Replenishment Cycle Research Articles

Optimizing and evaluating the performance of integrated supply production centers: A hybrid heuristic-simulation applied to olive oil waste circular supply chains

This study examines the potential of an integrated bio-commodity supply-production center. Previous research has demonstrated the financial viability of circular bio-economies through Mixed Integer Linear Programming (MILP) or simulation. However, these models 1) do not incorporate the necessary logistics operations for biomass collection and storage, 2) do not integrate and optimize those with the requirements of multi-products line lot sizes. A Geographic Information System (GIS) based model is proposed, integrating insertion and genetic algorithms with a hybrid (ABM-DES) simulation model. A case study in Greece's Fthiotida region, a hub for olive cultivation, investigates the production of pellets, olive cakes, and pomace oil from prunings and Two-Phase Olive Mill Waste (TPOMW). The findings suggest that the model allows for a more accurate estimation of impacts when considering the supply and production operations. In particular, it shows how the frequency and lot sizes of replenishment cycles, influenced by the varying numbers of mechanized harvesters employed, significantly impacts the fluctuation observed in the return on investments. Running the model, it is found that the optimized system with two harvesters can reach a breakeven point, within just 36 months, and corresponding emissions of about 622,041 kgCo2eq. This provides a benchmark for other olive industries to harness unexploited resources.

A Study on the Production-Inventory Problem with Omni-Channel and Advance Sales Based on the Brand Owner’s Perspective

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions.

Waste management of multiple food products through IoT enabled preservation policies and secondary supply chains

Recycling food waste (FW) into secondary products can transform the waste-to-value. This study proposes reducing FW through IoT controlled preservation and consuming it through a secondary supply chain (SSC). It explores the best policies for SSC management that recycles FW. The food chain comprises of multiple food processors for preparing multiple food products, and a common retailer where IoT based preservation curtails food deterioration. The SSC consumes the FW by recycling into multiple secondary products at multiple recycling plants and retailing at a secondary retailer. A dual channel waste collection viz. from retailers and consumers is maintained. Each product within primary and secondary supply chains (PSSCs) has a different lifetime, rate of deterioration (RoD), and preservation effectiveness. A nonlinear mathematical model is presented that optimizes decision-making for maximizing profitability. Outcomes of computational experiments demonstrate that the SSC removes 100% of the FW, conserves 89% of material resources, reduces 16% of the total cost, minimizes preservation efforts by 50%, improves replenishment cycle, and increases profitability by 39%. Some managerial insights are provided for making vital supply chain decisions.

A Study on the Construction and Evaluation of the Water Resource Reutilization System for Farmland Diversion and Drainage

Water is an essential resource for both rural and agricultural areas; it can be wisely distributed and used in the field to protect daily life, production, the natural environment, and the safety and stability of regional drainage and flood control systems. Our research selected a typical plains rural river network area with agriculture as the main industry to investigate the most effective method of farmland diversion and drainage. We comprehensively planned and transformed the water system flow, water conservation engineering, and the ecological environment in the irrigation area through the reutilization system. The reutilization system’s operation and scheduling design is implemented for four specific periods: the water replenishment cycle, agricultural irrigation, agricultural drainage and the rainy period of the flood season. The research period ranges from 2020 to 2023 after the completion of the system. We used monitoring, the recording of hydraulic equipment parameters and data collection to evaluate the balance of water supply and demand in the study area. At the same time, we have tracked and evaluated the four aspects of water quality enhancement, water conservation and flood control, and agricultural irrigation. The results show that the total agricultural water consumption decreased by 2.9%, and the amount of water saved increased by 9.6%. The current segment creates the rivers’ embankment standards. With a 92% irrigation guarantee rate, the current section forms and the embankment standards of the rivers satisfy the design storage volume and the flood level of one in twenty years. The water quality of all the rivers in the area has decreased by 5~10% compared to the average concentration prior to establishment. This study verifies the comprehensive effect and the suitability of the system by comparing the before and after effects, and provides a scientific basis for the method of efficient recycling and utilization of water resources in the rural plains river network area; we also propose the guidance of increasing the digital twin control and long-term operation mechanism to ensure the long-term stable operation of the technology.

