Production Control Policies Research Articles

Closed-loop supply chains: How do production capacity and production control policies impact the performance?

The paper addresses the impact of manufacturing and remanufacturing capacity in a multi-product closed-loop supply chain. In the problem under investigation, the manufacturing system of the factory node is characterized by a failure-prone production line that is not able to manufacture both types of product simultaneously. Therefore, changeover operations are needed to switch from one product type to another. Since failure events and changeover times may involve unforeseen problems, the production control policy of the factory assumes a key role to enhance both the internal and external performance of the closed-loop supply chain. For that reason, this research compares four production control policies in terms of bullwhip effect, fill rate, and average inventory levels. We consider the well-established Hedging Corridor Policy and Improved Modified Hedging Corridor Policy, and two different versions, named Closed-Loop Hedging Corridor Policy and Closed-Loop Improved Modified Hedging Corridor Policy, which adapt the original policies to the features of the closed-loop supply chain. Through an extensive experimental analysis, the results can guide managers in assessing the effects of multi-product manufacturing and remanufacturing operations on the performance of closed-loop supply chains and in comparing the effectiveness of the production control policies.

Adaptive production control of two-product closed-loop supply chain dynamics under disruptions

ABSTRACT This paper addresses the dynamics of a two-product closed-loop supply chain with realistic assumptions on production capacity constraints. The closed-loop supply chain is also subject to unpredictable disruptions, which lead to the non-stationarity of customer demand. The factory employs a production control policy to decide the product type to be processed. We propose a novel production control policy, named the Adaptive Hedging Corridor Policy, which makes decisions on production capacity based on the demand evolution. The proposed strategy is compared with well-known production control policies widely used in literature, such as DDMRP. Simulation results demonstrate the benefits of implementing an adaptive production control as it enables the closed-loop supply chain to enhance customer service level and bullwhip effect. Additionally, a sensitivity analysis is provided to assess the influence of experimental factors on the performance. The analysis highlights the significance of return flows and manufacturing operations for the closed-loop supply chain.

An adaptive product changeover policy for a capacitated two-product supply chain in a non-stationary demand environment

ABSTRACT The present paper investigates the effectiveness, in terms of fill rate, of a new adaptive production control policy for a two-product, two-echelon (i.e. factory and retailer) supply chain subject to a non-stationary customer demand. To model the capacity constraints more realistically, the factory node consists of an unbuffered production line subject to failures, and non-negligible changeover times. To cope with the disruptive events, we propose a new production control strategy named Adaptive Hedging Corridor Policy. A simulation model based on discrete-time difference equations is developed, and a two-step experimental campaign is conducted to compare the proposed control policy with two well-known alternatives proposed in the literature. The analysis of the results demonstrates the effectiveness of the new adaptive strategy in maximising the fill rate of supply chains subject to a non-stationary demand.

Production and energy mode control of a production-inventory system

Energy efficiency in manufacturing can be improved by controlling energy modes and production dynamically. We examine a production-inventory system that can operate in Working, Idle, and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival, production and warm-up times, we formulate the problem of determining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy, inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival, production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint production and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary, we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system.

Optimal joint production, maintenance and product quality control policies for a continuously deteriorating manufacturing system

ABSTRACT This article presents a study with the aim of optimizing a production system, consisting of a machine that produces a single type of parts, that degrades over time. This degradation affects the availability of the deployed machine and increases the defective rate of the manufactured products. The machine is subject to random failures and repairs. It has a defective rate which increases with its degradation. An overhaul of the machine allows to reduce this defective rate and bring it back to its initial condition. The objective of this study is to find a joint policy for production, maintenance and quality control, in order to increase the availability of the machine, improve the quality of the manufactured products and minimize the total cost of production. To achieve this goal, we formulated the research problem and used a stochastic dynamic programming approach to develop the Hamilton-Jacobi-Bellman (HJB) type optimum conditions. Then, we simulated a practical hybrid application to optimize the production of a Router class R CNC cutting machine that transforms polypropylene sheets in a Quebec (Canada) company specialized in the production of fire pumps. The obtained results allowed us to propose a critical threshold production policy, corrective and preventive maintenance strategies, and a sampling type of quality control to the company. Finally, we performed a sensitivity analysis to ensure the validation of the proposed policies.

