Dynamic Lead Time Research Articles

Enhancing Supply Chain Efficiency with Predictive Analytics: A Machine Learning Approach to Dynamic Lead Time Prediction in a Chemical Industry

We develop and employ a four-step methodological approach for predicting the lead time delays in all echelons of a supply chain (SC). The first step of the methodological approach involves a critical synthesis of academic research efforts for identifying the main sources of delays in all echelons of a supply chain. The second step involves the development of questionnaires for validating the findings of the research through workshops with industry stakeholders. The third step involves the development of a suite of machine learning (ML) models, namely, Random Forest Regression, Decision Tree Regression, and Linear Regression. These models were selected based on their prevalence in the recent literature and their ability to handle linear and nonlinear relationships between multiple variables. The final fourth step involves the implementation of the suite of machine learning models in the real case of a Hellenic chemical manufacturing supply chain. The implementation results reveal that Random Forest Regression exhibits the highest predictive accuracy throughout all stages of the supply chain, achieving the lowest Mean Absolute Percentage Errors (MAPE), ranging from 0.5 to 7% in the examined supply chain echelons.

Single versus dynamic lead-time quotations in make-to-order systems with delay-averse customers

Single versus dynamic lead-time quotations in make-to-order systems with delay-averse customers

Impact of carbon emission reduction on supply chain model with manufacturing decisions and dynamic lead time under uncertain demand

Consignment stock policy plays a crucial role in stock management to accomplish more significant benefits for supply chain players. Accordingly, in this research, we developed an inventory model based on the vendor managed inventory and consignment stock agreement by delivering a single item from the single vendor to a single buyer under controllable lead time. Manufacturing industries are a major source of worldwide greenhouse gas emissions. Consumption of larger energy through non-renewable sources increases the use of fossils and coal. Though, this produces more waste and has a negative impact on the environment. In contrast to earlier research, logarithmic investment is employed to control the amount of carbon generated throughout the manufacturing process. The demand rate is treated as a pentagonal fuzzy number to account for the uncertainty. The primary goal of this study is to maximize gross profit with various realistic factors and reduce carbon emissions from the production process. Five numerical experiments, comparison and sensitivity analysis are performed to validate the proposed model. Finally, the benefits of variable pricing and investing in carbon reduction will assist industry managers in effectively managing pricing and inventory strategies to adopt a sustainable supply chain in an uncertain environment.

Fair and profitable: How pricing and lead-time quotation policies can help

In this paper, we propose four policies to serve price and lead-time sensitive customers with a single type of product produced in an M/GI/1 type make-to-stock queueing system. The policies are developed to observe certain principles of fairness: if a customer is quoted a longer lead-time, she/he must be charged a lower price and a significant proportion of the deliveries has to be made during the quoted lead times. Although handling non-exponential service times in this setting presents difficulties, our analysis of the proposed policies is exact. Two of the policies operate with static prices, while one of the two dynamic pricing policies also quotes dynamic lead times. By construction of the policies, we show that the dynamic pricing policies are more profitable. Numerical examples bring additional light by showing that in small markets with oversensitive customers, dynamic policies can be profitable while static pricing policies can fail. In a larger market, a simple dynamic policy charging two prices depending on the stock availability can be a reasonable compromise. Dynamic policies tend to suffer less against high production time variability as well.

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center.

Adaptive Order Release Planning with Dynamic Lead Times

An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. One key parameter is the lead time which is defined as the planned time that elapses between the release of an order and its completion. Lead times are normally determined based on the actual duration that an order takes to traverse through the production system (flow times). Traditional order release models assume static lead times, although they should be set dynamically to reflect the dynamic operational characteristics of the system. Therefore, we present a cycle time estimation procedure to set lead times dynamically based on exponentially smoothed flow time forecasting. Additionally, we implement a safety lead time to incorporate the underlying cost ratio in the order release model. We use a simulation model of a three-stage flow-shop and show that our approach outperforms other forecast based order release mechanisms from literature.

