Production Planning System Research Articles

Advanced Production Plan System of Military Manufacturing Enterprises Based on Linear Programming Model

The problem encountered by military manufacturing enterprises in the preparation of production plans is mainly attributable to the failure to optimize supply and demand resources in the supply chain. The article innovatively employs the advanced planning system based on linear programming model to solve the problem. The problem is first of all described mathematically; then its linear programming model is constructed, and the latter’s APS system designed; at last, the model is solved and checked. Upon the verification, the model is proven helpful for enterprises to work out production plans quickly and efficiently, and carry out what-if analysis. While winning the time advantage for military manufacturing enterprises in the market competition, it also enhances their operating management efficiency.

The fuzzy goal programming approach to production planning of intermediate gear spare parts: a case study

Production planning has an important role in the company's business processes. A company engaged in the manufacture of intermediate gear parts has a problem in optimizing its production system. The production planning system that occurs is still based on predictions from decision-makers. This study aims to optimize the production planning system to maximize the 15T intermediate gear spare parts' production capacity and the 30T intermediate gear spare parts. Optimization of production planning uses the fuzzy goal programming method to optimize objectives based on existing constraints such as working hours, profit tolerance values, and demand tolerance values. The results showed that the use of fuzzy goal programming was able to increase the production level by 2.765, with an increase in profit of 2.57%. Fuzzy goal programming implementation provides an optimal solution in increasing profits in accordance with company goals based on the constraints that occur.

Development of Distributed MRP System for Production Planning and Operation in Korean OEM/ODM Cosmetics Manufacturing Company

Up to date cosmetic OEM/ODM (original equipment manufacturing/original development manufacturing) industry receives attention as a future growth engine due to steady growth. However, because of limited research and development capability, many companies have employed commercial management platforms specialized for large-sized companies; thus, overall system effectiveness and efficiency is low. Especially, MRP (material requirement planning) system introduced originally in 1970s is employed to calculate the requirement of the parts. However, dynamic nature of production lead time usually results in incorrect requirements. In addition, its algorithm does not consider the capability of the production resources. Also, because the commercial MRP system calculates all subcomponent for fixed period, the more goods have subcomponent, the slower calculation is. Therefore, conventional MRP system cannot respond complicated situation in time. In this study, we will suggest a new method that can respond to complicated situations resulting from short lead time and urgent production order in Korean cosmetic market. In particular, a distributed MRP system is proposed, that consists of multi-functional and operational modules, based on the characteristic of the BOM (bill of material). The distributed MRP system divides components (i.e. products and parts) into several fields and decrease the problem size; thus, we can respond to dynamically changed data any time. Through this solution, we can order components quickly, adjust schedules and planned quantity, and manage stocks reasonably. In addition, a prototype of the distributed MRP system is presented in this paper, in which ERP (enterprise resource planning) sever data is associated with an excel spreadsheet via MSsql. System user interface is implemented by a VBA (visual basic for applications) tool. According to a case study, response rate for delivery and planning achievement rate were enhanced about 20%, and inventory turnover was also decreased. Consequently, the proposed system improves overall profit.

Production planning and control in multi-stage assembly systems: an assessment of Kanban, MRP, OPT (DBR) and DDMRP by simulation

Multi-stage assembly systems where the demand for components depends on the market-driven demand for end products, are commonly encountered in practice. Production Planning and Control (PPC) systems for this production context include Kanban, Materials Requirement Planning (MRP), Optimised Production Technology (OPT), and Demand Driven MRP (DDMRP). All four of these PPC systems are widely applied in practice and literature abounds on each of these systems. Yet, studies comparing these systems are scarce and remain largely inconclusive. In response, this study uses simulation to assess the performance of all four PPC systems under different levels of bottleneck severity and due date tightness. Results show that MRP performs the worst, which can be explained by the enforcement of production start dates. Meanwhile, Kanban and DDMRP perform the best if there is no bottleneck. If there is a bottleneck then DDMRP and OPT perform the best, with DDMRP realising lower inventory levels. If there is a severe bottleneck, then the performance results for DDMRP and OPT converge. This identification of contingency factors not only resolves some of the inconsistencies in the literature but also has important implications for the applicability of these four PPC systems in practice.