Simple heuristics for the joint inventory and pricing models with fixed replenishment costs

This study considers the joint inventory and pricing problem of a firm selling a single item over a multi-period planning horizon. A fixed replenishment cost is incurred whenever the replenishment decision is made. Period demands are considered to be non-stationary, stochastic, and price-dependent. The literature shows that the optimal policy in such a system is of an (s, S, p)-type. The implementation of this type of policy might be challenging in practice due to the prevailing computational difficulty in determining the optimal policy parameters. This study, therefore, aims to develop computationally efficient heuristic approaches for the joint inventory and pricing problem. The proposed heuristics provide different levels of flexibility in making inventory and pricing decisions. Furthermore, they are essentially built on the concept of the replenishment cycle and do not require solving a stochastic dynamic program. Our numerical study demonstrates the profit efficiency of the proposed heuristic approaches against the optimal policy. The results also indicate that firms lacking the technical ability to employ a dynamic pricing strategy might still gain significant profit improvement by only using a dynamic inventory control policy.

Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores

In today's competitive grocery retail environment, effective inventory management coupled with precise sales prediction is pivotal for success. This research undertakes an in-depth exploration into a machine learning framework tailored for "Sales Forecasting and Inventory Optimization in Grocery Retail." Utilizing historical sales records, meteorological data, and consumer behavior metrics, the proposed model endeavors to predict nuanced seasonal demand fluctuations. The primary value proposition of this approach lies in its capacity to curtail wastage, ensuring the balance between overstocking and inventory stockouts. By recognizing and adjusting for seasonal dynamics, the system offers enhanced demand fulfillment during high-demand periods. The subsequent refinement of the replenishment cycle fosters superior operational efficacy. As an outcome, businesses witness cost reductions, augmented profit margins, and elevated consumer contentment due to consistent product availability. As the broader grocery sector undergoes transformation, embedding sophisticated machine learning strategies can be a linchpin for sustained adaptability and competitiveness. This research further underscores the role of emergent technologies, such as machine learning within the IoT spectrum, in reshaping supply chain and communication paradigms. The endeavor here is to curtail lifecycle expenses in the supply chain by refining inventory practices. The research introduces a Deep Inventory Management (DIM) technique, employing the LSTM paradigm of deep learning. Through DIM's innovative approach, time-series challenges are reimagined as supervised learning tasks, enabling efficient model training. Preliminary tests indicate DIM's prediction accuracy hovers around 80%, translating to an impressive 25% reduction in inventory expenses when juxtaposed with prevailing methods, alongside rapid anomaly detection in inventory activities

A multi-period vaccines supply chain network design with capacity expansion and different replenishment cycles under uncertain demand

The World Health Organization (WHO) proposes guidelines for vaccine distribution networks that incorporate economic order quantity and risk pooling principles, specifically through implementing different replenishment cycles between different levels of the distribution network. The substantial impact of different replenishment cycles on inventory levels is demonstrated in the existing literature. However, current optimization network design research has paid limited attention to this aspect. This article aims to address this research gap by investigating a two-stage stochastic programming for a multi-period perishable vaccine network design problem, taking into account the different replenishment cycles characteristic and capacity expansion under uncertain demand conditions. Given the inherent difficulty of solving the proposed problem, a matheuristic approach based on Variable Neighborhood Search (VNS) is developed. To illustrate the practical application and analyze the impact of uncertain demand, we apply the proposed model and its solution method using the Indonesia dataset in the COVID-19 situation. The computational results indicate that lower replenishment cycles leads to higher inventory levels, total costs, and capacity requirements for the top-level distribution center. Optimum replenishment cycles and further managerial implications and future potential research are also presented.

A case study of the impact of carbon emissions and inflation on nonlinear dense neutrosophic fuzzy inventory system of varying demand with delayed deterioration

In a comprehensive consensus, human should reduce the carbon emission on the way to reduce the adverse effect of global warming. Manufacturing firms contributes significant amount of carbon emissions in the environment. In many countries, regulatory authorities are taking actions to reduce emissions. Thus, in this paper we have analysed the effect of carbon emissionfor non-instantaneous deteriorating items having advertisement and price dependent demand. The paper also focused on the effect of inflation where shortages are partially backlogged and partially lost in sales. In this paper a new concept of non-linear triangular dense neutrosophic number with its basic properties are developed. Further the classifications of symmetry, asymmetry is introduced and thereafter De-neutrosophication technique has been applied for crispification. Since the effect of some parameters like carbon emissions, advertising, and inflation are uncertain so we have considered it in this new aforesaid form to grab the uncertain characters of the underlying parameters. The classification of the uncertain parameter based on the symmetric and asymmetric nature and linear-nonlinear nature of triangular dense neutrosophic number have been also investigated here. Additionally, the effect of the model is examined under different situations for both linear and non-linear triangular dense fuzzy, dense intuitionistic, dense neutrosophic. Finally, a numerical example is considered to illustrate the model and it is observed that the model is optimum when the parameters are considered in Asymmetric Convex Non-Linear number for shorter replenishment cycle. Also, comparative study has been done by performing sensitivity analysis through the case study and provides managerial insight into this outcome.