Preventive maintenance and silica exposure limits integrated in the production planning of a granite processing unit

PurposeThis paper studies the integration of production and maintenance planning for an unreliable production system subject to gradual deterioration. The goal of this planning is to optimize production and maintenance while reducing workers' exposure to silica dust. The objective will therefore be to offer manufacturers a production strategy that minimizes the total cost of production while considering the health of employees.Design/methodology/approachAdequate prevention methods are determined and integrated into the granite transformation production system, which evolves in a stochastic environment. With the failure rate of the dust reduction unit being a function of its degradation state, the authors solve the optimization problem using stochastic dynamic programming in the context of nonhomogeneous Markov chain.FindingsThe resulting planning strategy shows that one can manage stock optimally while ensuring a healthy environment for workers. It ensures that crystalline silica prevention equipment is available and effective and defines the production rate according to a critical threshold, which is a function of the age of the dust reduction unit.Research limitations/implicationsThis article illustrates that it is possible to integrate silica dust reduction measures into production planning while remaining optimal and ensuring the health of operators. In the present study, the machined granite was assumed to be a natural granite, and production takes place in a closed environment.Originality/valueThe originality of this work lies in its development of an optimal joint production and maintenance strategy, which considers limits of exposure to crystalline silica. An optimal production and maintenance control policy considering employees' health is therefore proposed.

Analysing the impact of production control policies on the dynamics of a two-product supply chain with capacity constraints

In this paper, we investigate the impact in terms of Fill Rate of production control policies in a two-product, two-echelon supply chain dynamic problem with production capacity. The factory node is subject to two different disruptive occurrences (i.e. failure events and changeovers) that can cause unforeseen problems in the supply chain. To control these adverse events, the factory can adopt different production control policies. We compare the well-known Hedging Corridor Policy with two variants, namely Modified Hedging Corridor Policy and Improved Modified Hedging Corridor Policy, and Demand-Driven Material Requirements Planning policy. Firstly, we use the Response Surface Methodology to calibrate the endogenous factors for each strategy. Then, through an extended full-factorial Design Of Experiments, we evaluate the effectiveness of the production control policies for several operational and market scenarios defined by varying exogenous factors. Interestingly, our study reveals that the Hedging Corridor Policy represents the best rule to increase the Fill Rate. The policies have been compared also in terms of indicators related to the factory inventory level. The results seem to point out that efficient production control policies (measured in terms of operational efficiency) do not necessarily yield the best results when measured in terms of supply chain efficiency.

Machine learning integrated design and operation management for resilient circular manufacturing systems

The implementation of smart manufacturing devices in the manufacturing industry has provided an effective and intelligent way to monitor their facilities and thus control their integrated manufacturing processes. Despite the ongoing technological advancements, the same industry is often involved with complex problems that are associated with increasing costs related to the persistent deterioration of the manufacturing/remanufacturing systems. In literature, these systems are often called “circular manufacturing systems”. Furthermore, this deterioration affects the quality of the manufactured products. To address these problems, a novel integrated design and operation management-based framework is introduced in order to obtain joint policies for the authorization of production, recycling, maintenance and remanufacturing activities in the context of deteriorating circular manufacturing systems. This framework utilizes a reinforcement learning technique and ad-hoc production control policies, such as Base Stock and CONWIP, in an effort to improve the flexibility and the resilience of the examined systems to the ever-changing customer demand. Finally, a series of simulation experiments evaluate the behavior and the efficiency of the proposed mechanism within the context of single-stage and 2-stage deteriorating manufacturing/remanufacturing systems. Results suggest that the presented approach can effectively generate sufficient inventory of ready-to-be-sold products due to the enhanced awareness of the ongoing customer demand compared to the traditional reinforcement learning joint control.

