Initial Stock Level Research Articles

The Proactive Base Stock Policy for Condition Based Maintenance

This paper introduces the proactive base stock policy (ProBSP) that incorporates degradation data into spare parts decision-making processes. The proposed policy aims to anticipate the demand for spare parts by proactively ordering them, instead of ordering each time one is needed. ProBSP involves two decision variables: the initial stock level and the degradation order threshold. To determine the optimal values for these variables, a simulation-based optimization method is developed. More precisely, a discrete event simulation is constructed to evaluate a single point in the parameter space, and an intelligent algorithm is employed to search for the optimal solution by efficiently exploring the solution space, thereby exploiting structural properties of ProBSP. Finally, numerical experiments are conducted to gain insights into the performance of ProBSP. These experiments reveal the effectiveness of ProBSP: the average spare parts stock level is reduced by 68%, with reductions exceeding 90% in specific scenarios.

Mobile Money Operations: Policies for Managing Cash and Digital Currency Inventories in the Developing World

Problem definition: Mobile money systems—platforms built and managed by mobile network platform operators (MMPOs) to allow money to be stored as digital currency—connect millions of poor and “unbanked” people to the formal financial system. Unfortunately, low service levels because of the suboptimal management of cash and digital currency (e-float) inventory impede the development of these ecosystems. Accordingly, we seek to answer the question of how agents should manage inventories of cash and e-float. Academic/practical relevance: This paper extends inventory theory to the mobile money context, unique in that sales of cash generate inventory of e-float and vice versa. In doing so, we address a key pain point for an emerging sector that improves lives at the base of the pyramid. Methodology: We develop an analytical heuristic to determine initial stocking levels for cash and e-float and analyze its performance on simulated and actual data. Results: By partnering with an MMPO, we tested the performance of the heuristic inventory policy with data from more than 35 million transactions. The heuristic captured 99.9998% of the optimal profit on simulated data and, on actual data, we found that following the recommendations could increase agents’ profits by an average of 15.4%. Managerial implications: We develop a pragmatic inventory policy that performs nearly optimally. We also analyze under which conditions the performance deteriorates and examine heterogeneity among agents with respect to the heuristic’s impact on their performance. Thus, we equip MMPOs with guidance as to whom to target and how. By contributing to service level and profit improvements, this work can make mobile money a more effective financial inclusion tool in the developing world as well as improve the livelihoods of agents. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1175 .

Robust collaborative maintenance logistics network design and planning

Maintenance service providers to advanced technical devices are confronted with uncertain demand, high cost of components, and the need for certified operators. Collaboration in terms of sharing scarce resources among different facilities in a maintenance logistics network is expected to reduce the delays in the delivery of repaired devices. This study proposes a two-stage robust optimization model for collaborative design and planning of maintenance networks under demand uncertainty. The goal of this model is to determine the optimal allocation of customers to each maintenance center along with the initial stock level of different components in each facility so as to minimize the cost of late deliveries under worst-case demand scenarios. Component and operator sharing strategies are proposed as the recourse actions in this model to hedge against the demand surge. The proposed approach is compared with a deterministic model by the aid of Monte-Carlo simulation on several test instances inspired by a real case study. Our numerical experiments demonstrate the significance of adopting the proposed collaborative mechanisms among maintenance facilities in terms of cost reduction, especially when the demand fluctuation is relatively high.

Crisp and interval inventory models for ameliorating item with Weibull distributed amelioration and deterioration via different variants of quantum behaved particle swarm optimization-based techniques

ABSTRACTThis paper presents two inventory models for ameliorating items under crisp and interval environments. In these models, three-parameter Weibull distribution is considered to represent both the amelioration and deterioration rates. In crisp, an inventory model is formulated for ameliorating item with fixed values of different inventory parameters. Due to uncertainty, these parameters may not be fixed. In this context, another inventory model with interval valued parameters is developed. Also, demand is dependent on the selling price and advertisement frequency of the product. The corresponding profit maximization problem has been developed. For solving the problem, different variants of quantum behaved particle swarm optimization technique (QPSO) are applied. To validate the proposed models, two numerical examples are considered and solved. The results are compared for different variants of QPSO techniques. Finally, graphical sensitivity analyses are presented to study the impact of several system parameters on cycle length, initial stock level along with average profit for both the models.