Research on an Optimal Maintenance and Inventory Model Based on Carbon Tax Policy

The equipment in a factory will gradually deteriorate during production, leading to the production of defective products. Without appropriate maintenance, the defect rate will increase over time. Consequently, the production cost will rise, the inventory quality will be affected, the profit will decrease, and the risk of carbon emissions will increase, leading to more customer complaints and damaging the corporate image. In addition to focusing on preventive maintenance to ensure the quality of products, companies should also take carbon emissions into consideration. Furthermore, the frequency of maintenance must be carefully considered, as both carbon emissions and maintenance costs will increase if the frequency is too high; conversely, if the maintenance frequency is too low or non-existent, the defect rate may increase cumulatively, or production may be suspended due to equipment failure. Therefore, this research explores preventive maintenance and inventory management issues within an imperfect production system and develops an extended economic production quantity model that incorporates defective products as well as taking carbon tax and preventive maintenance into consideration. The main purpose is to determine the optimal maintenance frequency, production, and replenishment cycle length, so as to maximize the total profit under the carbon tax policy. This study demonstrates a computing process with relatively impractical product data based on the actual business situation of a disposable diaper manufacturer. Furthermore, a sensitivity analysis is implemented to the model parameters in the proposed model. The managemental insights are illustrated based on the results of theoretical analysis to provide a reference to policy makers during decision making, hence, to secure the sustainability and green transitions of corporates. The results of this study not only help to reduce environmental impact but can also improve the competitiveness and sustainable development of enterprises.

Dynamic pricing and replenishment policy under price, time, and service level-dependent demand and preservation investment

Time, price, and servicing are three important factors which affect the demand for any product. In a multi-period inventory model, different pricing in different periods may have a positive impact on demand. Offering discounts to customers during a shortage period is an efficient way to reduce lost sales. To address all these issues, a multi-period inventory model is developed and analyzed in this paper to obtain optimal pricing and replenishment periods where the market demand depends on price, time and service level provided by the retailer. The time horizon is fixed; the replenishment cycles are of equal length, but the shortage period may vary in different cycles. The retailer is allowed to charge different prices in different periods and provide discounts if someone places demand during a shortage period. Learning effects in holding and ordering costs are considered. Under a dynamic pricing framework, this study examines service improvement through learning and price discounts during shortages as an incentive for customers. It demonstrates that discounting indeed helps to produce more revenue when the back-order rate is low, and the service level is critical in generating demand when the stock-out period is lengthy, revealing a critical relationship between shortage periods and service level. A numerical experiment is performed to investigate the optimal policy in different situations and obtain some managerial insights.

Optimal strategy on inventory model under permissible delay in payments and return policy for deteriorating items with shortages

In this model, the manufacturer offers a trade credit policy to the retailer. Demand depends upon selling price and time for non-instantaneous deterioration items. The retailer offers the customer a returns policy. Customers can return the product to the retailer if the product is unsatisfactory for the customer. The retailer does not ultimately return the amount to its customer for the returned product. The manufacturer offers the retailer a trade credit policy. The retailer resale the returned products at the same selling price. A partially backlogged shortage is permitted and its rate is thought to depend on how long it takes for the following lot to arrive after a lot has been replenished. The main objective is to increase the retailers' overall profit by determining the optimal order quantity, optimal selling price, and optimal replenishment cycle. An EOQ is framed for analyzing the sample, which can obtain the optimal solution.

Enhancing molten salt oxidation sustainability: thermodynamic insights for spent salt reuse and carbonate cycle replenishment

An optimized replenishment cycle for the molten salt oxidation method restored the SO2 adsorption capacity of the molten salt for the high-cycle treatment process, achieving over 99% oxidation efficiency and maintaining 80% sulfur interception.

