Lead time management

doi:10.1016/0377-2217(83)90235-7

European Journal of Operational Research

Volume 14, Issue 4, December 1983, Pages 351-358

https://doi.org/10.1016/0377-2217(83)90235-7 Get rights and content

Abstract

In intermittent production systems, job shops, manufacturing lead times are often long and variable, yet only about 10% is due to the actual processing time. This is a major difficulty in planning production. Two alternative approaches exist for determining planning values for manufacturing lead times to use in production planning and control systems. The first is to treat them as independent uncontrollable variables. It is then a forecasting problem with the emphasis on minimising the impact of the forecasting errors. The second approach puts the emphasis on control and attempts to manage the average lead times to match pre-determined norms. This article reviews and compares these two approaches. It concludes that the second is the most appropriate but that it requires a close cooperation between the production and marketing functions of the firm. This is best achieved by regarding the firm as a hierarchical chain of backlogs connected by input/output relations.

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Cited by (64)

Average reward adjusted deep reinforcement learning for order release planning in manufacturing
2022, Knowledge-Based Systems
Citation Excerpt :
Waiting times are a result from queuing (e.g. @jobs queue before and after processing) and depend heavily on the amount of jobs in the system (i.e. the WIP). Thus, they are relatively difficult to estimate, making the setting of lead times a challenging task (e.g., [6,7]). Most order release models assume that lead times are static or fixed, and thus neglect the nonlinear relationship between resource utilisation and flow times, which is well known from practise and queuing theory [8].
One of the key challenges in production planning, especially in discrete manufacturing, is to determine when to release which orders to the shop floor. The major aim of this planning task is to balance Work-In-Process (WIP) and utilisation levels together with timely completion of orders. The two most crucial attributes of production planning are (i) the highly nonlinear relationship between WIP, flow times and output, and (ii) the dynamically changing environment. Nonetheless, most state-of-the-art models use static lead times to address this problem. Only recently, some papers set lead times dynamically based on the flow time forecasts to react to the dynamic operational characteristics reporting promising results. This paper contributes to this line of research by presenting an order release model that uses reinforcement learning (RL) to set lead times dynamically over time. The applied RL agent is especially designed for processes with periodic feedback and highly variable context. We compare the performance of our new RL algorithm to static order release models and state-of-the-art deep Q-Learning agents by using a multi-stage, multi-product flow-shop simulation model. The results show that, especially for scenarios with high utilisation, our proposed method outperforms the other approaches.
The lead time updating trap: Analyzing human behavior in capacitated supply chains
2021, International Journal of Production Economics
We analyze the lead time syndrome ( $L T S$ ), which describes a positive feedback loop between lead time updating and increasing order quantities. While previous studies have focused on theoretical analysis and anecdotal evidence, we examine the impact of human decision making with the help of controlled laboratory studies using a novel experimental design of a simple two tier supply chain. We show that the theoretically postulated phenomenon of the $L T S$ can be reproduced in the lab including human decision makers. We find that the dynamic (load dependent) lead time in the supply chain serves as coordination mechanism among retailers, i.e. experimental participants, but that this mechanism can be distorted by less reliable lead times. Comparing the experimental results to an optimization model in a rolling horizon setting, we further argue that human behavior initiates the vicious cycle of the $L T S$ in our experiments. Finally, we find individual-level evidence for behavioral decision-making, in particular hedging behavior, in our experiment, due to various forms of uncertainty. We discuss the implications of these results in the light of supply chain and production planning systems.
Application of time series data mining for the prediction of transition times in production
2020, Procedia CIRP
Transition times between value-adding operations often account for more than 90 % of lead times in workshop productions and vary greatly from order to order. However in day-to-day business, they are treated as static master data or only roughly estimated. One approach for a more precise prediction of future transition times of production orders is time series data mining. This paper presents a first application approach as basis for improving the quality of production planning. Based on empirical findings, future research issues are derived.
Measurement and optimization of responsiveness in supply chain networks with queueing structures
2018, European Journal of Operational Research
Citation Excerpt :
Our work is also related to the literature on workload control in make-to-order systems. Studies on workload control date back to the 1980s (see e.g., Kingsman, Tatsiopoulos, and Hendry, 1989 and Tatsiopoulos & Kingsman, 1983) and focus on controlling order release into the shop floor to protect the throughput against variability in complex manufacturing networks. Oosterman, Land, and Gaalman (2000) and Sabuncuoglu and Karapinar (2000) consider a periodic release method in which accepted orders are released only at periodic intervals according to some rule.
In this paper we consider supply chains with multiple stages of serial or network structure. The supply chains are endogenous in the sense that they involve queues because each order’s lead-time is dependent on the orders already in the system. We define supply chain responsiveness as the probability of fulfilling customer orders within a promised lead-time and study the problems of measuring and optimizing supply chain responsiveness using queueing network models. We first consider a single-server multi-stage serial supply chain and find a closed form expression for the fulfilment time distribution. For the multi-server multi-stage problem, the closed form evaluation of the fulfilment time distribution becomes intractable due to the dependency of the lead-times in different stages. We circumvent this difficulty by proposing a novel FCFS discipline which enables a closed-form analysis. For the multi-server multi-stage Jackson-type supply chain network, to enable analysis, we convert the system into an equivalent single server single stage system with state-dependent rates. For each case, we present detailed numerical examples for both measurement and the optimization of supply chain responsiveness.
Random lead times in replenishment planning for single-level assembly systems: The value of information
2017, IFAC-PapersOnLine
Lead times and their variability have a key role to play in multi-component assembly systems, directly impacting their performance. They are rarely known exactly in advance, being generated by various sources of uncertainty. This paper focuses on the stochastic solution of the replenishment planning problem of a single-level assembly system, in the framework of which: (i) procurement decisions must be made before the fixed demands are realized, and (ii) backlogs are authorized. By approaching this problem in the context of stochastic programming, we discuss the value of (stochastic) information for, subsequently, characterizing and better apprehending the grade of stochasticity and its effect on the corresponding solutions.
Rule-based vs. optimisation-based order release in workload control: A simulation study of a MTO manufacturer
2012, International Journal of Production Economics
Citation Excerpt :
The basic release procedure is our reference for the rule-based approach described in Section 1. Since there is no multi-period planning model, order release mechanisms of this type are largely independent of detailed demand forecasts and time-phased load projections (Bechte 1980, p.71; Tatsiopoulos and Kingsman 1983, p. 356). An alternative approach to designing order release systems – and the second research tradition in our comparison, termed optimisation-based approach – is multi-period models for order release planning (for a description, see Missbauer and Uzsoy, 2011).
The paper presents a simulation study that compares multi-period models for order release optimisation (Input/Output Control with fixed lead times and a clearing function model) with a traditional workload control-based order release mechanism (control of aggregate workload with periodic release). For the optimisation models the study assumes periodic re-planning and thus can also assess the effects of demand predictability. The simulation model is based on a practical case of a manufacturer of optical storage media. The results indicate that optimisation models for order release planning largely outperform traditional workload control-based order release mechanisms even in the case of poor demand forecasts.