Integrated production and maintenance control policies for failure-prone manufacturing systems producing perishable products

The problem of integrated production and maintenance control of unreliable manufacturing systems evolving in a stochastic and dynamic environment is studied in this paper. The considered system is subject to degradation and the produced products are perishable with random shelf-lives. The literature of operations management for perishable products reports a correlation between the shelf-life of the product and the machine degradation. In fact, the latter results in shelf-lives reduction. Ignoring this correlation effect may result in inaccurate values of the shelf-lives and inefficient control policies. The objective of this paper is to develop a joint production and maintenance control policy that minimizes the total cost composed of backlog, inventory holding, disposal, and maintenance costs. The proposed parametrized joint control policy combines a multi hedging point policy and an age-based preventive maintenance policy. The optimization of the parameters of the proposed joint control policy is obtained using a simulation-based optimization approach and sensitivity analyses are provided to confirm its robustness. The obtained results show that the correlation between machine degradation and shelf-life reduction has a major influence on the control parameters and that preventive maintenance interventions can lead to increasing the shelf-life of products and minimizing the total cost. The proposed joint control policy is then compared to three other polices for a wide range of system and cost data. The obtained results show that the proposed joint control policy outperforms the other polices in terms of total incurred costs.

Modelling and analysis of a Markovian Production-Inventory Network

In this paper we model and study a stochastic manufacturing system with two stages in tandem that operates under a push type production control policy. The system produces a single part type and each stage consists of a machine and an output buffer. The machines are subjected to deterioration while operating and this deterioration results in decreased productivity. The production system is modeled as a continuous time Markov chain that describes the transitions between all system states due to the occurrence of demand arrival, deterioration failure, production completion and repair completion events. Solving the Markov chain numerically enables us to determine the steady state probabilities and this facilitates the calculation of useful performance metrics, namely system availability, down time, idle time, utilization and average inventory. We conduct a series of numerical experiments where we examine the behavior of the system for specified parameter set and gain insights on its properties.

Integrated production-delivery control policy for an unreliable manufacturing system and multiple retailers

Production control policies have been studied in the literature for different types of unreliable manufacturing systems. But only few have been addressed to the unreliable manufacturer-retailer systems. We consider the integrated production and delivery control for an unreliable manufacturing system and multiple retailers. The retailers, supplied by the manufacturer through a common warehouse, are located in different sites and face different demand rates. We determine the optimal joint production and delivery control policy with the objective of minimizing the total cost, which comprises holding/backlog in addition to transportation costs. The stochastic optimal control and the impulse control theory are used to develop the optimal control policy (policy 1). The production is controlled by a hedging point policy and the delivery policy is governed by a state dependent economic order quantity. The optimal parameters of the proposed policy are determined by adopting a simulation-based optimization approach. A sensitivity analysis is conducted to verify the robustness of the results. An improved policy (policy 2) involving priority rules for delivery is proposed using a simulation-based approach. We conduct a comparative study of the new policies (policy 1 and 2) and the most relevant policy found in the literature (policy 3), and the results show that the new proposed improved policy (policy 2) outperforms others.

Frustration in Oil Market Price Effect on External Reserves in Nigeria 1970-2017

This study examines frustration in oil market price shock effect on external reserves Nigeria 1970-2019. Objectives are; to examine frustration in oil market price shock on the Nigerian external reserves 1970 to 2019 and to ascertain the impact of frustration in oil price on the Nigerian external reserves. The study employed the following advanced econometric techniques; Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, Structural VAR approach, Choleski decomposition and Imposing Short-run Restrictions test, statistical tests & Co-integration test. Based on the above econometric techniques conducted, it was observed that all the variables used became stationary after the first differences at degree of order one I (I). There is Co-integration (long run relations) among variables used in the study. Our results indicated that oil price does significantly influence shocks on external reserves in Nigeria the period of the study. Furthermore, frustration in oil price does insignificantly have impact on the external reserves in Nigeria from 1970 to 2019. The researcher recommends that; less emphasis of control should be placed on international market oil price since the oil price has 81% influences of international market externalities ‘shock on Nigeria external reserves. Rather, more emphasis should be place on other non-oil sector contributions to Nigeria external reserves since it may has virtually neutral or significant internal control that could lend to positive effect on external reserves in Nigeria. Direct manipulation of cured oil production and supply control policy should be Nigeria interest since Nigeria economy operates system of floating exchange rates