Optimal production and predictive maintenance decisions for deteriorated products under advance-cash-credit payments

In the 4th industrial revolution era, manufacturers tend to integrate predictive maintenance into production systems to maximize the lifespan of equipment and avoid costly disruptions. Using internet of things sensors and data analyses, predictive maintenance can significantly reduce the production downtime by detecting and predicting system problems before they become uncontrollable, resulting in a lower defective rate. Additionally, an advance-cash-credit (ACC) payment scheme is commonly applied in real-world business transactions to enhance cash flow flexibility among supply chain members. In this study, we developed a supplier–manufacturer chain, wherein a predictive-maintenance-adopting manufacturer received an ACC payment from a supplier, to demonstrate a supply chain management situation within the imperfect economic production quantity framework. The inventory system of the manufacturer was modeled as a cost minimization problem to determine the optimal replenishment cycle time and predictive maintenance effort for deteriorated products. The proposed model also adopted a discounted cash-flow analysis to consider the time value of the cost function, which was proven to be strictly convex in the replenishment time and predictive maintenance effort. Furthermore, a numerical and sensitivity analysis was conducted to illustrate the effectiveness of the proposed model and gain management insights.

Modeling and Solving the Joint Replenishment Problem with Cross-Selling Effects Considering One Shared Minor Item

In this paper, we provide a model to handle multiple replenishment cycles and the cross-selling of multiple major items with one minor item, while allowing partial late delivery. The optimization analytic expression of the model is finally obtained by utilizing the convexity of cost function for F and using the first-order conditions in optimization theory. Numerical examples and sensitivity analysis demonstrate the effectiveness of the model and algorithm, which offers a competent solution for practical applications.

Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores

In today's competitive grocery retail environment, effective inventory management coupled with precise sales prediction is pivotal for success. This research undertakes an in-depth exploration into a machine learning framework tailored for "Sales Forecasting and Inventory Optimization in Grocery Retail." Utilizing historical sales records, meteorological data, and consumer behavior metrics, the proposed model endeavors to predict nuanced seasonal demand fluctuations. The primary value proposition of this approach lies in its capacity to curtail wastage, ensuring the balance between overstocking and inventory stockouts. By recognizing and adjusting for seasonal dynamics, the system offers enhanced demand fulfillment during high-demand periods. The subsequent refinement of the replenishment cycle fosters superior operational efficacy. As an outcome, businesses witness cost reductions, augmented profit margins, and elevated consumer contentment due to consistent product availability. As the broader grocery sector undergoes transformation, embedding sophisticated machine learning strategies can be a linchpin for sustained adaptability and competitiveness. This research further underscores the role of emergent technologies, such as machine learning within the IoT spectrum, in reshaping supply chain and communication paradigms. The endeavor here is to curtail lifecycle expenses in the supply chain by refining inventory practices. The research introduces a Deep Inventory Management (DIM) technique, employing the GradientBoostingRegressor paradigm of machine learning. Through DIM's innovative approach, time-series challenges are reimagined as supervised learning tasks, enabling efficient model training. Preliminary tests indicate DIM's prediction accuracy hovers around 85%, translating to an impressive 15% reduction in inventory expenses when juxtaposed with prevailing methods, alongside rapid anomaly detection in inventory activities

A green inventory model considering environmental emissions under carbon tax, cap-and-offset, and cap-and-trade regulations

ABSTRACT This study assesses green technology investment as well as carbon pricing regulations, including carbon tax, cap-and-offset, and cap-and-trade, to control carbon emissions from retailer inventory. A green inventory model is studied due to a lack of investigation on the simultaneous effect of green technology investment and variable demand. Prior studies neglected the demand pattern that is sensitive to product quality, pricing, and the promotional effort of a green inventory model. This study contributes guidance to retailer managers on the optimal price and replenishment cycle given a carbon pricing regulation and green technology investment plan. This study maximizes the total profit using the classical optimization technique. Two cases are involved in representing the inventory model with and without green technology investment. The carbon pricing regulations were studied. Essential theoretical derivations have been provided to show the concavity of the anticipated model. The results prove the advantages of green technology investment and carbon pricing implementation. The numerical example and sensitivity analysis are shown, and the cap-and-trade policy works efficiently within constraints to curb carbon emissions. The manager established an optimal selling price and the replenishment cycle period by forecasting the amount of investment in green technology and some transportation cost variables.