Inflationary Induced EOQ Model for Weibull Distribution Deterioration and Trade Credits

In any business transaction the constant unit price assumption is not true. The tendency in inflationary environment is to buy more in order to reduce the total system cost, which may be true in certain situations but it is not true when consumption rate of items is dependent on initial stock level since buying more quantity under inflationary environment leads to more consumption resulting in higher total system cost. The model developed in this paper helps to determine optimum ordering quantity for stock dependent consumption rate items under inflationary environment with infinite replenishment rate without permitting shortages. The effect of the inflation rate, deterioration rate, Initial-stock-dependent consumption rate and delay in payment are discussed. This study develops an inventory model for constant demand rate and time dependent deterioration rate with delay in payment is discussed. In this study mathematical model are also derived under two different cases. Case-I: The credit period is less then cycle time T; and Case-II: Credit period is greater than cycle time T. This study will proposes an inventory model under a situation in which the supplier provides the purchaser a permissible delay of payments if the purchaser orders a large quantity. Numerical example is given to support the purposed model. Mathematica 7.0 is used for numerical solutions.

Periodic review inventory systems with fixed order cost and uniform random yield

We consider periodic review stochastic inventory control problems with both fixed order cost and uniform random yield. Our objective is to characterize some structural properties of the optimal policies so that efficient approaches can then be established towards finding an optimal policy. In particular, we provide a lower-and-upper bound structure for the optimal policy at the beginning of any period, such that it is optimal not to order anything if the initial stock level is above the upper bound, and it is optimal to order a positive quantity if the initial stock level is below the lower bound, where the optimal quantity can be found efficiently. For any initial stock level in-between the upper and lower bounds in each period, a partial characterization of the optimal policy is provided. Furthermore, we show that the optimal order quantity is monotonically decreasing in the initial stock level in any period and this will help the search for the optimal order quantity at any initial stock level. In addition, we illustrate a few interesting phenomena about the behavior of an optimal policy. These phenomena show that the structure of the optimal policy for this problem is in general significantly different from that for the traditional periodic review inventory control problems with certain yield and fixed order cost.

Optimising spare parts provision and inspection intervals for an aircraft fleet undergoing cannibalisation

This paper studies the problem of determining the optimal initial stock level of recoverable components and the aircraft preventive maintenance inspection interval for an aircraft fleet. Demand for spare components arises from both corrective and preventive maintenance tasks. An aircraft that experiences failures while performing a mission is maintained by replacing failed components with working spares. In order to reduce the amount of this unplanned, corrective maintenance that is performed, aircraft are usually inspected periodically and components found to be in poor condition replaced preventively. The supply of spares is critical to maintaining fleet operational requirements but determining the stock required while considering critical fleet operational requirements and budget limitations is a complex task. When spares are unavailable, inoperative aircraft must wait for failed components to be repaired. In such situations, cannibalisation may be performed, where working components are removed from inoperative aircraft in order to replace failed components of the same type in other aircraft. Cannibalisation is beneficial in maintaining fleet readiness and reducing spare part demands since is it allows any shortage of spares to be consolidated into a small number of inoperative aircraft. In this paper a coloured Petri net (CPN) model is used to model the fleet maintenance process. The model accounts for fleet operation, aircraft preventive maintenance, repair, spares and cannibalisation. A genetic algorithm (GA) based simulation-optimisation approach is employed to find the optimal solutions of fleet spare provisioning and the aircraft periodic inspection interval to minimise the cost.

Optimal production planning for assembly systems with uncertain capacities and random demand

We study the optimal production planning for an assembly system consisting of n components in a single period setting. Demand for the end-product is random and production and assembly capacities are uncertain due to unexpected breakdowns, repairs and reworks, etc. The cost-minimizing firm (she) plans components production before the production capacities are realized, and after the outputs of components are observed, she decides the assembly amount before the demand realization. We start with a simplified system of selling two complementary products without an assembly stage and find that the firm's best choices can only be: (a) producing no products or producing only the product of less stock such that its target amount is not higher than the other product's initial stock level, or (b) producing both products such that their target amounts are equal. Leveraging on these findings, the two-dimensional optimization problem is reduced to two single-dimensional sub-problems and the optimal solution is characterized. For a general assembly system with n components, we show that if initially the firm has more end-products than a certain level, she will neither produce any component nor assemble end-product; if she does not have that many end-products but does have enough mated components, she will produce nothing and assemble up to that level; otherwise she will try to assemble all mated components and plan production of components accordingly. We characterize the structure of optimal solutions and find the solutions analytically.

