Variable Lead Time Research Articles

Investing in lead-time variability reduction in a collaborative vendor–buyer supply chain model with stochastic lead time

This study deals with investing in lead-time variability reduction problems for the integrated vendor–buyer supply chain system with partial backlogging under stochastic lead time. We consider that lead time variability can be reduced through further investment; more specifically, a logarithmic investment function is used that allows investment to be made to reduce lead-time variability. By using the proposed supply chain model, considerable savings can be achieved to increase the competitive edge. The objective is to derive the optimal production/ordering strategy, and the best investment policy to minimize joint total cost. A computer code using the software, Mathematica, is developed to derive the optimal solution. Furthermore, we discuss the sensitivity of the optimal solution together with the changes of the values of the parameters associated with the model for decision-making. Various numerical examples are given to illustrate the results.

Reducing lead-times and lead-time variance in cooperative distribution networks

The academic and professional attention to the large synergies hidden in horizontal collaborations is increasing. This study attempts to address the impact of collaborative transport on transportation lead-time and lead-time variability through empirically investigating a group of SMEs involved in a collaborative distribution network. Data was collected for seven pre-cooperation and eight cooperative orders over a period of 14 months. The results of Mann-Whitney U-test show a significant average reduction of 30.8% in the duration of lead times. Lead-time variability was also found to be reduced as the result of changes in the coefficient of variances and the Bartlett's test for homogeneity of variances. Horizontal collaboration in transport could eventually lead to reduced lead times and lower variability of lead time which results in reduced supply chain costs. This can be achieved by means of direct routing and avoiding multi-transshipment routes which reduces the uncertainty and variability by diminishing the number of stages in the transport chain. Thus, effective lead-time management is considered a source of competitive advantage as it can reduce supply chain costs by lowering inventory levels, but is also capable of improving performance and customer service by offering improved product quality service levels.

Understanding Sustainability Data through Unit Manufacturing Process Representations: A Case Study on Stone Production

Efficiency of natural stone production processes in quarries directly affects the economic output and environmental performances, such as production lead times and energy consumptions. Knowledge on stone production processes is crucial in making responsible decisions in this business. Having a structured representation of information characterizing the stone production processes will support stakeholders in better assessing production resources in terms of sustainability and productivity. Value stream mapping can provide an overview and guidance for sustainability performance evaluation, but its application is limited. The challenges arise when trying to specifically map and relate sustainability data between processes e.g., variability in lead time and CO2 emissions. Manufacturing process characterization standards currently being developed by ASTM International manifest the potential to not only fill this gap but also to provide opportunities to characterize and compose manufacturing processes with relevant environmental information and description. This paper shows the application and lessons learned from deploying one such effort towards standardization.

A note on the joint economic lot size model with stochastic demand and variable lead time

This paper considers the problem of choosing optimal production policy in an integrated production-inventory system comprising a single manufacturer and a single buyer. Two production scenarios are compared, namely continuous and lot-for-lot production policies, using a mathematical modelling approach and the expected total joint cost is considered as the performance measure. In addition, the lost opportunity cost due to production downtime in the lot-for-lot policy, including the cost of unemployment of human resources and stoppage of the production line, is incorporated into the model. Numerical experiment and sensitivity analysis in terms of setup and lost opportunity costs are provided to illustrate the theory and the percentage of difference in the expected total cost of the two policies is analysed. The presented approach helps managers to make the right decisions on the optimal production policy under different conditions, especially where expert and technical human resources and/or expensive production equipments are utilised.

A collaborative supply chain inventory model with defective items, adjusted production rate and variable lead time

This paper proposes a production-inventory model for supply chain system consisting of single-vendor and single-buyer. The buyer uses periodic review policy to manage his inventory. The lead time is formulated by considering production time and transportation time. Moreover, the demand in buyer side is assumed to be normally distributed and the shortage is assumed to be fully backordered. The production process maybe deteriorated during production time hence the lot received by the buyer may contain some defective items. The defective rate of the items is assumed to be fixed. The objective of the proposed model is to determine review period, production rate and number of deliveries to minimise the joint total cost. An iterative procedure is proposed for finding the solutions of the model. A numerical example and sensitivity analysis are illustrated to show the model's behaviour.

