Production Inventory Control Research Articles

Inventory control systems for stochastic lead time demand

Product inventory control can be used to provide information related to processes of production and distribution products based on lead time and product demand from consumers. The research aims to design product inventory control systems for stochastic lead time demand, and using exponential smoothing to make lead time demand prediction. The design of the control system uses the probability method based on the estimated number of product demand from the market or consumer. This research provides theoretical framework for control systems of products that can determine the size of inventory parameters in supply chain management, that is reorder point and fixed order quantity, so it can manage the production amount and supply of products according to market or consumer demand. The benefits of a product control systems in the inventory management are to provide the necessary inventory allocation information in making decisions to determine the amount of production and distribution of products to consumers.

DEVELOPING AGENT BASED HEURISTIC OPTIMISATION SYSTEM FOR COMPLEX FLOW SHOPS WITH CUSTOMER-IMPOSED PRODUCTION DISRUPTIONS

The study of complex manufacturing flow-shops has seen a number of approaches and frameworks proposed to tackle various production-associated problems. However, unpredictable disruptions, such as change in sequence of order, order cancellation and change in production delivery due time, imposed by customers on flow-shops that impact production processes and inventory control call for a more adaptive approach capable of responding to these changes. In this research work, a new adaptive framework and agent-based heuristic optimization system was developed to investigate the disruption consequences and recovery strategy. A case study using an Original Equipment Manufacturer (OEM) production process of automotive parts and components was adopted to justify the proposed system. The results of the experiment revealed significant improvement in terms of total number of late orders, order delivery time, number of setups and resources utilization, which provide useful information for manufacturer’s decision-making policies.

Discrete product inventory control system with positive service time and two operation modes

Discrete product inventory control system with positive service time and two operation modes

CONTINGENCY PRODUCTION-INVENTORY CONTROL POLICY FOR CAPACITY DISRUPTIONS IN THE RETAIL SUPPLY CHAIN WITH PERISHABLE PRODUCTS

Interrelations of product perishability and supply chain resilience are studied. The major distinguishing features in a supply chain with perishable products that may affect the resilience are the risks of goods write-off. Constraints on product perishability typically result in safety stock reductions and transportation frequency increase. Consideration of the production capacity disruption risks may lead to safety stock increase. The developed simulation model was run with data of a fast moving consumer goods company and revealed novel managerial insights and practical recommendations on inventory, on-time delivery and service level control. More specifically, the evidence for adjustment of production and inventory control policies during the disruption time is provided. Otherwise the delayed and backlog orders are accumulated over the disruption time. They transit as residues into the post-disruption period and do not allow full performance and operation dynamics stabilization even after full capacity recovery. An example of such a contingency production-inventory control policy is presented.

Analysing the impact of different classical controller strategies on the dynamics performance of production-inventory systems using state space approach

PurposeThe purpose of this paper is to examine the impact of applying two classical controller strategies, including two proportional (P) controllers with two feedback loops and one proportional–integral–derivative (PID) controller with one feedback loop, on the order and inventory performance within a production-inventory control system.Design/methodology/approachThe simulation experiments of the dynamics behaviour of the production-inventory control system are conducted using a model based on control theory techniques. The Laplace transformation of an Order–Up–To (OUT) model is obtained using a state-space approach, and then the state-space representation is used to design and simulate a controlled model. The simulations of each model with two control configurations are tested by subjecting the system to a random retail sales pattern. The performance of inventory level is quantified by using the Integral of Absolute Error (IAE), whereas the bullwhip effect is measured by using the Variance ratio (Var).FindingsThe simulation results show that one PID controller with one feedback loop outperforms two P controllers with two feedback loops at reducing the bullwhip effect and regulating the inventory level.Originality/valueThe production-inventory control system is broken down into three components, namely: the forecasting mechanism, controller strategy and production-inventory process. A state-space approach is adopted to design and simulate the different controller strategy.

Controlling System for Stock Raw Material for Production Planning and Inventory Control in a Pharmacy Company

Master production schedule and material requirement planning are two important tasks for production planning and inventory control to monitor the stock of raw materials and finished goods. However, data used in master production schedule and material requirement planning come from various sources which have various types of data without good integration. This makes the forecasting of production planning and inventory control to provide raw materials for production not adequate. One of the applications for production planning and inventory control is EXACT. However, the application is operational-based. Thus, it cannot show the analysis of data. This paper proposes a data warehouse system that can provide a report for the production planning and inventory control system. By using a data warehouse, the necessary reports can be served quickly and flexibly in accordance with the needs of planning and inventory control system. Kimball Nine step is a method employed for designing a data warehouse, especially if each part is designed gradually. The development of data warehouse can give the report faster and more complete by accessing the dashboard. It also simplifies the process which occurs in the data retrieval of planning and inventory control system forecast.