A two storage inventory model for deteriorating items with variable demand and partial backlogging

The goal of this article is to develop a deterministic inventory model for single deteriorating items with two separate storage facilities (owned and rented warehouse, RW) due to limited capacity of the existing storage (owned warehouse, OW). The demand rate of the item is dependent on time, selling price of an item, and the frequency of advertisement in the popular electronic and print media and also through the sales representatives. The stocks of RW are transported to OW in continuous release pattern. Shortages, if any, are allowed and partially backlogged with a variable rate dependent on the duration of waiting time up to the arrival of next lot. The storage capacities of the warehouses are assumed to be limited (finite). According to the relative size of the storage capacities of warehouses, the different scenarios with sub-scenario have been mentioned for the stock level at initial and reorder points of the proposed inventory system. For each sub-scenario, the corresponding problem has been formulated as mixed integer nonlinear constrained optimization problem. For solving this problem, we have used GRG method. To illustrate the proposed model, a numerical example has been considered and solved. Finally, to perform the sensitivity analyses, the effects of changes of demand, deterioration, and backlogging parameters on the initial stock level, reorder point, cycle length, and frequency of advertisement along with the maximum profit of the system have been studied.

Dynamic acquisition pricing policy under uncertain remanufactured-product demand

Purpose– The purpose of this paper is to investigate the dynamic acquisition pricing strategy for collecting used products (also known as cores or returns) in a finite planning horizon. In particular, this paper studies a cost-minimization model in which a firm offers acquisition price that impacts the quantity of the returns, and remanufactures the used product to satisfy the customer demand.Design/methodology/approach– This paper uses multi-period stochastic dynamic programming theory to model a remanufacturing system that faces the random demand for remanufactured products. The number of the returns at each period is uncertain and increases linearly with the acquisition price offered.Findings– The study shows that when the uncertainty of demand for remanufactured products increases, the remanufacturer should hold a higher core stock level to minimize the expected total cost and thus a higher acquisition price is needed to attract returns. However, given demand uncertainty, the optimal price decreases in the initial core stock level in each period. It also indicates that the optimal acquisition price increases in the variance of the returns, but decreases in the mean of the returns.Practical implications– The findings suggest that a remanufacturer could reduce the expected total cost by adjusting the acquisition price according to the number of returns periodically.Originality/value– Introducing the impact of supply uncertainty on the acquisition price of used products, this paper uses a multi-period dynamic model, instead of single period model in previous studies, to examine the remanufacturer’s dynamic acquisition pricing policy.

Optimal policies of a two-echelon serial inventory system with general limited capacities

We study optimal policies of capacitated two-echelon serial inventory systems under periodic review. For a system with smaller downstream capacity, we fully characterise the optimal policy as a further modified echelon base stock policy using an intuitive backward induction. The key lies in the magnitude relation between the initial upstream stock level and the downstream capacity. For a system with smaller upstream capacity, we demonstrate that the optimal policy is of a more complex structure where there can be at most four/five target levels up to which the upstream/downstream echelon tries to produce/order. The numbers of levels and their values depend on the length of remaining horizons and the amount of initial upstream inventories. We also specify these potential target levels and then suggest a way to simplify the search of optimal solutions.

Assessing optimal configurations of multi-paddock grazing strategies in tallgrass prairie using a simulation model

Maintaining or enhancing the productive capacity and resilience of rangeland ecosystems is critical for the continued support of livelihoods and the ecosystem services that benefit society at large. While the benefits of multi-paddock grazing management have been evident for many years in many countries, it is extremely difficult if not impossible to adequately assess the consequences of the different combinations of management options possible when using multi-paddock management under constantly varying conditions on rangelands. To investigate grazing scenarios that would be impractical to conduct in the field we developed a simulation model to focus on addressing the impacts of different cattle grazing management options with multi-paddock management on ecological condition (EC) and profitability. Cattle ranching options are simulated over 25 years periods under varying levels of multi-paddock grazing management complexity at low to moderate stocking levels and fixed or variable stocking rates. We examine the likely ecological and economic effects of shortening grazing periods, lengthening recovery periods, using fixed versus adaptive operational decisions and increasing the number of paddocks in the grazing configuration. At initial stocking levels of up to at 70% of forage standing crop, both EC and profitability are increased with increasing number of paddocks. Shorter periods of grazing increase both EC and profitability while increasing recovery periods increases both EC and profitability initially but profitability decreases if recovery periods are too long. Both EC and profitability are positively related to number of paddocks used.

