Customer Order Decoupling Point Research Articles

A fuzzy multiobjective team decision model for CODP and supplier selection in customized logistics service supply chain

To address information vagueness and uncertainty of the order demand, the logistics service provider (LSP) service capacity and the target preference caused by emergency events, a multiobjective team optimization model is developed for the customer order decoupling point (CODP) decision, supplier selection and order quantity allocation problems in customized logistics service supply chain. Three important techniques are described: (1) Fuzzy objective coefficients due to the uncertainty of the order and objective weights. (2) Fuzzy constraint parameters due to LSP service capacity and order demand variations. (3) A fuzzy single objective weighting technique with acceptance coefficient of constraint and fuzzy parameters that reflects decision-maker preferences. The acceptance coefficients of the constraints are integrated into the single objective model, which reflects the satisfaction level of the logistics service integrator (LSI) for feasible solutions. Then, we perform fuzzy decision and feasible solution design based on the center of gravity method defuzzification and maximum membership degree principle. To optimize decisions for decision-makers, the effects of some important fuzzy objective coefficients and fuzzy constraint parameters on the CODP decision and the acceptance coefficient of constraint was explored through sensitivity analysis. Numerical experiments show that the scale effect coefficient and objective function threshold have greater effects on the CODP decision.

Connecting organizational context to environmental sustainability initiatives and industrial symbiosis: Empirical results and case analysis

Industrial symbiosis can move us closer to a circular economy and enable efficient and sustainable use of resources. Its potential has however been far from realized, and to increase our understanding of why that is, we investigate drivers, barriers, and outcomes associated with both broadly defined environmental sustainability initiatives and industrial symbiosis from an organizational context perspective. A mixed-methods approach is used, combining statistical analysis of survey material with an embedded case study at an industrial symbiosis network in Sotenäs, Sweden. The position of the customer order decoupling point (CODP), a critical aspect of supply chains that separates forecast-based operations from those tied to specific orders, enables comparisons between organizations with primarily forecast-driven operations from those with primarily order-driven operations. We find that the CODP plays an important role in organizations' commitments to sustainability initiatives in general, as organizations with different CODP positions experience different levels of benefits from such initiatives. We did not find that the CODP position had the same impact for industrial symbiosis initiatives. Our results indicate that both industrial symbiosis, a very specific type of sustainability initiative and collaboration, and environmental sustainability initiatives in a broad sense, are associated with multiple, positive business outcomes. However, case study participants also described that their industrial symbiosis participation was time consuming and associated with an added administrative burden. This could be a reason why such collaborations are not more prevalent, despite the potential of bringing about several positive business outcomes. Finally, our findings indicate that industrial symbiosis may bring business-related benefits to firms regardless of their CODP position, but then in order to understand why such networks are not more prevalent, we recommend that future research investigates ways of quantifying and distributing burdens and rewards associated with industrial symbiosis collaboration.

Determination of Customer Order Decoupling Point (CODP) Based on Mass Customization Concept to Minimize Manufacturing Lead Time

Determination of Customer Order Decoupling Point (CODP) Based on Mass Customization Concept to Minimize Manufacturing Lead Time

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Supply chain antecedents of servitization: A study in ETO machinery companies

Nowadays, manufacturers are increasingly shifting towards Product Service Systems (PSS) by accommodating servitization. Whereas a fair body of research investigates the effect of servitization on the product supply chain, studies on the impact of the supply chain on the company's service offerings are scarce and reveal controversial results. In this paper, we aim to clarify the latter relationship by studying the impact of three supply chain features on servitization within three Engineer-To-Order (ETO) machinery Small- and Medium-sized Enterprises (SMEs). First, our results reveal that companies located downstream in the product supply chain exhibit higher servitization levels. Second, the level of customization, represented by the position of the Customer Order Decoupling Point (CODP) in the supply chain, and the company's servitization level seem to be unrelated. Whereas it is true that higher product customization, per se, enables companies to offer more services around the product, the operational challenges encountered because of higher customization levels can lead companies to deliberately reduce their menus of services to the customer. Third, higher levels of vertical integration support the provision of basic services because companies have more control on supplies required for services such as maintenance and repair. Companies with low levels of vertical integration cannot provide the speed and responsiveness required for basic services, but can still offer advances services, provided that they engage in strong collaboration with their suppliers. Thus, with this work, we highlight the extent to which product supply chains can support or inhibit the servitization endeavours of ETO companies.