A collaborative supply chain inventory model with defective items, adjusted production rate and variable lead time

This paper proposes a production-inventory model for supply chain system consisting of single-vendor and single-buyer. The buyer uses periodic review policy to manage his inventory. The lead time is formulated by considering production time and transportation time. Moreover, the demand in buyer side is assumed to be normally distributed and the shortage is assumed to be fully backordered. The production process maybe deteriorated during production time hence the lot received by the buyer may contain some defective items. The defective rate of the items is assumed to be fixed. The objective of the proposed model is to determine review period, production rate and number of deliveries to minimise the joint total cost. An iterative procedure is proposed for finding the solutions of the model. A numerical example and sensitivity analysis are illustrated to show the model's behaviour.

The Lead Time Syndrome of Manufacturing Control: Comparison of Two Independent Research Approaches

The aim of production planning and control is to ensure the achievement of the logistic targets of high due date reliability, low lead times, high capacity utilization, and low WIP levels, while maintaining productivity and quality targets. If order due dates are missed, a common intuitive reaction of production planners is to adjust planned lead times. How often and to what extent updates are reasonable has previously been unclear because, while trying to improve the logistic target achievement, planned lead time adjustments may actually cause an opposite effect, which is known as the Lead Time Syndrome (LTS) of Manufacturing Control [1]. Previous research on the LTS interactions has shown that the line of argumentation of the LTS is valid [2]. Knollmann et al. showed by means of mathematical modeling, control-theoretic simulation and case study research that planned lead time adjustments lead to a short-term increase in lead time variation, thus to an increase in lateness variation and to a decrease in due date reliability [2–5]. The authors suggest to choose update frequency depending on the ratio of latency period and the update frequency (the period between two consecutive adjustments) as the misbalance of these two parameters turns out to be the main trigger of the LTS. Selçuk investigated the LTS by means of queuing theory in an independent approach [6–8]. The authors concluded that planned lead time adjustments lead to an increase in process variability, thus to high WIP levels and long lead times. However, they suggest to reduce update frequency, to decrease process variability and thus to avoid LTS. This conclusion is not in line with the conclusions drawn from the research presented by Knollmann et al.. Therefore, this paper compares the different research approaches methodologies and discusses how the different research methodologies impact the conclusions drawn for practice application. This comparison provides further insights into LTS research and indicates further research fields.

Inventory management with variable lead-time dependent procurement cost

A reduction in the inventory replenishment lead-time allows reducing safety stock requirements and improving customer service. However, it might be accompanied by increased procurement costs because of premium charges imposed by suppliers, or higher transportation costs. This paper studies a single-stage variable lead-time inventory system with lead-time dependent procurement cost. Selection of the lead-time value represents finding the trade-off between benefits of lead-time reduction and increase in the procurement cost. A model for joint optimization of inventory and procurement costs is developed. Numerical studies are conducted to identify conditions under which lead-time reduction is favorable compared to procuring at the lowest cost.

Analysis of dynamic decision making underpinning supply chain resilience: A serious game approach

Abstract: This paper proposes a serious game approach using the ColPMan game to analyze the dynamic decision-making process underpinning supply chain resilience. In this approach, a newly developed serious game named ColPMan is used. The players operate a virtual supply chain of a large-scale make-to-order company by making its production and delivery decisions. The game generates decision logs and contents of conversations among the players, which are then analyzed with a protocol analysis method, which treats communicative interactions observed in the game session as collaborative efforts to find and solve problems. The virtual supply chain in the game is composed of a headquarters accepting orders from customers, an upstream factory producing materials, and three downstream factories processing materials into products. Each player is assigned to one of the sites and makes its production and delivery plans. The game probabilistically incorporates both stationary and non-stationary environmental disturbances, such as lead-time variations, defective materials and products, and machine breakdowns. The players aim to maximize the profits of the whole supply chain. Since operations at the different sites of the chain are related, the players should learn to create a shared strategy and act in coordination to manage disturbances to obtain a high score. The proposed approach is tested through experiments with different scenarios of environmental disturbances. The experimental results confirm how the players tend to develop discussion and share awareness of the stationary and/or non-stationary disturbances in their group decision-making process.

Replenishment Policy for Items Having a Fixed Shelf Life under Permissible Delay and Variable Lead Time

All organizations whether manufacturing or service have to keep inventory for smooth running of their business processes. This study is devoted to the items like medicines, cosmetics which are having a fixed shelf life, i.e. they will be of no use after some prescribed time. This model also considers the permissible delay which means that the buyer can pay for goods after some fixed time and has to pay interest after that fixed time. The demand considered here is fixed constant demand. The lead time varies as per the availability of the product and follows normal distribution.