Directions of Production Planning & Production Control System: Mathematical Evolution from the Flexibility Point of View

Production Planning and Production Control systems are some of the keys that determine the development of modern production systems. The article presents the importance of a flexibility factor in the process of manufacturing, planning, and control systems development during the 20th century. Finally, actual problems and trends in the production system design and control over the design were perceived in addition to possible feasible directions of the development of manufacturing systems, probably in the 21st century were specified. Production planning and production control are usually painstaking to be one of the most noteworthy issues in the planning and operation of a manufacturing structure. A better planning system has a significant bang on cost reduction, increased productivity, customer satisfaction, and overall spirited improvement for a product. Also, the current customer demand for prominent diversity products has put into an increase in product complications that further lay emphasis on necessitates for superior planning. Proficient planning leads to the amplification in capability exploitation competence and, therefore, thereby reducing the operational time required to intact jobs and accordingly escalating the profitability of an organization in the current spirited environment. There are different systems of manufacturing, planning, and control for a job-shop together with flow-shop in which the jobs are to be a progression through a series of machines for an optimizing number of required performance measures

Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications

Many companies are struggling to manage their production systems due to increasing market uncertainty. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing have been touted as having the potential to transform production management, the realities of their adoption and use have been much more challenging than anticipated. In this paper, we explore these challenges and present a conceptual model, a use-case matrix and a product–process framework for a smart production planning and control (smart PPC) system and illustrate the use of these artefacts through four case companies. The presented model adopts an incremental approach that companies with limited resources could employ in improving their PPC process in the context of industry 4.0 and sustainability. The results reveal that while make-to-order companies are more likely to derive greater benefits from a smart product strategy, make-to-stock companies are more likely to derive the most benefit from pursuing a smart process strategy, and consequently a smart PPC solution.

Determination of Joint Lot Size using the supply chain approach with Vendor Managed Inventory (VMI) Method at PT. XYZ

PT. XYZ is a company producing animal feed products which is located in the Medan Region. This company is a branch of Z Group company based in Surabaya. In the process company business activities, both from the process of ordering raw materials to suppliers up to the sale of products to buyers, often happens planning mismatches in determining production lots so often cause overstock and stockout. This is based on the lack of integration each component in the supply chain. Each component is still using the traditional way that is by making each plan without coordinating with each other. This manufacturing company still needs to do improvements in the production planning system. The absence of the PPIC Department make planning in the production in the company becomes out of control. This study aims to improve the supply chain system by method VMI and determine the optimum combined lot size by the JELS method. The JELS calculation results show an optimal Q of 8,692 bags, with QM for 437,082 bags with a frequency of product shipments 50 times and the frequency of replenishment of raw materials 13 times.

Development and Integration of RFID Simulation Based Tool for Improved Schedule Performance in Saudi Arabia Precast Concrete Industry

A construction project is measured as a process that encompasses numerous activities and a huge amount of information of numerous types that are related to each other. Effective project management entails controlling all aspects of a construction project: quality and quantity of work, costs, and schedules to guarantee the accomplishment of the project. The construction industry in the kingdom of Saudi Arabia is implementing off-site construction with precast cement, for its higher quality, short cycle time, and negligible environmental influence. Amongst the various off-site construction approaches, such construction follows a manufacturing method where houses are built in smaller panels in an assembly line. Owing to the complex and exclusive nature of the home building process, prevailing manufacturing concepts such as automated production planning and control system are not applicable to the panelized home production facility. This research aims in the development and integration of radio frequency identification (RFID) technology to for production planning and control system, resulting in improved schedule performance, in Saudi Arabia precast concrete industry. Radio frequency identification is appropriate to numerous construction applications and generates cost savings via increased speed and accuracy of data entry. The study provides a set of helpful protocols for the deployment of passive RFIDs for automated on factory management of precast concrete operations. Further it aims at paving the way for managing the site for the sake of attaining the desired goals considering the guarantee of achieving high quality and quick conveyance to the related sites which, hence, prompts auspicious conveyance of development ventures. The study is beneficial for the construction professionals to assess the prevailing construction quality perception and the expectations from implementation of any quality improvement technique in construction projects.

Development of a BIM-based production planning and control system for Lean Construction through advancement and integration of existing management techniques

As part of general construction management, production planning and control is vital for successful project delivery. Numerous approaches supporting production planning and control exist in practice and research. However, the different approaches focus on distinct areas such as workflow stabilization or cost control, and no single system combines all the requirements of a holistic production management system. Varying production management systems can be explained by the unique characteristics of many construction projects. As an approach for the digital twinning in the construction industry, building information modeling (BIM) can help standardize production management through shifting the management system design toward the digital prototype. Previous scientific work has acknowledged this approach, thereby generating numerous concepts for using building information models within construction management approaches. However, BIM is often merely used as a parallel support rather than as an integral part of production management systems. To address this gap and in terms of research methodology, we follow a Design Science Research approach. Thus, we propose a new BIM-based production management system, which is characterized by a theoretical integration model for BIM and existing construction management techniques, and a methodology for applying these concepts in practice.

