Dynamics analysis of a production-inventory control system with two pipelines feedback

An Automatic Pipeline, Inventory and Order Based Production Control System (APIOBPCS), which is firstly proposed by Towill in 1982, lies at the heart of many commercial and bespoke ordering systems based on periodic review of stock and production targets. This simple and elegant control system works well, even when dealing with scenarios in which there are many competing value streams. However, when the demand is with time trend (such as ramp demand) rather than step changes, we show via linear s-transform analysis that there will be inventory drift in the APIOBPCS model. An innovative solution named PID-APIOBPCS model is proposed in this paper. In the new model, there are a PI controller in the inventory feedback loop which can eliminate the inventory drift and a PD controller in the order rate policy which can smooth the inventory level. At last, a simulation example is given to illustrate the benefit of PID-APIOBOBPCS model.

PurposeThis paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO).Design/method/approachThe production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation.FindingsThe results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions.Research limitations/implicationsThis research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level.Originality/valuePSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

This paper considers a well-established production and distribution scheduling algorithm, called the Automatic Pipeline, Inventory and Order Based Production Control System (APIOBPCS) within a Vendor Managed Inventory (VMI) supply chain. It develops a transfer function model of the system using causal loop diagrams, block diagrams, difference equations and z -transforms. Important insights into the VMI supply chain are derived from the mathematical model pertaining to the stability and robustness of the VMI system. Analysis confirmed by dynamic simulation clearly demonstrates instability arising from poor design. We also demonstrate its avoidance via our recommended parameter settings for tuning the two feedback loops within the supply chain for a specific production delay. The procedure is readily extended for other production delays and distributions.

This paper studies the well-established automatic pipeline feedback compensated inventory and order-based production control system (APIOBPCS) for a single echelon supply chain, in which the uncertain demand is modelled using the fuzzy numbers. The bullwhip effect is therefore measured as the possibility variance amplification in replenishment orders. It has been found that the bullwhip effect measured using fuzzy approach has similar features as the one measured using probability theories, and the sharper the fuzzy demand forecast, the smaller the bullwhip effect.

Multi-echelon inventory control is the main form of the management of supply chain inventory, and it has been paid extensive attention by scholars because of its importance in supply chain management. Process industries, represented by metallurgy, petrochemical and pharmaceutical industry, have typical characteristics such as high investment, high costs, and high resource utilization, they play a key role in industrialization. In this paper, the simulation model of a multiproduct three-echelon inventory control system is established for the hybrid supply chain of the process industry. Based on the idea of control engineering, a feedback control law is designed for inventory control. Several mixed inventory control strategies were applied to the model. The proportional plus integral control algorithm is used to modify the inventory control strategies. Based on this simulation model, a simulation-based optimization method is applied to the three-echelon inventory control system. The results show that the reciprocal of total entropy ratio and customer satisfaction are optimized by the mixed inventory control strategies when the hybrid supply chain is affected by order uncertainty.

Background: The high number of purchases created various impacts. It delayed other activities of RMSc CB operations. Inventory management staff had to spend most of working time for the procurement of supplies. This time constraint had affected the inventory control. In 2001, RMSc CB applied for the ISO/IEC 17025 accreditation. The nonconformance to the quality assurance system was found in the area of inventory management particularly in inventory control. The inventory scientific equipment control involve keeping detailed record on each piece of equipment, including such information were serial numbers, date the equipment was purchased, price, location, agency etc. The control and maintenance of scientific equipment supplies according to the quality assurance system had not been in place. The actual coding numbers labeled on scientific equipment were still the old numbers that were not matched with the number in the record of durable articles developed in 2001. Some equipment had not even been labeled with any numbers at all. This was not compliant to the quality assurance inventory control according to SENAC system (Scientific Equipment Name and Coding number system). The high number of purchases created various impacts. It delayed other activities of RMSc CB operations Inventory Staff had to spend most of working time for the procurement of supplies. This time constraint had affected the inventory control. Scientific equipment had not been controlled by SENAC system of the quality assurance system. This problem presented during 1999-2001 but no actions were done. According to the RMSc CB’s Vision in 2005, laboratory analysis should apply for ISO/IEC 17025 accreditation, Non-conformances have been found every time of the inspection for ISO/IEC 17025 accreditation of RMSc CB. More importantly, it had caused problems. Therefore, these problems had to be addressed as an urgent basis. This was based on the concept of work model according to “Open System Theory” of Robbins and Coulter, 1999, So that the working team was established to address the problems. They conduced a study in effort to find solution using SWOT analysis and solving problem. Objective: The goal of the project is to enhance the effectiveness of inventory management system of RMSc CB through the development of inventory procurement; inventory control; and improvement of related staff performance in order to achieve the following objectives. Methods: The project was implemented during 6 month consisting of 3 phases. The first phase focuses on preparation by reviewing existent inventory management system, planning and goal setting ,and design and developing supply and inventory management system. During the second phase the inventory staff’s had been trained and new system was tested. The last phase was an evaluation. Data were collected using both qualitative and qualitative methods including the use of survey, observation and in depth interviews. Results: The study found that the knowledge and skill of inventory staff have increased. The drop of annual number of the office supply purchase in 2003 reached the target of decreased of 50 percentage. And the coding numbers of all scientific equipment were matched and update with the ones in the inventory control system. The satisfaction levels of RMSc CB staff have increased as well. Conclusion: The project was successful in implementation. The evaluation program provided information about the results of the training in term’s knowledge and skills of inventory staff have increased. New inventory systems management could be decrease frequency purchase office supplies and error scientific equipment coding number. That could ultimately enhance staff’s satisfaction. However the overall program performance could be further improved.