Capacity Allocation of an Integrated Production and Service System

Manufacturing servitization has become a major trend in industry that is implemented by so called integrated production and service systems (IPSSs) to offer not only products but also their associated services. In this study, we explore the capacity allocation policy for an IPSS that consists of a manufacturing facility and a service center. The IPSS serves two classes of customers, each demanding a specific product produced at the manufacturing facility and, subsequently, the associated service offered by the service center. We formulate this problem of resource allocation as a Markov decision process. Our analysis suggests that it is optimal to serve the customer with the larger cost saving rate and slower service rate, where the cost saving rate equals service rate times the sum of holding cost and waiting cost. Then we explore production control policies coupled with optimal service ones. We demonstrate that, with light service traffic, the system under the optimal production policy becomes similar to a make‐to‐stock system. Such a system can effectively hedge the system uncertainty. Therefore, the optimal production policy for the IPSS is also a hedging point policy. Under this policy, switching and idling curves split the state space into three regions: one without any production, the two others for production of two type products, respectively. We also discuss the optimal control policies of the two extensions of the IPSS (one with multiple types of product and customers, and the other with a single product and two classes of customers) and find that the characters of the optimal production and service policy still hold. Finally, we develop three heuristic integrated scheduling policies. Numerical experiments show that one is very effective.

Development of a transboundary model of livestock disease in Europe.

Epidemiological models of notifiable livestock disease are typically framed at a national level and targeted for specific diseases. There are inherent difficulties in extending models beyond national borders as details of the livestock population, production systems and marketing systems of neighbouring countries are not always readily available. It can also be a challenge to capture heterogeneities in production systems, control policies, and response resourcing across multiple countries, in a single transboundary model.In this paper, we describe EuFMDiS, a continental‐scale modelling framework for transboundary animal disease, specifically designed to support emergency animal disease planning in Europe. EuFMDiS simulates the spread of livestock disease within and between countries and allows control policies to be enacted and resourced on a per‐country basis. It provides a sophisticated decision support tool that can be used to look at the risk of disease introduction, establishment and spread; control approaches in terms of effectiveness and costs; resource management; and post‐outbreak management issues.

Joint production, inspection and maintenance control policies for deteriorating system under quality constraint

This paper studies the integration of production, sampling inspection and age-based maintenance planning for an unreliable production system subject to gradual deterioration. The deterioration process of the production unit has a twofold effect on its reliability and product quality. To mitigate the effects of such deterioration, an age-based major maintenance can be conducted, which denotes a perfect repair that restores the production unit to initial conditions. The quality control is performed through a sampling plan that inspects a fraction of the parts produced. The problem further considers that the optimal decision must be determined under a constraint on the outgoing quality required by the final customer. In this domain, standard sampling procedures are applicable only to production process that are statistically stable and under control. Nevertheless, such sampling plans disregard the interaction with production management and maintenance issues and they do not consider the effects of deterioration. In this paper a new joint control policy considering the interactions between production-quality and maintenance is proposed. A stochastic mathematical model is developed through specialized optimization techniques to solve such quality constrained problem. Numerical examples are provided to illustrate the usefulness of the proposed approach and to study the interactions between production-quality and maintenance strategies. An extensive sensitivity analysis and a comparative study are conducted to illustrate the effectiveness of the obtained joint control policy.

