Make-to-order Research Articles

Um modelo de análise do planejamento e controle da produção para pequenas e médias empresas

Objetivo: Desenvolver um modelo de análise para o Planejamento e Controle da Produção (PCP) em Pequenas e Médias Empresas (PMEs) que operam com a estratégia de Produção Contra Pedido e verificar o impacto da formalização dos processos e da mensuração de resultados no PCP das PMEs. Método: Este trabalho utilizou a pesquisa-ação para validar um modelo de análise do PCP em 3 PMEs. Foram criadas equipes de melhoria em cada empresa e aplicadas técnicas como a visita técnica, observação participante e entrevistas focadas para mapeamento e análise dos processos de PCP. Fundamentação teórica: Ao analisar a literatura, foi possível mapear três principais características das operações das PME's: (1) Planejamento e Controle da Produção deficientes, (2) informalidade na execução das operações e (3) falta de mensuração de resultados. O modelo de análise proposto neste trabalho parte de um entendimento de que as caraterísticas (2) e (3) explicam (1) em grande medida. Resultados: A formalização dos processos ajudou a disseminar o conhecimento das tarefas e prioridades entre os funcionários das empresas e diminuiu consideravelmente a improvisação nos processos. A mensuração de resultados facilitou a identificação de problemas e oportunidades de melhoria nos processos de PCP. Contribuições: São poucos os modelos de análise do PCP disponíveis na literatura que atendem necessidades específicas das PMEs. Este trabalho preenche essa lacuna com o desenvolvimento e a validação de um modelo de análise destinado a esse segmento.

Assessing the impact of performance determinants in complex MTO/ETO supply chains through an extended hybrid modelling approach

In make-to-order (MTO)/engineer-to-order (ETO) business environments multiple customer-oriented projects compete for and share resources through interdependent engineering and production activities. Deep knowledge of critical dimensions that affect performance is key in this context. For this, we propose a set of determinants – workload, complexity, outsourcing, design reuse, project type, and knowledge/experience with technology, that impact performance. These determinants are input to an extended hybrid simulation model using system dynamics (SD), discrete event simulation (DES) and agent-based simulation (ABS) that tackles the needs imposed by activities of very different nature, as the project development and manufacturing/assembly operations. The hybrid model is applied to the case of an advanced manufacturing company. Through Monte Carlo sampling, the influence of different combinations of determinants in the performance variability is assessed. A correlation analysis shows evidence of association between all performance determinants and the project time and cost, while no evidence of association between the design reuse and project type determinants and the manufacturing and assembly time.

The mass consumption of refashioned clothes: Re-dyed kimono in post war Japan

Among the strategies of post-consumer textile waste management, refashioning or the makeover of used clothes, is gaining attention as value added recycling. However, refashioning business is considered as being possible only on a small scale. This article presents a case of its mass scale operation and clarifies the factors that enabled it. From the 1920s to the 1960s, re-dyeing played an indispensable role in Kyoto maintaining its central position in dyed kimono production. This study clarifies how the coordinators of re-dyeing and makeover, the shikkai, established a MTO (make to order) network, forming direct and recurrent ties with customers nationwide.

Role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm

Flexible manufacturing system (FMS) readily addresses the dynamic needs of the customers in terms of variety and quality. At present, there is a need to produce a wide range of quality products in limited time span. On-time delivery of customers’ orders is critical in make-to-order (MTO) manufacturing systems. The completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. Among those, batch size is a significant construct for effective scheduling of an FMS, as it directly affects completion time. On the other hand, constant batch size makes MTO less responsive to customers’ demands. In this paper, an FMS scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. The effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. A mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. Scheduling of an FMS is not only a combinatorial optimization problem but also NP-hard problem. Suitable solutions of such problems through exact methods are difficult. Hence, a meta-heuristic Genetic algorithm is used to optimize scheduling of the FMS.