A deterministic inventory model for deteriorating items with selling price dependent demand and three-parameter Weibull distributed deterioration

Article history: Received November 2 2013 Received in Revised Format 10 January 2014 Accepted February 8 2014 Available online February 21 2014 In this paper, an attempt is made to develop two inventory models for deteriorating items with variable demand dependent on the selling price and frequency of advertisement of items. In the first model, shortages are not allowed whereas in the second, these are allowed and partially backlogged with a variable rate dependent on the duration of waiting time up to the arrival of next lot. In both models, the deterioration rate follows three-parameter Weibull distribution and the transportation cost is considered explicitly for replenishing the order quantity. This cost is dependent on the lot-size as well as the distance from the source to the destination. The corresponding models have been formulated and solved. Two numerical examples have been considered to illustrate the results and the significant features of the results are discussed. Finally, based on these examples, the effects of different parameters on the initial stock level, shortage level (in case of second model only), cycle length along with the optimal profit have been studied by sensitivity analyses taking one parameter at a time keeping the other parameters as same. © 2014 Growing Science Ltd. All rights reserved

A simheuristic algorithm for the Single-Period Stochastic Inventory-Routing Problem with stock-outs

This paper describes a ‘simheuristic’ algorithm – one which combines simulation with heuristics – for solving a stochastic variant of the well-known Inventory-Routing Problem. The variant discussed here is integrated by a vehicle routing problem and several inventory problems characterized by stochastic demands. Initial stock levels and potential stock-outs are also considered, as well as a set of alternative refill policies for each retail center. The goal is to find the personalized refill policies and associated routing plan that minimize, at each single period, the expected total costs of the system, i.e., the sum of inventory and routing costs. After motivating it, a detailed description of the problem is provided. Then, a review of the related literature is performed and our simulation–optimization approach is introduced. The paper presents a set of numerical experiments comparing the proposed method against different refill strategies and discusses how total costs evolve as the level of system uncertainty and the inventory-holding costs per unit are varied.

Determining the fill rate for a periodic review inventory policy with capacitated replenishments, lost sales and zero lead time

In this paper we consider a periodic review order-up-to inventory system with capacitated replenishments, lost sales and zero lead time. We consider discrete demand. It is shown that the initial stock levels of the different review periods form a Markov chain and we determine the transition matrix. Furthermore we study for what probability mass functions of the review period demand the Markov chain has a unique stationary distribution. Finally, we present a method to determine the fill rate.

Approximation schemes for single machine scheduling with non-renewable resource constraints

In this paper we discuss exact and approximation algorithms for scheduling a single machine with additional non-renewable resource constraints. Given the initial stock levels of some non-renewable resources (e.g., raw materials, fuel, money), and time points along with replenishment quantities, a set of resource consuming jobs has to be scheduled on the machine such that there are enough resources for starting each job, and the makespan is minimized. We show that the problem admits a pseudo-polynomial time algorithm when the number of replenishments is not part of the input, and also present an FPTAS when there is only a single resource, and it is replenished only once. We also describe a PTAS for the problem with a constant number of replenishments.

Selling with Money‐Back Guarantees: The Impact on Prices, Quantities, and Retail Profitability

In this paper, we consider a retailer adopting a “money‐back‐guaranteed” (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full or partial refund. The retailer either salvages returned products or resells them as open‐box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open‐box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open‐box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered. We show that selling with MBGs increases retail sales and profit. Furthermore, the second‐sale opportunity created by restocking returned products enables the retailer to generate additional revenues. Our analysis identifies the ideal conditions under which this practice is most beneficial to the retailer. Offering an MBG without restocking increases the new product price. We show that if the retailer decides to resell the returned items as open‐box, the price of the new product further increases, while open‐box items are sold at a discount. On the other hand, customers enjoy more generous refunds along with lower restocking fees. The opportunity to resell returned products also generally decreases the initial stocking levels of the retailer. Our extensive numerical study substantiates the analytical results and sharpens our insights into the drivers of performance of MBG policies and their impact on retail decisions.

