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Article
Publication date: 11 April 2008

Matthew A. Waller, Brent D. Williams and Cuneyt Eroglu

Whereas inventory theory traditionally assumes the periodic review inventory model (R, T), with an order‐up‐to level R, has a random demand and lead time coupled with a…

2100

Abstract

Purpose

Whereas inventory theory traditionally assumes the periodic review inventory model (R, T), with an order‐up‐to level R, has a random demand and lead time coupled with a deterministic review interval T, firms often deviate from a strict adherence to a fixed review interval when they attempt to capture transportation scale efficiencies. Employing this policy introduces additional supply chain variability. This paper aims to provide an expression for the standard deviation of demand during the protection period, important in setting safety stock, as well as an expression for the amount of order variance amplification induced by a stochastic review interval.

Design/methodology/approach

Analytical modeling is used to develop the expression for the standard deviation of demand during the protection period as well as the calculation for the amount of order variance amplification induced by a stochastic review interval.

Findings

In terms of the variance of demand over the protection period, a stochastic review interval has a similar effect to that of a stochastic lead time, but its impact on demand variance amplification within the supply chain differs fundamentally. Specifically, a stochastic review interval creates an order batching bullwhip effect not identified in existing literature.

Research limitations/implications

This study offers an expression for the standard deviation of demand during the protection period when stochastic review intervals are employed. The expression can be used to more effectively set safety stock. The paper also offers an expression for the order variance amplification induced by a stochastic review interval.

Practical implications

The study offers suggestions for retailers and suppliers regarding when the use of a stochastic review interval is effective in terms of cost efficiencies.

Originality/value

While the existence and effect of lead time variability is well‐established in the literature, traditional approaches the periodic review inventory model ignore the stochastic nature of review interval. This paper highlights the use of stochastic review intervals as a contributing factor to the bullwhip effect.

Details

International Journal of Physical Distribution & Logistics Management, vol. 38 no. 3
Type: Research Article
ISSN: 0960-0035

Keywords

Article
Publication date: 3 August 2020

Yichen Qin, Hoi-Lam Ma, Felix T.S. Chan and Waqar Ahmed Khan

This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service…

Abstract

Purpose

This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.

Design/methodology/approach

A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.

Findings

Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.

Research limitations/implications

The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.

Practical implications

Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.

Originality/value

A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.

Details

Industrial Management & Data Systems, vol. 120 no. 9
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 1 June 1998

A.S. Humphrey, G.D. Taylor and T.L. Landers

In this article, we present the results of a study examining the behavior of various inventory stocking methodologies in repair/rework operations. A major area of focus is on the…

1487

Abstract

In this article, we present the results of a study examining the behavior of various inventory stocking methodologies in repair/rework operations. A major area of focus is on the sensitivity of key model parameters to stochastic replenishment lead times, product demand, and overhaul factors. A case study in a US Army depot provides validation for the effort. Simulation results indicate the current depot stocking methodologies are adequate in ideal conditions, but are less effective in more challenging and realistic scenarios. Results also indicate that some commonly used inventory models are quite robust to stochastic operating parameters in the unique/rework environment.

Details

International Journal of Operations & Production Management, vol. 18 no. 6
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 1 September 2005

George Baltas

The purpose of this paper is to consider a new application of stochastic frontier analysis, in which the method is applied to demand data for a food product category, in an…

1409

Abstract

Purpose

The purpose of this paper is to consider a new application of stochastic frontier analysis, in which the method is applied to demand data for a food product category, in an attempt to benchmark category consumption and segment food consumers.

Design/methodology/approach

In a unified, two‐stage approach, a stochastic frontier model is first estimated and subsequently deviations from the demand frontier are regressed on customer characteristics. The method is illustrated in scanner panel data.

Findings

A frontier demand function estimated in scanner data of a frequently‐bought food category has significant and consistent parameters. Specific descriptor variables can explain excessive category demand and profile customers with considerable sales potential.

Research limitations/implications

More work is needed to generalise the usefulness of the proposed model in different food categories. Future research may employ alternative functional specifications and explanatory variables.

Practical implications

The empirical identification of salient characteristics improves consumer understanding and can assist in the design of data‐driven marketing action. Applied researchers can use marketing and demographic variables that are found in standard consumer panels to estimate frontier models.

Originality/value

The paper introduces stochastic frontier analysis as a means to determine consumer differences in food demand. This is an important area for retailers, producers and researchers.

Details

British Food Journal, vol. 107 no. 9
Type: Research Article
ISSN: 0007-070X

Keywords

Article
Publication date: 16 November 2015

Syed Asif Raza

The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a…

1900

Abstract

Purpose

The purpose of this paper is to study the impact of differentiation price which has been utilized to segment demand, but results in imperfect segmentation. The use of a differentiation price is among the most widely used Revenue Management (RM) techniques to segment a firm’s demand to augment profitability.

Design/methodology/approach

Mathematical models are developed for a firm’s RM which use a differentiation price to categorize its market demand into two segments. Three distinct demand situations are considered: price-dependent deterministic demand, price-dependent stochastic demand whose distribution is known and price-dependent stochastic demand whose distribution is unknown. Models are analyzed to determine optimal joint control of a firm’s pricing and inventory decisions for each market segment.