A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company

Abstract Order promising in manufacturing systems that produce non-uniform units of the same finished good becomes a more complex process when customer orders need to be served with homogeneous units. To facilitate this task, we propose a mathematical model-based decision tool to support the order promising process according to product homogeneity requirements in hybrid Make-To-Stock (MTS) and Make-To-Order (MTO) contexts. In these manufacturing environments, the comparison of Available-To-Promise (ATP) and/or Capable-To-Promise (CTP) quantities with homogeneous ones ordered by customers is necessary during the order commitment. To properly deal with customers’ product uniformity requirements, different ATP consumption rules are implemented by defining a novel objective function. CTP modelling in these systems also entails having to address new aspects, such as estimating future homogeneous quantities in additional lots to the master plan, accomplishing minimum lot sizes and saving in setups when programming new lots. By including CTP in the order promising model, a closer integration with the master production schedule is achieved. The resulting mathematical model was applied to a ceramic tile company in different supply scenarios and execution modes, and at several availability levels (ATP and ATP&CTP). The results validate model performance and provide insights into the impact of ATP consumption rules on the profits made from committed customer orders in different scenarios for the specific ceramic tile company.

THE IMPACT OF CELL STRUCTURE ON THE MTO PRODUCTION SYSTEM

Today the customer market is becoming more demanding than ever. In many cases application of the 7R rule alone is not sufficient. The customer wants to have an influence on the final shape, form, functionality and features of the product. This forces entrepreneurs to abandon the make-to-stock (MTS) approach and adopt the make-to-order (MTO) concept, not only to satisfy the customer’s requirements better but also, for example, to reduce costs through manufacturing exactly the quantity of products that the market demands. The article describes an example of such an enterprise. This company is a manufacturer of windows, doors, roller shutters and other products. The custom products department manufactures products where the final look and functionality may be determined by the customer, who can take part in the process by selecting, for example, the shape, dimensions and accessories used. This is entirely make-to-order production. This system offers the benefits mentioned above, but on the other hand production planning is more difficult and the system requires a relatively high degree of production flexibility. The article describes how the application of simple methods of analysis may help to introduce a certain degree of standardisation of processes/operations/activities and to identify and eliminate certain types of waste from the production process.

Emission reduction decision and coordination of a make-to-order supply chain with two products under cap-and-trade regulation

This paper incorporates carbon emission reduction into a make-to-order (MTO) supply chain with one upstream supplier that provides two types of raw materials and one downstream manufacturer that produces and sells two products. Emissions from the supply chain are the result of production activities, and the manufacturer invests in emission reduction technology to curb carbon emissions under cap-and-trade regulation. We first analyze the optimal operational decisions of the supplier and the manufacturer in the decentralized scenario and then test the supply chain’s performance by comparing its profit and carbon emissions with those in the centralized scenario. The results show that the supply chain’s profit in the decentralized scenario can be improved and that carbon emissions can be reduced. We propose a revenue- and investment-sharing contract to coordinate the supply chain and derive several conditions under which the contract is accepted by both members of the supply chain. A numerical study is conducted, and the theoretical results and impacts of several key carbon parameters on supply chain coordination are presented to inform management decisions.

Production line performance analysis within a MTS/MTO manufacturing framework: a queueing theory approach