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Invited reviewLead time management

Abstract

European J. Operational Res.

A comparison of capacity planning techniques in a job-shop control system

Management Sci.

Time series analysis in determining endogenous due-dates in job-shop simulation studies

Introduction to Sequencing and Scheduling

An investigation of due-date assignment rules with constrained tightness

J. Operations Management

De levertijd in machinefabrieken. Een orienterend onderzoek

Steuerung der Durchlaufzeit durch belastungsorientierte Auftragsfreigabe bei Werkstattfertigung

Integrating capacity planning and capacity control

Production and Inventory Management

Input/output illustrated

Production and Inventory Management

Produktionsreihenfolgen und Losgrössen

Werkstattstechnik

Production Control and Information Systems for Component-manufacturing Shops

The effect of workload control on order flow-times

Theory of Scheduling

Due-dates in job-shop scheduling

Internat. J. Production Res.

Stand und Entwicklungsmöglichkeiten der Fertigungssteuerung unter Berücksichtigung personenbezogener Auswirkungen

Ermitteln von Durchlaufzeiten zur Auftragsterminierung mit EDV

Rationalisierung

Due-dates and instantaneous load in the one-machine shop

Management Sci.

Die Analyse der Fertigungsdurchlaufzeit im Industriebetrieb

Production planning and control integrated

Harvard Business Rev.

A loading and balancing methodology for job-shop control

A loading and balancing methodology for job-shop control

AIIE Trans.

Networks of waiting lines

Operations Res.

Kapazitätsterminierung zur Bestandsregelung in der Werkstattfertigung

Invited review
Lead time management