Robust single-item lot-sizing problems with discrete-scenario lead time

In this paper, we consider the minmax robust lot-sizing problem (RLS) with uncertain lead times. We explore the uncapacitated (RULS) and capacitated (RCLS) cases with and without lost sales, with discrete scenarios. We provide a complexity analysis proving that robust lot sizing problems are NP-hard even in the case with two scenarios. For each case, a mixed integer programming model is proposed. We show that several optimality conditions for the deterministic cases provided in Wagner and Whitin (1958),Aksen et al. (2003), as well as a classical facility location based model, are no longer valid. However, we generalize these properties to the lot sizing problem with deterministic but dynamic lead times and demonstrate their validity. Numerical experiments on instances with up to 400 periods and 25 scenarios reveal that, against all expectation, the capacitated version of the robust lot sizing problem is easier to solve than the uncapacitated version.

Optimization and Customer Utilities under Dynamic Lead Time Quotation in an M/M Type Base Stock System

In a manufacturing and inventory system, information on production and order lead time helps consumers’ decision whether they receive finished products or not by considering their own impatience on waiting time. In Savaşaneril et al. (2010), the optimal dynamic lead time quotation policy in a one-stage production and inventory system with a base stock policy for maximizing the system’s profit and its properties are discussed. In this system, each arriving customer decides whether he/she enters the system based on the quoted lead time informed by the system. On the other hand, the customer’s utility may be small under the optimal quoted lead time policy because the actual lead time may be longer than the quoted lead time. We use a utility function with respect to benefit of receiving products and waiting time and propose several kinds of heuristic lead time quotation policies. These are compared with optimal policies with respect to both profits and customer’s utilities. Through numerical examples some kinds of heuristic policies have better expected utilities of customers than the optimal quoted lead time policy maximizing system’s profits.

Dynamic lead time quotation under responsive inventory and multiple customer classes

We address the lead time quotation problem of a manufacturer serving multiple customer classes. Customers are sensitive to the quoted lead times and the manufacturer has the flexibility to keep inventory to improve responsiveness. We model the problem as a Markov decision process and characterize the optimal lead time quotation, rationing, and production policies. We then define internal and external service level measures and analyze the impact of inventory keeping decision on these measures. Before analyzing the impact, we first derive the relation between inventory level and lead time quotes. We then show that the effect of inventory level on the service levels may not follow the intuition. We also study alternative lead time quotation and production schemes. We contrast the performance of these alternative policies with that of the optimal policy. Specifically, through a numerical study, we quantify the value of controlled arrivals and customer rejection, the value of information on customer status, and the value of a far-sighted policy. Through the numerical study, we also identify the effect of revenue mix and demand mix on the inventory keeping decisions and the performance measures. Finally, we measure the impact of lead time quotation on resource pooling (i.e., inventory and capacity pooling). We show that the value of resource pooling is limited under the optimal policy, since lead time quotation is already effective in balancing the demand with capacity, and in allocating the resources among different customer classes.

Coping with production time variability via dynamic lead-time quotation

In this paper, we propose two dynamic lead-time quotation policies in an M/GI/1 type make-to-stock queueing system serving lead-time sensitive customers with a single type of product. Incorporating non-exponential service times in an exact method for make-to-stock queues is usually deemed difficult. Our analysis of the proposed policies is exact and requires the numerical inversion of the Laplace transform of the sojourn time of an order to be placed. The first policy assures that the long-run probability of delivering the product within the quoted lead-time is the same for all backlogged customers. The second policy is a refinement of the first which improves the profitability if customers are oversensitive to even short delays in delivery. Numerical results show that both policies perform close to the optimal policy that was characterized only for exponential service times. The new insight gained is that the worsening impact of the production time variability, which is felt significantly in systems accepting all customers by quoting zero lead times, decreases when dynamic lead-time quotation policies are employed.