Abstract Paper aims Mixing the Make-To-Stock (MTS) and Make-To-Order (MTO) strategies to benefit from the both manufacturing systems in an environment with impatient customers. Originality This is the first research article which uses the queuing theory to find the best place of the Order Penetration Point (OPP) in a production line with impatient customers. Research method Two scenarios are studied in this paper. 1- Semi-finished products are produced and stored in a buffer. No semi-finished product will be completed until a specific order comes to the system. This strategy leads to idle cost, but there is savings obtained in terms of eliminating investment in finished goods inventory and its holding cost. We can calculate the total cost of the system and find the optimal machine for the buffer of semi-finished products. 2- We use both MTS and MTO for completing the semi-finished products. When there is no customer in the system, semi-finished products are completed based on the MTS strategy and finished products are sent to a warehouse. But, when an order comes in for customization, a semi-finished product get assigned to that order and after finishing the current MTS job on each machine, this MTO job starts to complete the semi-finished product. To calculate system performance indexes, we use the Matrix Geometric Method (MGM) after modeling the systems with queuing theory concepts. Main findings Numerical examples show the convexity of total cost in terms of product completion percentage and number of customization lines after the OPP. Also, increasing the production rate leads to higher expected number of semi-finished products in the buffer. Implications for theory and practice Positioning OPP in manufacturing systems to compare different production strategies (MTS/MTO) has not been widely studied yet. This paper shows how manufacturing companies can apply the OPP to obtain benefits from both MTS and MTO strategies according to various cost parameters of the production lines. Using queuing theory concepts to model the problem under study helps to consider the external factors such as impatient customers and demand arrival uncertainty that can affect the performance measures of the system besides the internal factors such as production rate and inventory related costs. The idea of expanding the production line after a specific station and have more customization lines to improve customer satisfaction is studied in this paper as well.

Analysing the impact of uncertainty reduction on WLC methods in MTO flow shops

ABSTRACTWith more and more demanding customers, make-to-order (MTO) companies are becoming more important. Workload control (WLC) is specifically developed for MTO companies. Most previous studies have proposed solutions assuming no mismatch between planned and actual production time. However, in real situations, inaccurate estimation of processing time is a daily routine. This study focuses on processing time estimation error and evaluates performance of two different WLC order review and release (ORR) methods through simulation of a pure flow shop. Simulation results demonstrate that balancing workload is more effective than limiting workload at different error levels. However, with decrease in errors, difference in performance between limiting and balancing methods becomes wider. Further investigation shows that improving processing time estimation heavily affects the order selection process in balancing method, which is the main reason for performance improvement. Finally, aspects of Industry 4.0 are discussed in context of ORR methods in MTO flow shops.

Towards a hybrid multi-dimensional simulation approach for performance assessment of MTO and ETO manufacturing environments

Despite the growing relevance of customization as a source of competitive advantage, the make-to-order (MTO)/engineer-to-order (ETO) manufacturing strategies have been neglected in the literature. Companies following these strategies deal with simultaneous customer-oriented projects that compete for and share resources, while coordinating interdependent engineering and production activities. It becomes relevant understanding the impact that different development projects and production variables have on the manufacturing system performance. For this, we propose a hybrid multi-dimensional simulation model, using System Dynamics (SD), Discrete Event Simulation (DES) and Agent-based simulation (ABS) for MTO/ETO performance assessment.

MODIFICATION OF THE CELLULAR STRUCTURE WITH INCREASED PRODUCTION IN THE MAKE-TO-ORDER SYSTEM

The customer market is becoming more and more demanding. Customers want to have influence on the final shape, form, functionality and characteristics of the product. As a result, entrepreneurs are forced to abandon the Make-to-Stock system in favour of the MTO (Make-to-Order) concept, with the purpose of not only meeting the customer’s needs but also, for example, reducing the costs by manufacturing exactly the number of products which is currently demanded on the market. The article describes an example of a production task performed in the cellular structure. The customer required a substantial and abrupt increase (by 65%) of the production task. In order to tackle the problem, logistics engineering and lean manufacturing methods were applied. Having regard to the Industry 4.0 perspective, a concept of automated work cells was presented in order to minimise the role of the operator.

Automated generation of CNC programs for manufacturing of individualized products

Companies are facing an increasing demand for individualized products. Thereby, the relevance of make-to-order (MTO) production systems rises. Product configuration systems and parametric design models have been established in MTO production environments to enable efficient manufacturing of individual variants towards the quantity of one piece. High potential for further improvements of lead time, cost and quality is offered by the automated generation of the individual CNC programs. This research focuses on CNC program generation based on parameters supplied by product configuration systems. Different approaches are described and characterized. A decision tree for automated CNC program generation is presented.