A two-stage stochastic programming approach for production planning system with seasonal demand

A two-stage stochastic programming approach for production planning system with seasonal demand

Production Planning and Scheduling in Knitted Garment Mill (Dept.T)

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Optimization of garment sizing and cutting order planning in the context of mass customization

In this paper, we present a mass customization (MC)-oriented garment production planning system using mathematical optimization methods to generate the most efficient size chart and cutting order plan. It is composed of two subsystems, i.e., the fit-oriented garment sizing system and the cost-oriented garment cutting-order-planning (COP) system. In the fit-oriented sizing system, additional sizes are generated based on classical standard sizes, where a genetic algorithm (GA) is used to find the global optimum within an acceptable computation time. The comprehensive fit (CF), an overall garment fit evaluation of the target population, is taken as the objective function of the GA. In the cost-oriented COP system, under the hypothesis that fabric-cutting markers vary greatly (regarding the marker length and the cutting length) with various size combinations, an expanded integer programming (IP) model is developed to generate a cutting order plan with the lowest overall cutting cost (including the costs of fabric, spreading operation, and cutting operation) for the proposed sizing system. This MC-oriented production planning system has been validated with the performance of personalization (fit) and economy (cutting cost) through a case study on a women’s basic straight skirt. The experimental results show that the proposed system enables a considerable improvement of custom-fit at the expense of a very limited amount of extra cutting cost. Nevertheless, the cutting cost can fluctuate with the increasing number of extra sizes rather than increase monotonically with it. This study illustrates that these optimization approaches which support the garment sizing and COP for MC can help to gain a high customer satisfaction in terms of the garment fit and the cutting cost. More precisely, a GA is capable of rapidly finding the globally optimal sizing scenario, and an IP is able to work out the corresponding best cutting order plan. Furthermore, these proposed approaches can ultimately facilitate the evolution of garment production from mass production (MP) to MC.

PERENCANAAN DAN PENGENDALIAN PRODUKSI AGREGAT PADA USAHA TEDUNG UD DWI PUTRI DI KLUNGKUNG

Production planning and control is very important to minimize the occurrence of excess product inventory so that the demand season is low and vice versa there is a shortage of products during the high demand season. The purpose of this study is to determine and determine the amount of production with minimal costs at UD Dwi Putri Klungkung. This research was conducted on building products using an aggregate production planning system while the data used in this study were sales data for one year, production capacity, overtime costs, storage costs and labor costs. Based on the results of the study indicate that the estimated number of products produced during the next year is as many as 31,531 units, which are based on the exponential smoothing method. This method is used, because the exponential smoothing method gives results that have the smallest standard error with MAD value of 208.812, MSE value of 61843.5 and MAPE value of 7.97%. Aggregate planning strategy with chase strategy produces a total production cost of Rp. 242,995,500.00, where the total cost lower, compared to the total production cost with a strategy level of Rp. 254,337,081.00. 
 Keywords: Forecasting, Aggregate Planning, Chase Strategy, Strategy Level

Connected production planning and control systems – implementation and the optimization process for subcontracting

Production planning and control (PPC) systems are used to organize operating materials. In order to address connectivity demands of interacting PPC systems a new software architecture has been presented in prior work. This software architecture provides a basis for data exchange in-between such systems. It is up to this paper to describe an implementation approach realizing the theory. Furthermore, a new optimization process which targets subcontracting, dynamics and communication of interconnected PPC systems is presented. As a result, the mandatory and optional content of an order enquiry as well as of a response message is discussed.

The Digital Twin as a Core Component for Industry 4.0 Smart Production Planning

Production systems that adhere the Industry 4.0 vision require new ways of control and integration of individual components, such as robots, transportation system shuttles or mobile platforms. This paper proposes a new production system control concept based on closing a feedback loop between a production planning system and a digital twin of the physical production system. The digital twin keeps up-to-date information about the current state of the physical production system and it is combined with the production planner utilizing artificial intelligence methods. Production recipes and concrete process instantiations are planned for each production order on-the-fly, based on the production system state retrieved form the digital twin. This approach provides a high flexibility in terms of ability to add and to remove products as well as production resources. It also enables error recovery by re-planning the production if some failure happens. The proposed approach is tested and evaluated on an internally hosted Industry 4.0 testbed, which confirms its efficiency and flexibility

Simulation-based evaluation of an energy oriented production planning system

Energy intensive companies are trying to adopt their production to volatility of the energy market in Germany because of the increasing amount of renewable energies. Therefore, established production planning processes must be changed to adapt their energy consumption according to the volatility of energy prices using energy flexibility potentials. The energy oriented production planning system supports the company by scheduling their production system cost efficient. The attainable benefit depends on the planned time period which is optimized and the uncertainties during the operation mode. Therefore this paper introduces a method to evaluate an energy oriented production planning system focusing on the optimization parameters and the optimized planning period.