A single-vendor, multiple-buyer coordinated supply chain model with unequal-sized batch shipments and cycle-dependent safety stocks

This paper investigates a single-vendor, multiple-buyer coordinated supply chain under stochastic demand. Each buyer implements a continuous review policy, and the shortage quantity is fully back-ordered. The lead time for the first shipment includes setup, production, and transportation times, while the lead time for the remaining shipments includes only transportation time. The production time is a function of production lot and production rate, which are both decision variables, and the setup and transportation times are controllable as well. For each buyer, the safety stock in the first replenishment cycle is not necessarily identical to that in the remaining cycles. Two successive shipments from the vendor to a buyer may have different sizes, and their ratio is a decision variable. The problem is finding the production and inventory replenishment policy, production rate, and lead times that minimise the long-run expected total cost per time unit. We demonstrate properties satisfied by the cost function and develop an optimisation algorithm, whose performance is compared to a benchmark algorithm based on a commercial solver in a numerical experiment. The experiments also investigate the benefits stemming from the proposed model when compared to models reproducing situations that leverage some or any of the controlled factors.

An efficient data-driven method for storage location assignment under item correlation considerations

This research investigates the storage location assignment problems of correlated-items under a realistic multiple-cross-aisle warehouse setting. To accommodate the shortest picking route decision and update customer orders in each replenishment cycle to capture the changing trend in customer preferences. An item-correlations considered fitness function is developed to evaluate the benefit of exchanging item locations and minimise the picking costs. A data-driven storage location assignment method called storage location assignment for correlated-item method is proposed to improve the order picking efficiency. The explicit considerations make this work distinct from existing studies: (1) correlation among items in customer orders, (2) penalty for crossing-aisles in warehouse traffic, and (3) real retail dataset adopted. Our method considers the effect of correlated items in customer orders, through a storage exchange benefit function to evaluate the fitness of storage location to minimise warehouse operation costs and enhance operation efficiency. With a real ecommerce dataset, the numerical study results show that our method can reduce the travelling distance by 5–10% compared with a conventional turnover-based storage policy. Our method not only outperforms in terms of travel distance. The picking time improvement is even more significant for large warehouse if a moderate penalty for crossing-aisles is considered.

Optimizing Supplier Selection and Order Lot-Sizing Decisions in a Two-Stage Supply Chain

This paper analyzes different lot-sizing policies for the supplier selection and order allocation problem in a two-stage supply chain. The supply chain consists of multiple candidate suppliers and a single buyer. In this system, selected suppliers produce a product in batches at finite production rates, ship it to the buyer, and the buyer sells it to the market at a constant demand rate. Our goal is to evaluate two lot-sizing policies and select the one that optimizes the supply chain by minimizing the total cost and maximizing supplier efficiency. A bi-objective mixed-integer nonlinear programming (BOMINLP) model is proposed. The first objective consists of the development of a coordination mechanism for supplier selection and order allocation that minimizes the entire supply chain cost, and the second objective comprises a data envelopment analysis (DEA) approach to evaluate the overall performance of suppliers to optimize supplier efficiency. Then, the lot-for-lot and order frequency policies are applied to the BOMINLP model separately to determine the set of selected suppliers as well as the corresponding order quantities and number of orders allocated to each selected supplier per replenishment cycle. Numerical examples that illustrate the solution approach and compare the two lot-sizing policies are provided.

An optimal replenishment cycle and order quantity inventory model for deteriorating items with fluctuating demand

Suppliers often prefer to offer their retailers a delay period in payment to attract more sales and promote revenue in a supply chain. The retailers usually ask their customers to pay a portion of purchasing cost when receiving the product (i.e., a downstream partial trade credit) to reduce the default risk. On the other hand, the suppliers provide discounts for bulk purchases, and the retailer has enough capital to purchase more goods than can be stored in its warehouse. The retailer must store the excess quantities in a rented warehouse if the storage capacity is limited. A two-warehouse inventory system is needed to model this problem. In reality, the demand rate fluctuates with time, and the relevant cost is usually affected by the present value of time. This study focuses on the limited storage capacity inventory model for deteriorating items with fluctuating demand, downstream partial trade credit transactions, and discounted cash-flow considerations. The aim is to find the optimal replenishment cycle and order quantity and keep the present value of the total relevant cost per unit of time as low as possible. We further present numerical examples to demonstrate the applicability and develop managerial insights.