A simulation study of capacity utilization to predict future capacity for manufacturing system sustainability

Capacity utilization (CU) measurement is an important task in a manufacturing system, especially in make-to-order (MTO) type manufacturing system with product customization, in predicting capacity to meet future demand. A stochastic discrete-event simulation is developed using ARENA software to determine CU and capacity gap (CG) in short run production function. This study focused on machinery breakdown and product defective rate as random variables in the simulation. The study found that the manufacturing system run in 68.01% CU and 31.99% CG. It is revealed that machinery breakdown and product defective rate have a direct relationship with CU. By improving product defective rate into zero defect, manufacturing system can improve CU up to 73.56% and CG decrease to 26.44%. While improving machinery breakdown into zero breakdowns will improve CU up to 93.99% and the CG decrease to 6.01%. This study helps operation level to study CU using “what-if” analysis in order to meet future demand in more practical and easier method by using simulation approach. Further study is recommended by including other random variables that affect CU to make the simulation closer with the real-life situation for a better decision.

AN APPROACH TO IMPROVING CUSTOMER ON-TIME DELIVERY AGAINST THE ORIGINAL PROMISE DATE

One of the main challenges that a low-volume high-variety (LVHV) product manufacturing industry faces is to improve customer on-time delivery (OTD) against the original promised date (OPD) in a make-to-order (MTO) situation. A systematic root-cause analysis is carried out in the LVHV industry to find the true root cause and eliminate it. This paper proposes a methodology that incorporates the lean thinking methodology, a lean live tracking (LLT) tool, and cross-functional team (CFT) approaches to improve OTD. These are tested using real-time data from an industrial valve manufacturing LVHV firm, resulting in an average OTD improvement from 30 percent to 90 percent in about eight months.

A risk based approach to support the supplying of components in a MTO assembly process

Product customization is becoming more and more an option required by the customers and, when referring to complex items, pursuing this path could have a strong impact on the way companies have to manage the products and the associated processes. In this context, a Make To Order (MTO) and Engineer To Order (ETO) paradigms are viable approaches. Using these paradigms, the coordination between supplying and manufacturing has a prominent importance. A missing component during the production phase can cause significant delays and disruptions in the plans and, consequently, delays respect to the due dates negotiated with the customers. Due to the intrinsic uncertainty associated to the selection of customization options by the user, the supplying of the components and the production/assembly process, company managers addressing this coordination problem have to ground on risk measures supporting the selection of the right supplying option, aiming at minimizing the probability of missing components. In this paper, we present an approach to support this selection in the production and assembling of complex products grounding on the definition and calculation of two indicators, the Risk Index and the Criticality Index. The first one addressing the risk associated to the supplying of a component through different supplying alternatives, the second one providing an assessment of the criticality of the coordination between the supplying and assembling phases together with the specific risk aversion. An application to a real MTO industrial case is also provided addressing the production of machine tools.

Logistics approaches assessment to better coordinate a forest products supply chain

This paper presents two mathematical models to plan the key activities of a forest products supply chain encompassing different harvesting areas. These areas supply four sawmills with logs, which in turn send lumber to the construction market and chips to one paper mill. In particular, three logistics approaches are compared (i.e., Make-to-Order (MTO), Vendor Managed Inventory (VMI), and a centralized planning approach), and key performance factors used (e.g., demand satisfaction and wood fiber freshness), with the goal of optimizing harvesting, storage, transportation, and production operations while improving the competitiveness of this proposed forest products supply chain. Results demonstrate the importance of an effective balance between chips and lumber production to ensure adequate quality of finished products as well as higher profits for the entire supply chain. These results also confirm the ability of the VMI approach to reduce logistics costs for the overall forest products supply chain.