Minimax Distribution-Free Procedure for Mixture Inventory Model with Variable Lead Time and a Service Level Constraint by Reducing Order Cost

SYNOPTIC ABSTRACTAs supply chains have become more extended in recent years, coordination and information sharing among supply chain members to improve system efficiency have attracted much interest. In order to achieve a higher degree of coordination and automation among the supply chain parties, the supply chain sometimes invests in reducing the ordering cost to streamline and speed up transactions via the application of information technology. If the ordering cost per order could be reduced effectively, then the total relevant cost per unit time could be automatically improved. In this study, we investigate a continuous review inventory model involving controllable lead time for the mixtures of distributions of the lead time demand, subject to a service level constraint in order to accommodate more practical features of the real inventory systems. Further, ordering cost can be reduced by an investment in which the optimal inventory control approach is shaped to account for the service level restriction.We assume that any mixture of distribution functions of the lead time demand gives only known finite, first two moments, but its probability distribution is unknown, and we apply a minimax distribution-free procedure to find the optimal solution. Moreover, a computational algorithm with the help of a computer code using the software MATLAB 7.0 is developed to solve the problem. A numerical example is presented to illustrate the theoretical results, and a sensitivity analysis is performed with respect to the various system parameters. Through sensitivity analysis, some observations and managerial implications are presented.

Optimal Timing of Inventory Decisions with Price Uncertainty

What is the optimal time for a firm to buy inventory to sell in a product market in which the selling price and demand are random and their forecasts improve with time? What is the value of order timing flexibility to the firm? What lead times would a supplier see? To answer these questions, we develop a continuous time inventory model where demand and price are realized at the horizon date T, and the stocking decision can be made at any time in the interval [0,T] given progressively more accurate forecasts of price and demand and a time dependent purchasing cost. We show that the optimal timing of inventory ordering decision follows a simple threshold policy in the price variable with a possible option of non-purchasing, and is independent of the demand. Given this policy structure, we evaluate the benefits of timing flexibility using the best pre-committed order timing policy as the benchmark. We find that the time flexible ordering policy is particularly valuable in the high price volatility or low margin environments. Higher price volatility leads to a greater postponement in the purchasing decisions. In addition, decreasing profit margins lead to a higher lead time variability.

Enhancing Nursing Staffing Forecasting With Safety Stock Over Lead Time Modeling

In balancing competing priorities, it is essential that nursing staffing provide enough nurses to safely and effectively care for the patients. Mathematical models to predict optimal "safety stocks" have been routine in supply chain management for many years but have up to now not been applied in nursing workforce management. There are various aspects that exhibit similarities between the 2 disciplines, such as an evolving demand forecast according to acuity and the fact that provisioning "stock" to meet demand in a future period has nonzero variable lead time. Under assumptions about the forecasts (eg, the demand process is well fit as an autoregressive process) and about the labor supply process (≥1 shifts' lead time), we show that safety stock over lead time for such systems is effectively equivalent to the corresponding well-studied problem for systems with stationary demand bounds and base stock policies. Hence, we can apply existing models from supply chain analytics to find the optimal safety levels of nurse staffing. We use a case study with real data to demonstrate that there are significant benefits from the inclusion of the forecast process when determining the optimal safety stocks.

Prior Entry for Feature-based Attention: Are Objects of the Attended Color Perceived Earlier?

Prior entry refers to the hypothesis of attended objects being perceived prior to unattended objects. In the current study, we employed the paradigms of temporal order judgment (TOJ) and simultaneity judgment (SJ) to investigate prior-entry effects for objects of the attended feature (color). Stimuli comprised two differently oriented bars equidistant from fixation, presented either simultaneously or successively, with a variable stimulus-onset asynchrony between them. Color feature cues preceded the stimuli with variable cue lead-time. To reduce the confounding effect of response bias, we employed an orthogonal judgment method in the TOJ task: observers reported the temporal order based on the orientation, rather than the color of the stimuli. Using the TOJ paradigm, significant prior-entry effect based on attending to the color feature was/was not observed when observers performed/did not perform an attentional task on the color cue. A similar trend of effect was observed with the SJ paradigm, but the effect was not statistically significant. One possibility is that there are separate mechanisms subserving TOJ and SJ tasks, and the mechanism underlying the TOJ task is more likely to reveal prior-entry effect.