A lab-scale manufacturing system environment to investigate data-driven production control approaches

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible before implementation. Similarly, using simulation models can result in overlooking critical aspects of the performance of a new control method. In order to overcome these shortcomings, using a lab-scale physical model of a given manufacturing system can be beneficial. We discuss the construction and the usage of a lab-scale physical model to investigate the implementation of a data-driven production control policy in a production/inventory system. As a data-driven production control policy, the marking-dependent threshold policy is used. This policy leverages the partial information gathered from the demand and production processes by using joint simulation and optimization to determine the optimal thresholds. We illustrate the construction of the lab-scale model by using LEGO Technic parts and controlling the model with the marking-dependent policy with the data collected from the system. By collecting data directly from the lab-scale production/inventory system, we show how and why the analytical modeling of the system can be erroneous in predicting the dynamics of the system and how it can be improved. These errors affect optimization of the system using these models adversely. In comparison, the data-driven method presented in this study is considerably less prone to be affected by the differences between the physical system and its analytical representation. These experiments show that using a lab-scale manufacturing system environment is very useful to investigate different data-driven control policies before their implementation and the marking-dependent threshold policy is an effective data-driven policy to optimize material flow in manufacturing systems.

Integrated Capacity Planning and Production Control of an Assembly Manufacturing System

For an assembly manufacturing system, its capacity planning problem and production control problem are usually considered separately. However, production capacity cannot exert its maximum productivity without employing optimal production control; and optimal production control may not satisfy the demand without sufficient production capacity. Therefore, it is important to jointly optimize capacity planning and production control. First, the mathematical formulation of integrated production control and capacity planning problem for an assembly manufacturing system with multiple upstream production lines and one downstream assembly line is proposed in this paper. Second, we develop a method to obtain the marginal probability distribution of the inventory level in the assembly system, which can be employed to calculate the average production cost under an approximate optimal production control policy. Since optimal control policy is subject to production capacity (i.e., machine number), we employ the integer programming method to optimize the machine number (i.e., production capacity), which consequently influences the control policy. Numerical experiments are conducted to verify the correctness of the method of calculating steady-state probability distribution, and the effectiveness of the algorithm of integrated optimization of production control and production capacity.

An Integrated Model of Production, Maintenance, and Quality Control with Statistical Process Control Chart of a Supply Chain

This article investigates integrated maintenance, production, and product quality control policy for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. The variation of the production rate and its use over time impact the manufacturing system’s degradation degree. Hence, the machine is subject to a random failure that directly affects the quality of the products. The goal of this study is to establish an optimal production and delivery planning with inventory management considering the production, holding, and delivery costs, and then an appropriate maintenance strategy, considering the influence of the production rate on the system degradation. Also, we provide a quality control policy to reduce the proportion of non-compliant products by using the statistical process control chart to forecast production. Forecasting the production aims to satisfy the varying demands during a finite horizon under service and quality levels. Numerical examples are presented to justify the effectiveness of the suggested strategy.

Integrated production, maintenance and quality control policy for unreliable manufacturing systems under dynamic inspection

This paper proposes a new joint production, maintenance, and dynamic sampling inspection control policy, for failure-prone manufacturing systems. The integrated policy help decision-makers to find both production rate and maintenance frequency, as well as the size of the sample to be inspected. The inspection policy is based on a dynamic sampling plan, which adapts the sample size according to the system degradation and takes into account the interactions between production, maintenance, and quality control. To optimize jointly the integrated policy's control settings, a comprehensive and thorough mathematical model is developed to capture the complex dynamic and stochastic behavior of the manufacturing system. The proposed model relaxes several simplifying assumptions so that reliability and quality degradations are operation-dependent, duration and cost of inspection are non-negligible, no restriction on random distributions and failures occur at any time, as in the real manufacturing world. Besides, a new signal-based simulation model is developed and a detailed sensitivity analysis is carried out to validate analytical results. The results show the relevance of the analytical approach and help us to prove the importance of adapting the inspection effort to the inner quality in the production. An extended comparative study is provided and shows that the proposed integrated policy, based on dynamic inspection, outperforms those based on classical inspection strategies considered in the literature and practice.

A machine learning approach for implementing data-driven production control policies

ABSTRACT Given the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.