Elaboration of a production planning system for a snack manufacturing company

A Production Planning System was made for a snacks’ production Company by the development of a MRP system in order to increase the present production and to meet the demand. The methodology used was Material Requirement Planning (MRP) that basically consists in a logic system able to manage and control material requirement planning based on a number of pieces, components and material needed for manufacturing, in other words, it is a production planning system. The outcome of the Project has been an application with MRP features, with complements such as line balance, purchasing Budget of raw materials, among other things. The application relies on protection of information and backup by the same. The executable application of MRP system has contributed to reduce 50% the production breach at the company, as well as reducing waste in a 40%, idle time in a 50% and raw material costs in a 25%, achieving the stated goal.

New Optimization Model for Multi-Period Multi-Product Production Planning System with Uncertainty

The focus of this study is to develop a multi-period multi-product (MPMP) production planning system with uncertainty, and products demand (seasonal demand) uncertainty. Mainly, the problem aims reach the production levels of each product according to the uncertain demand for various periods, which depend on constraints of capacity, inventory, and resources. An analytical model proposed for this problem that can be categorized into two classes: non-linear and stochastic. The objective is to minimize the summation of variable production costs. As uncertain demand is a dynamic stochastic data process in the planning horizon, it is considered as a tree model. Each stage in the demand tree model is related to a cluster of a period time. Hence, depending on the tree model for the fluctuation demand; Two-Stage Stochastic Programming (TSP) model is presented as an alternative for all demand scenarios. In some of the reviewed articles validation of the analytical model were missing, while other studies were missing either manufacturing set up costs or assumptions of seasonal demand. Therefore, this study proposes TSP model using Sampling Average Approximation method (SAA) that is suitable for a production planning system in any manufacturing environment considering seasonal demand using optimization program (Lingo 16) to solve the mathematical model. Further, investigation of seasonal demand is performed using the multiplicative seasonal method, and the model validation was checked using Mathworks Matlab R2015a (64-Bit) considering manufacturing set up costs. Finally, some recommendations for future research are suggested.

Dispatching problems for parallel machines in the TFT‐LCD assembly process

AbstractThin‐film transistor liquid‐crystal displays (TFT‐LCDs) have gradually replaced cathode‐ray tubes because of their compact size and efficient use of power. Since product lifecycles are shortening, consumers require instant order delivery, which affects fluctuations in supply and demand. As various product types in the LCD industry exist, manufacturing time has to be reduced to satisfy consumer demand. This study investigates the dispatching of parallel machines for TFT‐LCD manufacturing. Since customers’ orders and suppliers’ deliveries vary significantly and the manual adjustment of production planning is time consuming, an appropriate production planning and scheduling system is necessary. Consequently, this study develops a genetic algorithm to solve the scheduling problem of unrelated parallel machines, taking account of multiple performance measurement indicators. We also adopt LINGO to solve the problem and compare the obtained solution quality and computation time of the two approaches to justify the effectiveness of the proposed genetic algorithm.

Perancangan alat bantu untuk memperbaiki proses perencanaan produksi pada PT X

Production planning is part of the operational management that is important in a company. By managing production planning properly, the production process in a company will be able to run better. PT X is a company that produces vehicle spareparts for private cars. The company runs make to order system to fulfill the production procss. In the make to order system, a new product will begin to be produced by the company if there is an order from the customer. In this system, a company has inventory i.e. raw material and work in process. The problem are PT X have lack of raw materials and the absence of lead time given by the company to customers about when a product will be sent. To overcome this problem, improvements are made to the existing production planning system. Improvements begin by changing the way raw materials are purchased. This is done by treating the system with order point. With this system, company will never experience a shortage of raw materials because when raw material below order point, company must immediately order new raw materials. Thus the production process will not be disturbed and can run smoothly. In determining when a product will be completed and sent to customers, a production planning system with DSS tools (decision support system) is made using Microsoft Excel tools. By using DSS, the system will automatically display when a product will be finished and when it can be sent to the customer, when to buy raw materials, and how much production order is. Accordingly, the company can provide certainty of information to customers when the products have been produced and shipped.