A study on optimization of delayed production mode of iron and steel enterprises based on data mining.

Delayed production mode has been adopted by an increasing number of process production enterprises as a method to realize mass customization of multi-products. This paper used the convolutional neural network-long short-term memory artificial neural network algorithm (C-LSTM) in data mining technology to analyze and determine factors that have an impact on delayed production mode in the internal and external production and operation of enterprises. Combined with the actual production situation of iron and steel enterprises, a quantitative model of the delayed production was constructed. Lastly, data from a large iron and steel enterprise with good operation was used to verify the validity of the proposed model and analyze key influencing factors. According to the research, in scenarios of considering PDP alone, considering CODP alone, considering both PDP and CODP, considering PDP and CODP and using data mining technology to model, the matching degree of these methods with the actual situation of the enterprise is 31.8%, 61.4%, 71.6% and 86.6%, respectively. The numerical analysis results of the model based on data mining technology show that in delayed production, when customer service level improves or the delay penalty coefficient increases, the optimal locations of the product differentiation point (PDP) and customer order decoupling point (CODP) move toward the end of production, and the total cost increases gradually. When the difference in production cost or benefit of early delivery between the candidate locations of PDP and CODP is small, optimal locations of PDP and CODP are close to the beginning of the general and dedicated production processes. With an increase of cost difference or early delivery benefit, the optimal locations of PDP and CODP jumped to the end stage of the general and dedicated production processes, and the total cost begins to decrease.

Integrating Customer Order Decoupling Point with Operation Planning in Deteriorating Supply Chain: General Dynamic Model and Applications

Considering the real dilemma of deteriorating items, we explore how to find a balance between the conflicting achievements of enhancing flexibility and reducing costs. We propose a general dynamic model to penetrate customer orders by decoupling point with objective to minimize the cost. The closed forms of optimal solutions are obtained for companies to deal with the integrated problem of customer order decoupling point (CODP) decision and production–inventory plan simultaneously. Applications and numerical experiments are performed to illustrate practical insights for managers. The results show that the truth of zero-inventory policy is to avoid unnecessary inventory instead of absolutely no inventory. The CODP will shift forward with the increasing of customer demand rate and shift backward with the increasing of deterioration rate under the production smoothing policy. However, although it encounters the same changes of demand, the CODP moves with a smaller extent in high deterioration rate than in low. In addition, we find that during the growth phase of product life cycle, the increase of time-sensitive degree of demand is coupled with forward shifting of CODP; while during the decline phase, it is coupled with CODP backward shifting.

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization manufacturing.

In this paper, we present two metaheuristic evolutionary algorithms-based approaches to position the customer order decoupling point (CODP) in smart mass customization (SMC). SMC tries to autonomously mass customize and produce products per customer needs in Industry 4.0. SMC shown here is from the perspective of arriving at a CODP during manufacturing process flow designs meant for fast moving and complex product variants. Learning generally needs several repetitive cycles to break the complexity barrier. We make use of fruit fly and particle swarm optimization (PSO) evolutionary algorithms with the help of MATLAB programming to constantly search better fitting consecutive process modules in manufacturing chain. CODP is optimized by increasing modularity and reducing complexity through evolutionary concept. Learning-based PSO iterations are performed. The methods shown here are recommended for process flow design in a learning-oriented supply chain organization which can involve in-house and outsourced manufacturing steps. Finally, a complexity reduction model is presented which can aid in deploying this concept in design of supply chain and manufacturing flows.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00521-020-05657-1.

A Fuzzy Programming Model for Positioning Customer Order Decoupling Point Based on QFD in Logistics Service with Mass Customization

Mass customization logistics service mode provides a new way to maintain the sustainable cooperative relationship between customers and integrators. One of the key factors to maintain the sustainable development of logistics service supply chain under MC mode is to locate a suitable customer order decoupling point (CODP) location. This paper investigates the problem of CODP in the logistics service supply chain based on the fuzzy set theory under the mass customization mode. With the help of a fuzzy QFD method and a new service quality function that we constructed, this paper quantifies the quality of a logistics service when the LSI selects a different CODP. Then, the fuzzy set of the high-quality logistics service and the fuzzy set of the satisfactory delivery time are built. Based on those two new fuzzy sets, this paper builds a new fuzzy programming model on CODP positioning. The solving methods of this model under different conditions are given. Finally, the influence of some important parameters on the optimal CODP position is studied by sensitivity analysis on a specific numerical case.