Priority optimization and make‐to‐stock/make‐to‐order decision in multiproduct manufacturing systems

AbstractWe consider a single‐stage multiproduct manufacturing facility producing a large number of end products. In order to reduce overall inventory costs, an efficient approach is to produce some items according to a make‐to‐stock (MTS) policy and others according to a make‐to‐order (MTO) policy. Items priority levels play a key role in the optimal MTO/MTS decisions for such typical large‐scale systems. To tackle this issue, the manufacturing facility is modeled as a multiproduct multipriority classes queuing system. We propose a general optimization procedure that selects near‐optimal priority classes, gives the associated flow control mode (MTO or MTS) for each product, and provides a lower bound and an upper bound with respect to the optimal cost. First, we illustrate efficiency of our optimization procedure for this class of nonlinear integer programs via several examples and by a numerical analysis, including a comparison with two alternative heuristics given in the literature. In addition, we provide managerial insights by exhibiting, under various parameter settings, the significant impact of an efficient priority level allocation among items on the inventory costs and on optimal splitting between MTO and MTS products.

Bees Algorithm for multi-mode, resource-constrained project scheduling in molding industry

In a resource-constrained environment project planning and scheduling becomes an extremely complex problem. For real life project schedules multi-mode resource requirements remarkably increase the complexity of and enlarge the respective solution spaces. Therefore schedulers require systematic methodologies compatible with the real world implementations in order to generate cost effective schedules. Similarly, plastic injection molding is known to be a “make-to order” process. The manufacturing of the mold which is a unique and essential component of plastic injection is considered kind of a project. The aim of this study is set to investigate the possibility of utilizing Bees Algorithm for single-resource, multi-mode, resource-constrained mold project scheduling in order to generate a systematic approach to solve the problems of this nature. A Bee-Based Mold Scheduling Model is therefore proposed and employed on a set of problems with different dimensions for the proof of concept. Detail description of an injection molding project together with respective performance analysis is also provided. After the implementation of the proposed methodology, it is well proven that, even for high number of activities and limited resources, the proposed method generates suitable schedules for the projects of this kind the implementation and respective modelling is explained and the results are discussed in detail within the text.

ERP System versus Stand‐Alone Enterprise Applications in the Mitigation of Operational Glitches

ABSTRACTBusiness function‐specific stand‐alone enterprise applications (SEAs) are displacing functionally integrated enterprise resource planning (ERP) systems, despite strong empirical support for the business benefits of the latter. This study explores the conditions under which it may be more effective to use a set of SEAs instead of a single‐suite ERP system, and vice versa. Based on Organizational Information Processing Theory, we expect differences in effectiveness to grow in prominence when the uncertainty of the operating environment increases, that is, when operational glitches in production processes become more frequent. Extending the existing literature, we postulate that high functional differentiation is a precondition for SEAs to be more effective than an ERP system, hypothesizing that the level of functional interdependence ultimately determines which type of software is superior for a given production process. We test our hypotheses using data collected from 163 make‐to‐order (MTO) production processes nested within 73 manufacturing plants and seven supply chains of complex, high‐tech machinery. Results show that when functional interdependence is low, the negative effect of operational glitches on delivery performance is effectively mitigated in MTO production processes wherein process‐related information is managed predominantly using SEAs; conversely, when functional interdependence is high, using an ERP system is more effective. Our findings offer practical guidelines as to when to use SEAs versus an ERP system while also integrating and updating the findings of earlier empirical research, in which each has been analyzed separately.

Sequencing of mixed models on U-shaped assembly lines by considering effective help policies in make-to-order environment

Mixed-Model Assembly Line (MMAL) is a type of production line where a variety of products' models similar to products' characteristics are assembled in the same line. Many manufacturers tend to use mixed-model assembly line in their production lines, since this policy makes it possible to assemble various products in the Make-To-Order (MTO) environment. In this research, the sequence of U-type mixed-model assembly lineis achieved through considering downstream help and storage of kits as e ective help policies for reducing total line stoppages and tardiness in delivery time of products to customers. Since this problem is NP-hard, hybrid GA-Beam search algorithm is developed to solve the problem. Numerical experiments are used to evaluate the performance and e ffectiveness of the proposed algorithm. To the best of our knowledge, this is the first study that considers getting help from downstream worker or using storehouse of kits, which has ready-toassemble parts in the conditions that workers cannot complete the remained task in the work horizon.