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

PurposeThe purpose of this study is to propose a new dynamic model of a production-inventory control system. The objective of the new model is to maximise the flexibility of the system so that it can be used by decision makers to design inventory systems that adopt various strategies that provide a balance between reducing the bullwhip effect and improving the responsiveness of inventory performance.Design/methodology/approachThe proposed production-inventory control system is modelled and analysed via control theory and simulations. The production-inventory feedback control system is modelled through continuous time differential equations. The simulation experiments design is conducted by using the state-space model of the system. The Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS) model is used as a benchmark production-inventory control system.FindingsThe results showed that the Two Automatic Pipelines, Inventory and Order-Based Production Control System (2APIOBPCS) model outperforms APIOBPCS in terms of reducing the bullwhip effect. However, the 2APIOBPCS model has a negative impact on Customer Service Level. Therefore, with careful parameter setting, it is possible to design control decisions to be suitably responsive while generating smooth order patterns and obtain the best trade-off of the two objectives.Research limitations/implicationsThis research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level.Originality/valueThis present model is an extension and improvement to Towill’s (1982) and John et al.’s (1994) work, since it presents a new dynamic model of a production-inventory control system which utilises an additional flow of information to improve the efficiency of order rate decisions.

Analysing the solution of production-inventory optimal control systems by neural networks

In this paper, a general production-inventory optimal control system is proposed which can be used in most cases that might arise in the theory of production-inventory control. The proposed general form is considered and approximately solved using neural networks. Since the obtained solutions are achieved based on neural networks, they have several advantages in practice. One of the important advantages is that the solutions can be easily used for post optimality and sensitivity analyses. The solutions of this model are compared with those of other existing methods and some illustrating notes are presented.

A supply chain model with vendor-managed inventory, consignment, and quality inspection errors

This paper considers the integration between quality control and production inventory control in supply chain management. Specifically, we study the effect of inspection errors on the costs incurred in a supply chain system with a single vendor and multiple buyers. In this system, the vendor enters into a vendor-managed inventory (VMI) and a consignment stock (CS) partnership with several buyers. We assume that the items made by the vendor are not in perfect quality, but they contain a given proportion of defective units. We also assume that quality inspection of these items by the buyers is subject to sampling errors. Three cases indicating to different levels of supply integration are considered: VMI–CS system, traditional system and integrated system. For each case, a mathematical model is formulated, an optimum solution is developed, and a numerical example is solved.

Forecast-corrected production-inventory control policy in unreliable manufacturing systems

In traditional research on production-inventory control problems with failure-prone manufacturing systems, a stationary demand process is an essential assumption. However, such a situation may not be true. This study extends the hedging-point-based production-inventory control problem into the case with non-stationary demand. The demand forecasting process is simulated and categorised into two different cases. First of all, a two-level control policy is proposed to solve the problem with a Markov modulated Poisson demand process which is often used in qualitative forecasting. Then the quantitative forecasting process using time series methods is modelled and a forecast-corrected control policy is proposed accordingly. The impact of forecasting on the system performance is then investigated. An integrated simulation and experimental design method was adopted to solve the modified optimal control problem. The results show that the proposed control policy can outperform the traditional stationary policy when the forecasting error is limited to a certain level. [Received 29 August 2014; Revised 13 July 2015; Revised 12 April 2016; Revised 20 September 2016; Revised 21 September 2016; Accepted 30 March 2017]

Joint production planning, pricing and inventory control for deteriorating items

In this paper, an inventory model with time and price dependent demand is developed in two echelon supply chain consists of producer and retailer. Deterioration rate considering in this model is assumed to follow Weibull distribution with known parameters. In addition, shortage is allowed in retailer and partially backlogged. The purpose of this research is to maximise the profits for firms which one side retailer finds the selling price and the economic order quantity simultaneously, and another side producer optimises production time and the number of delivery. The optimal decisions are determined by a proposed solution algorithm, and a numerical example is solved to endorse it. Eventually, sensitivity analysis on the optimal solution is carried out, and also some practical examples are presented.

Opportunity cost estimation using temporal association rule mining

Modern production planning and inventory control has been developed. However, efficient method for evaluating the lost sales in different time-periods has not been proposed. In this paper, a mathematical model to estimate the opportunity cost based on association rules is proposed considering cross-selling effect in various time-spans. A numerical example is used to explain the new approach. The results show that the proposed approach can gain a well insight into the cross-selling effect among items and is applicable for large sized transaction database.