Simulation-based optimal production planning model using dynamic lead time estimation

Production planning and scheduling models for the semiconductor fabrication manufacturing are suggested in various types according to the objectives and factors taken into consideration. The lead time is one of the important factors in production planning, but there does not exist a simple way to describe and include it in the model. In this paper, the clearing time method is suggested to model the lead time in the fabrication process of the semiconductor manufacturing, so that the dynamically changing environment on the lead time and work in process (WIP) status are considered. It is shown through the experimental computation that the suggested model yields the accurate description on the lead time on the planning model, and the appropriate planning model with good results for the important performance measures in the semiconductor manufacturing.

Adaptive lead time quotation in a pull production system with lead time responsive demand

In this paper, a single-stage single-class kanban controlled manufacturing system is considered, where the problem is quoting accurate lead times for orders that are generated by an MRP system. Order release mechanism as in classical MRP is sensitive to the changes in the lead time according to the concept of lead time syndrome. The objective is to establish a cost effective lead time quoting procedure. The problem is modeled as a two-dimensional Markov chain, and it is solved explicitly by using matrix geometric techniques. Comparative analysis is done between static and dynamic lead time quoting procedures, and significant cost benefits of the dynamic procedure are shown under various scenarios. Guidelines for setting design parameters such as the number of kanbans and the frequency of updating the lead time are provided through numerical tests.

Material and capacity requirements planning with dynamic lead times

Traditional material requirements planning (MRP) does not consider the finite capacity of machines, and assumes fixed lead times. This paper develops an approach (material and capacity requirements planning; MCRP) to integrate capacity planning into MRP. To obtain a capacity feasible production plan, different measures for capacity adjustment such as alternative routeings, safety stock, lot splitting and lot summarisation, are discussed. Additionally, lead times are no longer assumed to be fixed. They are calculated dynamically with respect to machine capacity utilisation. A detailed example is presented to illustrate how the MCRP approach works successfully.

The Impact of Uncertainty in Lead Time and Demand in Disaster Relief Supply Chains

This paper presented a system dynamics model to study the impact of uncertainty in disaster-relief supply chains. The material flows and information feedback flows of both the commercial supply chains and disaster-relief supply chains were analyzed. The structures of four types of inventory planning strategies were compared. The effect of static/dynamic lead time and constant/random demand were tested in simulations with different decision methods. The simulation results indicated that both the dynamic lead time and the random demand bring more oscillations to the supply system, and the mutual influence of them makes the behavior of a disaster-relief supply chain different from a commercial supply chain. More decision-making issues need to be considered to solve that uncertainty comes from the mutual influence.

Adaptive Adjustment on Dynamic Lead Time Prediction in Disaster Relief Operations

Adaptive Adjustment on Dynamic Lead Time Prediction in Disaster Relief Operations

Dynamic Lead-Time Quotation for an M/M/1 Base-Stock Inventory Queue

We study a dynamic lead-time quotation problem in a base-stock inventory system characterized by lead-time sensitive Poisson demand and exponentially distributed service times. We show that the optimal profit is unimodal in the base-stock level. We compare the base-stock system with a make-to-order (MTO) system and show that the lead-time quotes are lower in an MTO system and that increasing the base-stock level does not necessarily decrease the expected number of customers waiting. Numerical results show that when customers are less sensitive to lead-time quotes, the benefit of quoting lead times with a higher precision is significant, whereas when customers are more sensitive to lead-time quotes, the benefit of holding inventory is significant.

