improving the efficiency of inventory management in supply chains remains relevant for industrial, commercial, service and other enterprises. The results of a supply chains survey (distribution center - stores) with weekly deliveries throughout the year demonstrated that inventory management strategies may be classified into three groups: smooth (more than 47 weeks per year deliveries are recorded), low (from 20 to 46 weeks per year), and sporadic and lumpy (less than 20 weeks per year). The conducted studies have shown that for three groups of inventory management strategies in supply logistics, it is advisable to form three groups of calculation models that allow obtaining quantitative estimates of supply amount and making decisions under conditions of certainty, risk and uncertainty. The first group of calculation models includes Last period or Naive, as well as Exponential smoothing with trend, Multiple regression, etc. The second group consists of combined models, including estimates of the first group and expert estimates under risk conditions. The third group involves models based on decision-making under conditions of uncertainty. The proposed approach allows making reasonable management decisions based on the identification of the demand type to improve the efficiency and reliability of the supply chain.

Economic model predictive control for inventory management in supply chains

Reducing carbon emissions is an important issue in the global supply chain. Governments have formulated different carbon policies to achieve carbon neutrality or emission reduction, in particular, the carbon tax as a policy provides greater certainty about the potential cost to emitters. The Taiwan government is expected to implement the carbon tax policy in 2024 and does not rule out the inclusion of a progressive mechanism which is more effective than a single carbon tax in curbing high carbon emissions Recent studies have also confirmed that a high level and complete carbon tax could encourage companies to invest in green technologies, which can reduce emissions while reducing internalization costs. In addition, scholars have proposed that the transportation process has a significant impact on carbon emissions. Therefore, in this paper, we combine with the EOQ model of the integrated supply chain under the pro[1]gressive carbon tax, transportation cost, and green technology investment, the proposed model can help decision makers determine the optimal order quantity and green technology investment costs to minimize total costs and carbon emissions. It is also for the government to the trade-off between the carbon tax system and economic activities. Finally, numerical results are discussed to provide managerial insights.

The design of ordering policies for inventory management in supply chains has traditionally relied on the proportional feedback control structure as a foundation for the design process. Consequently, the impact of ordering policies that use both proportional–integral–derivative feedback control and feedforward control has been relatively unexplored. We evaluate the impact of ordering policies based on the combined control structure on supply chain stability and responsiveness. Copyright © 2011 John Wiley & Sons, Ltd.

Introduction to distribution logistics

Consignment policy (CP) is a novel approach to the inventory management in supply chains. It is based on strong interaction and reliable collaboration between vendor(s) and buyer(s), which is acquiring growing importance in today's industrial reality. Unlike most literature focusing on single-vendor single-buyer models and deterministic customer demand, a single-manufacturer (vendor) multi-retailer (buyer) generic model is developed under stochastic customer demand in this study. In order to understand the potential benefits of CP, it is compared with a traditional policy (TP) model developed in the similar approach. The models are tested with two scenarios of uniform and exponential demand distributions of the retailers. The results show how CP works better than the traditional uncoordinated optimization. It not only helps the manufacturer to generate higher profit, but also coordinates retailers to achieve a higher supply chain profit. At the same time, each retailer earns at least as much as they do in TP. Further price discount sensitivity analysis demonstrates the efficiency of CP when facing price-demand fluctuation.

Inventory management and control is concerned with the acquisition and storage of materials required for supporting various business operations. Lead time reduction is another important production activity in an integrated inventory control. Lead time plays a vital role and has been a topic of interest for many authors in inventory organization. Lead time is a necessary aspect in any supply chain management and inventory management system. The time gap between placing of an order and its actual arrival in the inventory is known as lead time. In most of the literature dealing with inventory problems, either in deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable, which therefore, is not subject to control. But, in numerous sensible circumstances, lead time can he reduced by an additional crashing cost; in other words, it is controllable, so in this article, we have considered the lead time crashing cost is an exponential function of lead time. A solution procedure is developed to find the optimal solution. In this article, both lead time and the order quantity are considered as the decision variables. The behaviour of the model is presented graphically. The result is illustrated with the help of a numerical example. Finally, Graphical representation is presented to illustrate the model. The solution procedure with the help of the software Matlab 2008 is furnished to determine the optimal solution.

In today’s volatile supply chain (SC) environment, competition has shifted beyond individual companies to the entire SC ecosystem. Reducing overall SC costs is crucial for success and benefits all participants. One effective approach to achieve this is through digital transformation, enhancing SC coordination via information sharing, and establishing decision policies among entities. However, the risk of unauthorized leakage of sensitive information poses a significant challenge. We aim to propose a Privacy-preserving Multi-agent Reinforcement Learning (PMaRL) method to enhance SC visibility, coordination, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning with additional SC connectivity information to improve training performance. The simulation-based evaluation results illustrate that the PMaRL method surpasses traditional optimization methods in achieving cost performance comparable to full visibility methods, all while preserving privacy. This research addresses the dual objectives of information security and cost reduction in SC inventory management, aligning with the broader trend of digital transformation.