Joint production planning, pricing and inventory control for deteriorating items

Joint production planning, pricing and inventory control for deteriorating items

Two-critical-number control policy for a stochastic production inventory system with partial backlogging

This paper deals with a production–inventory control model with partial backlogging, in which a reflected Brownian motion governs the inventory level variation. We consider a single storage facility with infinite capacity and assume that shortages are allowed and the total amount of stock-out is a mixture of backordering and lost sales. In addition, the production facility is controlled by a two-parameter (m, M) policy, which switches the production rate when the inventory level reaches the threshold values. The aim is to determine the optimal control parameters m and M by minimising the long-run total expected cost of the system. Some results are illustrated using numerical examples. A sensitivity analysis of the optimal solution with respect to major parameters is also carried out.

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well.

Performance regulation of event-driven dynamical systems using infinitesimal perturbation analysis

This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

Penerapan Material Requirement Planning (MRP) dengan Mempertimbangkan Lot Sizing dalam Pengendalian Bahan Baku pada PT. Phapros, Tbk.

PT. Phapros Tbk is one of the oldest and biggest pharmaceutical company in Indonesia. As raw material inventory plays a key role to compete in this type of industry, it is important for PT. Phapros, Tbk to keep it well maintained. However, the Production Planning and Inventory Control Division, whose responsibility is planning and controlling the raw material has not implemented the Material Requirement Planning (MRP) method that causes a problem regarding the overstock of captopril, the ingredient for producing captopril 25 mg tablet. There are too much raw materials ordered and ends up with higher expense. The author proposed a lotting method to develop an efficient raw material planning. Results shows that using Wagner Within Algorithm method can help the company minimizes the total expense spent by Rp 984.977.

Production-inventory model for two-echelon closed-loop supply chain with finite manufacturing and remanufacturing rates

ABSTRACTThis research develops a two-echelon closed-loop supply chain production-inventory model consisting of a manufacturer, a remanufacturer and a retailer. The retailer faces a constant demand from the customers, which is satisfied by new and remanufactured product received from the manufacturer and the remanufacturer, respectively. The manufacturer and remanufacturer produce the product in batches with finite production rate. The manufacturer procures raw material in batches from outside supplier, and the remanufacturer receives the used product from the customers at a constant return rate. Many studies in the area of forward supply chain have established that the centralised production-inventory control models provide better results than the decentralised models. Hence, with synchronised activities of the supply chain players, this research proposes to find an optimal production-inventory policy of the players by minimising the joint total cost of the two-echelon closed-loop supply chain. Numerical illustration is presented to demonstrate the solution procedure and a sensitivity analysis is carried out to examine the impact of key parameters of the problem. Also, the total cost of closed-loop supply chain model is compared with the total cost of the corresponding forward supply chain model.

A reinforcement learning methodology for a human resource planning problem considering knowledge-based promotion

This paper addresses a combined problem of human resource planning (HRP) and production-inventory control for a high-tech industry, wherein the human resource plays a critical role. The main characteristics of this resource are the levels of “knowledge” and the learning process. The learning occurs during the production process in which a worker can promote to the upper knowledge level. Workers in upper levels have more productivity in the production. The objective is to maximize the expected profit by deciding on the optimal numbers of workers in various knowledge levels to fulfill both production and training requirement. As taking an action affects next periods’ decisions, the main problem is to find the optimal hiring policy of non-skilled workers in long-time horizon. Thus, we develop a reinforcement learning (RL) model to obtain the optimal decision for hiring workers under the demand uncertainty. The proposed interval-based policy of our RL model, in which for each state there are multiple choices, makes it more flexible. We also embed some managerial issues such as layoff and overtime-working hours into the model. To evaluate the proposed methodology, stochastic dynamic programming (SDP) and a conservative method implemented in a real case study are used. We study all these methods in terms of four criteria: average obtained profit, average obtained cost, the number of new-hired workers, and the standard deviation of hiring policies. The numerical results confirm that our developed method end up with satisfactory results compared to two other approaches.

Production planning and inventory control for a two-product recovery system

The significance of product recovery through remanufacturing has been widely recognized and has compelled manufacturers to incorporate product recovery activities into normal manufacturing processes. Consequently, increasing attention has been paid to production and inventory management of the product recovery system where demand is satisfied through either manufacturing brand-new products or remanufacturing returned products into new ones. In this work, we investigate a recovery system with two product types and two return flows. A periodic-review inventory problem is addressed in the two-product recovery system and an approximate dynamic programming approach is proposed to obtain production and recovery decisions. A single-period problem is first solved and the optimal solution is characterized by a multilevel threshold policy. For the multi-period problem, we show that the threshold levels of each period are solely dependent on the gradients of the cost-to-go function at points of interest after approximation. The gradients are estimated by an infinitesimal perturbation analysis–based method and a backward induction approach is then applied to derive the threshold levels of each period. Numerical experiments are conducted under different scenarios and the threshold policy is shown to outperform two other heuristic policies.