Modelling supply chain inventory system with controllable lead time under price-dependent demand

In this paper, we study a two-echelon supply chain inventory system with a single manufacturer and a single buyer, considering price-dependent demand and variable lead time. Lead time can be reduced by added crashing cost. We first assume that the lead time demand follows a normal probability distribution and then consider the case where it is distribution free. We develop both the centralized and decentralized models and derive the optimal solutions by using profit maximization criterion. Feasibility and efficiency of the proposed models are justified through two numerical examples. We numerically investigate how the optimal decisions are affected by changes in various model-parameters. From numerical study, we observe that even though the lead time reduction diminishes the retailer’s selling price, it enhances the system profit significantly.

Influence of lot size and planned lead time on service level and inventory for a single-stage production system with advance demand information and random required lead times

In this paper a single-stage make-to-stock production system with advance demand information and random required lead times is investigated which produces different items with item specific lot sizes and planned lead times. Stochastic modeling is applied to investigate the influence of the interrelated planning parameters lot size and planned lead time on the key figures service level and inventory. The interrelation between the planning parameters is implemented with an endogenous production lead time distribution which depends on the lot size. Furthermore, both planning parameters lead to a pre-production on stock either anonymous (lot size) or of known customer orders (planned lead time). Most analytical studies in this field either focus on planned lead time optimization, assuming single item processing or predefined production order processing distribution, or on the optimization of production lot sizes, neglecting the planned lead time effect. The main contribution of this paper is that it provides a simultaneous treatment of these two planning parameters linked to an endogenous production lead time. Analytical results enable the numerical optimization of both planning parameters to reach a service level constraint with minimum overall inventory and can, e.g., be applied to discuss the effect of customer required lead time variance on optimal planning parameters. Numerical study results indicate that for moderate service level objectives, optimal lot sizes are near the production lead time minimizing lot size. Furthermore, the results show that higher lot sizes can reduce the service level for predefined planned lead time values.

Periodic review fuzzy inventory model with variable lead time and fuzzy demand

AbstractThis paper investigates a periodic review fuzzy inventory model with lead time, reorder point, and cycle length as decision variables. The main goal of this study is to minimize the expected total annual cost by simultaneously optimizing cycle length, reorder point, and lead time for the whole system based on fuzzy demand. Two models are considered in this paper: one with normal demand distribution and another with a distribution‐free approach. The model assumes a logarithmic investment function for lost‐sale rate reduction. Furthermore, two separate efficient computational algorithms are explained to obtain the optimal solution. Some numerical examples are given to illustrate the model.

On an EOQ model with service level constraint under fuzzy-stochastic demand and variable lead-time

This study deals with an EOQ inventory model with fuzzy stochastic demand and controllable lead-time by relaxing the assumption that demand during lead-time follows a specific probability distribution. Here considering the unsatisfied demands to be partially backordered, both lead-time and order quantity are considered as the decision variables. Instead of having a stockout term in the objective function, a service level constraint, which implies that the stockout level per cycle is bounded, is added to the model. This study, we provide an elegant methodology to determine the optimal order quantity and reorder point such that total expected annual cost in fuzzy sense has a minimum value. Finally the proposed model is illustrated by a numerical example.

Continuous review inventory model with reducing lost sales rate under fuzzy stochastic demand and variable lead time

The present study considers a continuous review inventory system for the inventory model involving fuzzy random demand, variable lead-time with lost-sales (backorder) caused by stock-out. Two forms of capital investment cost function viz. logarithmic and power are employed to reduce the lost-sales rate. We first formulate the basic model mathematically by assuming expected demand as triangle fuzzy number along with the capital investment to reduce lost-sales rate. As a result, the demand during lead-time is fuzzy random variable which is the linear sum of the weekly demand during the lead-time period. Consequently, the total weekly cost of this model is also fuzzy random in nature. Hence the expected total weekly cost is fuzzy valued function. To defuzzify the expected total weekly cost, we use the signed distance method. A computer program using the software MATLAB is developed to obtain the optimal solution and provide numerical examples to illustrate the models. Moreover, sensitivity analysis is carried out with respect to the key parameters.

Inventory Model with Fixed and Variable Lead Time Crashing Costs and Controllable Backorder Rate

Inventory Model with Fixed and Variable Lead Time Crashing Costs and Controllable Backorder Rate