A study on two-warehouse partially backlogged deteriorating inventory models under inflation via particle swarm optimisation

This paper deals with a deterministic inventory model for linear trend in demand under inflationary conditions with different rates of deterioration in two separate warehouses (owned and rented warehouses). The replenishment rate is infinite. The stock is transferred from the rented warehouse to owned warehouse in continuous release pattern and the associated transportation cost is taken into account. At owned warehouse, shortages, if any, are allowed and partially backlogged with a rate dependent on the duration of waiting time up to the arrival of the next lot. The corresponding problems have been formulated as nonlinear constrained optimisation problems for two different policies (inventory follows shortage (IFS) and shortage follows inventory (SFI)). Finally, the model has been illustrated with a numerical example and to study the effects of changes of different system parameters on initial stock level, maximum shortage level and cycle length with the minimum cost of the system, sensitivity analyses have been carried out by changing one parameter at a time and keeping the others at their original values.

Copreneurial identity development during new venture creation

PurposeThe purpose of this paper is to investigate formation of a copreneurial identity during new venture creation by investigating underpinnings of spousal commitment considering business communication quality.Design/methodology/approachThe study was grounded in identity theory, used a longitudinal copreneurial sample, and SEM modeling. Entrepreneurial literature is filled with how entrepreneurs form their identity, but little is known about how entrepreneurs and their spouses mutually form their copreneurial identity.FindingsEntrepreneurs’ reported spouses having high Time 1 commitment, but spouses reported they were more committed than reported by entrepreneurs. Links between spouse’s Time 1 commitment self‐assessment and Time 2 entrepreneur’s assessment of spousal commitment differed by business communication quality. Time 1 spouse’s commitment self‐assessment was positively related to Time 2 entrepreneur’s appraisal of spousal commitment only for the high business communication group and not for the low business communication group. For couples having high business communication quality, entrepreneur’s assessment of spousal commitment over time was composed of spouse’s self‐assessment of commitment and entrepreneur’s appraisal of spousal commitment, reflecting the mutual verification of a copreneurial identity.Originality/valueThis study provides evidence for Van Auken and Werbel's proposition that an entrepreneur's decision to launch a new business depends not only on opportunity analyses but also on the degree that an entrepreneur's spouse shares a common vision about firm goals. This study not only contributes to the theoretical development of a copreneurial identity but it also addresses measurement issues related to spousal business identity formation. Unlike previous studies considering spousal commitment in terms of marital status or work involvement, a measurement model for spousal commitment was tested using three indicators of cognitive moral commitment. Distinctions were made in stock and flow measures of spousal social capital and initial spousal stock levels were assessed. Furthermore, there appeared to be relatively high consistency between entrepreneur's assessment of spousal commitment and spousal's reflection of their own commitment, suggesting that the spousal commitment construct has some clearly defined properties.

Optimal partial backordering two-storage inventory model for deteriorating items with variable demand

The goal of this work is to develop an inventory model (deterministic) for single deteriorating items having two separate storage facilities (owned and rented warehouse) due to limited capacity of the existing storage (owned warehouse). The demand rate of the item is dependent on time, selling price of an item and frequency of advertisement in the popular electronic and print media, also through the sales representative. The stocks of rented warehouse are transported to the owned warehouse in continuous release pattern. Shortages, if any, are allowed and partially backlogged with a variable rate dependent on the duration of waiting time up to the arrival of next lot. The storage capacities of warehouses are assumed to be limited (finite). According to the relative size of the storage capacities of warehouses, the different scenarios with sub-scenarios have been mentioned for the stock level at initial and reorder point of the proposed inventory system and solved with the help of generalised reduced gradient method. To illustrate the results, a numerical example is given. Finally, to study the effect of change of demand, deterioration and backlogging parameters on initial stock level, reorder point stock level, cycle length and frequency of advertisement along with the maximum profit of the system.