Findings

The analysis of the firm’s RM model has shown that revenue is jointly concave in pricing and order quantity. In most demand situations, closed-form mathematical expressions for optimal pricing and inventory are obtained.

Research limitations/implications

In RM models developed in this paper, a firm only selects a differentiation price. Thus, an optimal selection of the differentiation price along with the pricing and inventory decisions may lead to an additional profitability which has not been explored in this research.

Practical implications

The findings reported are relevant to RM managers and practitioners and help them to calibrate their optimal revenues by segmenting markets using a differentiation price.

Social implications

This paper provides a quantitative perspective of a firm’s decision on the use of the differentiation price and the market response.

Originality/value

The paper provides a firm’s optimal decision on pricing and inventory when it experiences demand leakage due to categorizing its market demand into two segments using a differentiation price.

Details

Journal of Modelling in Management, vol. 10 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 3 April 2018

Remica Aggarwal, Surya Prakash Singh and P.K. Kapur

In this paper, vendor selection and order allocation problem is considered for a buyer dealing in multiple products to be supplied by multiple vendors. Each product has an…

Abstract

Purpose

In this paper, vendor selection and order allocation problem is considered for a buyer dealing in multiple products to be supplied by multiple vendors. Each product has an associated lead time with stochastic demand having stochastic capacity for each vendor across entire time period. Uncertainties related to costs which are further influenced by the periodically changing incremental quantity discounts offered by various vendors. The purpose of this paper is to find an optimal trade-off of vendor selection and order allocation in the presence of uncertainties involving multiple conflicting objectives such as cost minimization, service level/quality level maximization and delivery lead time minimization concurrently.

Design/methodology/approach

Vendor selection problem considered here has a multi-objective optimization design subject to a set of demand, capacity and quantity discount based constraints. These constraints as well as uncertainty related to lead time have been handled using chance constraint approach. The problem is titled as “integrated dynamic vendor selection problem (IDVSP).” The proposed multi-objective IDVSP is solved using both non-pre-emptive goal programming (GP) and weighted sum aggregate objective function (AOF) technique.

Findings

Findings indicate goal achievement for different objectives from both non-pre-emptive GP and AOF procedure. While the goals are satisfactorily achieved as per the target values for cost and lead time, quality/service level was somewhat compromised in order to find an appropriate trade off.

Originality/value

The research work is original as it integrates dynamic as well as stochastic (uncertain) nature of supply chain simultaneously coupled with the concept of incremental quantity discounts on lot sizes.

Details

Benchmarking: An International Journal, vol. 25 no. 3
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 9 April 2018

Harpreet Kaur and Surya Prakash Singh

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon…

Abstract

Purpose

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues.

Design/methodology/approach

This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters.

Findings

The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10.

Originality/value

The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.

Details

Management of Environmental Quality: An International Journal, vol. 29 no. 3
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 27 December 2021

Sara Nodoust, Mir Saman Pishvaee and Seyed Mohammad Seyedhosseini

Given the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem…

Abstract

Purpose

Given the importance of estimating the demand for relief items in earthquake disaster, this research studies the complex nature of demand uncertainty in a vehicle routing problem in order to distribute first aid relief items in the post disaster phase, where routes are subject to disruption.

Design/methodology/approach

To cope with such kind of uncertainty, the demand rate of relief items is considered as a random fuzzy variable and a robust scenario-based possibilistic-stochastic programming model is elaborated. The results are presented and reported on a real case study of earthquake, along with sensitivity analysis through some important parameters.

Findings

The results show that the demand satisfaction level in the proposed model is significantly higher than the traditional scenario-based stochastic programming model.

Originality/value

In reality, in the occurrence of a disaster, demand rate has a mixture nature of objective and subjective and should be represented through possibility and probability theories simultaneously. But so far, in studies related to this domain, demand parameter is not considered in hybrid uncertainty. The worth of considering hybrid uncertainty in this study is clarified by supplementing the contribution with presenting a robust possibilistic programming approach and disruption assumption on roads.

Article
Publication date: 7 July 2021

Pravin Suryawanshi and Pankaj Dutta

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors…

620

Abstract

Purpose

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).

Design/methodology/approach

The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.

Findings

The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.

Originality/value

The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

Details

International Journal of Productivity and Performance Management, vol. 72 no. 1
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 1 September 1995

Houmin Yan

Considers manufacturing systems with a failure‐prone machine and astochastic demand. The production is controlled by a kanbanscheme. Although the kanban‐control enjoys many…

720

Abstract

Considers manufacturing systems with a failure‐prone machine and a stochastic demand. The production is controlled by a kanban scheme. Although the kanban‐control enjoys many applications, in a stochastic setting, the problem of defining the optimal number of circulating kanbans remains unsolved except for some special cases. Seeks an optimal kanban‐controlled policy which minimizes long run average inventory and backlog costs. Uses perturbation analysis to estimate the gradients of the cost functional with respect to the number of circulating kanbans, and then employs an iterative algorithm, which is a constant step‐size stochastic approximation procedure, to find the optimal number of circulating kanbans. Proves that the perturbed path is a shifted version of the nominal path, and the gradient estimate is consistent. Also conducts numerical experiments to investigate the performance of the proposed algorithms.

Details

International Journal of Operations & Production Management, vol. 15 no. 9
Type: Research Article
ISSN: 0144-3577

Keywords

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