The search for operational excellence includes both a thorough understanding of the nature of the manufacturing operations and a choice of the right production and inventory control system for the environment in which it will operate. Understanding the nature of manufacturing operations can be facilitated by simulating the manufacturing system, so that the interrelationships among parameters can be studied. Production and inventory control systems can be simulated together with the physical manufacturing environment in order to enhance the understanding of the behaviour of a particular control system and also to facilitate the selection of control systems for the manufacturing system under study. In this paper we report on the design and learning effects of using simulation for investigating the behaviour and impact of different production and inventory control systems in a manufacturing system. We discuss the structure and simulation perspectives of production and inventory control systems. We provide a review of the benefits of using simulation for learning the manufacturing environment, and the related testing of alternative control systems.

Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system

The aim of this paper is to investigate how different smoothing parameter levels of the Automatic Pipeline Inventory and Order Based Production Control System smoothing replenishment rule impact on the bullwhip dampening efficacy, under progressive information sharing strategies. The main results of this work are: (1) The smoothing parameter variations significantly impact on performance of the supply chains characterised by low information sharing level. (2) As smoothing parameters increase, the supply chain process performance improves and the customer service level worsens. This opposite trend noticeably decreases as information sharing level increases. (3) Amongst the bullwhip dampening techniques, deeper information sharing weights more than the value of smoothing parameters. The analysis is performed through continuous time differential equation modelling.

In recent years, the correct management of inventories has become a fundamental pillar for achieving success in enterprises. Unfortunately, studies suggesting the investment and adoption of advanced inventory management and control systems are not easy to find. In this context, this article aims to analyze and present an extensive literature concerning inventory management, containing multiple definitions and fundamental concepts for the retail sector. A systematic literature review was carried out to determine the main trends and indicators of inventory management in Small and Medium-sized Enterprises (SMEs). This research covers five years, between 2015 and 2019, focusing specifically on the retail sector. The primary outcomes of this study are the leading inventory management systems and models, the Key Performance Indicators (KPIs) for their correct management, and the benefits and challenges for choosing or adopting an efficient inventory control and management system. Findings indicate that SMEs do not invest resources in sophisticated systems; instead, a simple Enterprise Resource Planning (ERP) system or even programs such as Excel or manual inventories are mainly used.

Invisible stock loss often occurs in inventory management systems, which is usually caused by quality deterioration, theft, and misplacement and makes inventory record inaccurate. Inaccurate inventory record will bring significant negative impact on the performance of inventory control and leads to severe stock-out. To eliminate this negative impact, in this paper, we develop a Markov decision process (MDP) model for an inventory control system with invisible stock loss, inaccurate record, and replenishment lead-time. Based on the MDP model, a robust inventory control policy is constructed by using the policy iteration technique. From numerical experiments, we find that the robust policy still has a reorder point/order-up-to level structure i.e., an (s, S) structure, and can significantly reduce the impact of inventory inaccuracy.

In an inventory and order based production control system (IOBPCS), there are three fundamental system parameters. One of these, the production delay time, is beyond the control of the IOBPCS designer. Indeed, the production delay may be highly variable with time due to a variety of problems commonly found in manufacturing industry. This uncertainty must be countered by the proper use of a feedback loop which detects and operates on inventory deviations. The IOBPCS designer is thus left with the forward path demand averaging time and feedback path gain to adjust, as best he can. This paper studies the ability of the IOBPCS to recover from shock demands, and to protect the manufacturing process from random variations in consumption. It is shown that the feedback path is of fundamental importance in good control, and that adequate performance cannot be obtained by feedforward alone. Dynamic recovery is examined graphically by separately identifying the feedforward and feedback components. Random di...

This study researches on the Wudongde hybrid multi-terminal Ultra High-Voltage Direct Current Transmission (UHVDC) project (referred to as Wudongde transmission project in short) from the electromagnetic transient model and simulation and establishes an accurate simulation model of the hybrid multi-terminal UHVDC transmission system with the Power Systems Computer-Aided Design (PSCAD). The hybrid multi-terminal UHVDC transmission system consists of the primary system and the control system. The simulation model of the primary system adopts the parameters of the Wudongde transmission project, and the simulation model of the control system refers to the control system benchmark model of HVDC transmission and 9-terminal DC grid in the International Council on Large Electric Systems (CIGRE). According to the characteristics of the hybrid multi-terminal UHVDC control system, the benchmark model of HVDC transmission and 9-terminal DC grid control system in the CIGRE is modified accordingly, and a simulation model of the control system applicable to the hybrid multi-terminal UHVDC transmission system is established. The accuracy and credibility of the model are verified through the comparison of the recording waveform and the simulating waveform of the steady-state and transient operation. The simulating and field recording waveforms show that the model can accurately simulate the Wudongde transmission project and be used as an effective tool for further studying the electromagnetic transient characteristics of the hybrid multi-terminal UHVDC transmission system.

This paper eals with the dynamics of a computer-based inventory control system. In general Buch a real-life system is designed according to the most basic principles of static inventory theory. To control the system in a dynamic sense, management has to vary the policy parameters of the system. Two different mathematical models of inventory dynamics are introduced. The first ie a simple first-order differential equation and the second a discrete multidimensional model. Applications of the two models are discussed with the aid of examples concerning, on the one hand, changes in policy parameters, and on the other production smoothing.

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