Near-Optimal Dynamic Lead-Time Quotation and Scheduling Under Convex-Concave Customer Delay Costs

We consider a make-to-order system where customers are dynamically quoted lead times (and prices). Customers are homogenous but have general (nonlinear) disutility for delay. Because the firm is a monopolist, the pricing problem is trivial and the dynamic problem reduces to one of lead-time quotation and order sequencing. We also consider the (static) problem of up-front capacity installation. We use a large-capacity asymptotic regime to make the problem tractable. We provide recommended policies for convex, concave, and convex-concave lead-time cost functions and prove that these policies are asymptotically optimal. The policies are both highly intuitive and readily implementable. Moreover, they provide delay guarantees for all served customers. They are tested numerically; we find that significant benefits can accrue by using the prescribed dynamic policies instead of first-come-first-served type policies.

TIME‐BASED PRICING AND LEADTIME POLICIES FOR A BUILD‐TO‐ORDER MANUFACTURER

We studied time‐based policies on pricing and leadtime for a build‐to‐order and direct sales manufacturer. It is assumed that the utility of the product varies among potential customers and decreases over time, and that a potential customer will place an order if his or her utility is higher than the manufacturer's posted price. Once an order is placed, it will be delivered to the customer after a length of time called “leadtime.” Because of the decrease in a customer's utility during leadtime, a customer will cancel the order if the utility falls below the ordering price before the order is received. The manufacturer may choose to offer discounted prices to customers who would otherwise cancel their orders. We discuss two price policies: common discounted price and customized discounted price. In the common discounted price policy, the manufacturer offers a single lower price to the customers; in the customized discounted price policy, the manufacturer offers the customers separately for individual new prices. Our analytical and numerical studies show that the discounted price policies results in higher revenue and that the customized discounted price policy significantly outperforms the common discounted price policy when product utility decreases rapidly. We also study two leadtime policies when production cost decreases over time. The first uses a fixed leadtime, and the second allows the leadtime to vary dynamically over time. We find that the dynamic leadtime policy significantly outperforms the fixed leadtime policy when the product cost decreases rapidly.

Multistage Inventory Management with Expediting

After reformulating Clark and Scarf's (1960) classical serial multi-echelon model so that the lead time between adjacent echelons is one week (period), the option to expedite between each resulting echelon is added. Thus, each week requires a decision to be made at each echelon on how many units to expedite in from the next upstream echelon (to be received immediately) and how many to regular order (to be received in one week), with the remainder detained (left as is). The model can be interpreted as addressing dynamic lead time management, in which the (remaining) effective lead time for each ordered unit can be dynamically reduced by expediting and/or extended. Use of Clark and Scarf's (1960) idea of echelon stocks reduces a complex, multidimensional stocking problem to the analysis of a series of one-dimensional subproblems. What are called top-down base stock policies, which are readily amenable to managerial interpretation, are shown to be optimal. Myopic policies are shown to be optimal in the stationary, in1nite horizon case. The results are illustrated numerically.

MRP/sfx: A kanban-oriented shop floor extension to MRP

Materials Requirement Planning (MRP) and JustIn-Time (JIT)/kanban are two widely used production scheduling and control (PS& C) methodologies in the manufacturing industry. MRP has better planning capabilities, while JIT has superior shop floor control capabilities. MRP is applicable to a wide range of manufacturing companies, while successful JIT/ kanban implementation is generally restricted to repetitive and serial manufacturing. Several hybrid MRP/JIT systems have been proposed by researchers and practitioners to combine the benefits from both methods, while eliminating some of their drawbacks. A few companies have successfully implemented customized hybrid systems. However, there are no 'generic' methods by which JIT/kanban can be combined with MRP. This paper introduces a generic and widely applicable JIT/ kanban-type shop floor extension to MRP. Further, it incorporates specific techniques that eliminate the drawbacks of JIT/kanban and enable it to be used with MRP in a broad range of manufacturing plants. The techniques include: (i) the Kanban Card Controller, which links the MRP output to the JIT/kanban input; (ii) the Kanban Prioritizer, an optimal scheduling methodology, which enables JIT to be used effectively in non-serial production facilities; and (iii) a dynamic lead time estimator which calculates lead time based on the most current shop floor parameters.