A Framework for Designing the Customer-Order Decoupling Point to Facilitate Mass Customization

Abstract In the age of Industry 4.0, manufacturing enterprises are under pressure to improve mass customization to satisfy evolving demands in different markets. One challenge is to fulfill orders swiftly at an acceptable cost, while maintaining service quality. To do this, the customer-order decoupling point (CODP), where value-adding activities take place, should be designed and adapted to changing market demands. We propose a formulation-exploration framework to make decisions on customer-order decoupling point positioning and improve the supply chain to support mass customization. A test problem of auto parts manufacturing is used to illustrate the efficacy of our framework. The formulation-exploration framework can be used to design a supply chain to facilitate the mass customization of products, especially when information is incomplete and inaccurate (including uncertainties), and goals conflict with each other. In this paper, we focus on the method embodied in the framework rather than the results per se.

Positioning push–pull boundary in a hesitant fuzzy environment

AbstractNowadays, fierce competition enforces supply chain planners to develop market‐oriented production strategies. Customer order decoupling point (CODP) could increase the supply chain efficiency and responsiveness simultaneously. The right position of CODP in production industries will result in a pattern for trade‐off between responsiveness and operational efficiency. The purpose of this paper is to address the positioning problem of a push‐pull boundary in a fuzzy hesitant environment. To this end, a hybrid multi‐criteria decision‐making methodology of analytic network process (ANP) and VIKOR is proposed in a hesitant judgement environment to determine the position of CODP in a supply chain. Finally, CODP positioning in the apparel supply chain, as an industry‐based example, is analysed to show the applicability of the proposed method.

Optimal postponement in supply chain network design under uncertainty: an application for additive manufacturing

ABSTRACT This study presents a new two-stage stochastic programming decision model for assessing how to introduce some new manufacturing technology into any generic supply and distribution chain. It additionally determines the optimal degree of postponement, as represented by the so-called customer order decoupling point (CODP), while assuming uncertainty in demand for multiple products. To this end, we propose here the formulation of a generic supply chain through an oriented graph that represents all the deployable alternative technologies, which are defined through a set of operations that are characterised by lead times and cost parameters. Based on this graph, we develop a mixed integer two-stage stochastic programme that finds the optimal manufacturing technology for meeting each market’s demand, each operation’s optimal production quantity, and each selected technology’s optimal CODP. We also present and analyse a case study for introducing additive manufacturing technologies.

Delivery time dynamics in an assemble-to-order inventory and order based production control system

System dynamics play a critical role in influencing supply chain performance. However, the dynamic property of the assemble-to-order (ATO) system remain unexplored. Based on control theory, the inventory and order based production control system (IOBPCS) family, can be utilized as a base framework for assessing system dynamics. However, the underlying assumption in traditional IOBPCS-based analytical studies is that the system is linear and the delivery time to end customers is negligible or backlog is used as a surrogate indicator. Our aim is to incorporate customer delivery lead-time variance as the third assessment measure alongside capacity availability and inventory variance as part of the so-called ‘performance triangle’– capacity at the supplier, the customer order decoupling point (CODP) inventory and the delivery lead-time. Using the ‘performance triangle’ and adopting non-linear control engineering techniques, we assess the dynamic behaviour of an ATO system in the electronics sector. We benchmark the ATO system dynamics model against the IOBPCS family. We exploit frequency response analysis to ensure a robust system design by considering three measures of the ‘performance triangle’. The findings suggest delivery LT variance can be minimised by maintaining the ATO system as a true Push-Pull hybrid state with sufficient CODP stock, although increased operational cost driven by bullwhip and CODP variance need to be considered. However, if the hybrid ATO system 'switches' to the pure Push state, the mean and variance of delivery LT can be significantly increased.

Simulation Model Development for Determination of Components Production Quantity and Lead Time Reduction in Mass Customisation of Single Production Stage

Market rivalry forces manufacturing companies to be customer focus and have an agile system. In manufacturing companies that produce semi-common products based on customer order, the pressure from customers are usually faster delivery, reasonable price and higher product variability. This paper presents the use of Mass Customisation (MC) concept in order to cope with faster delivery and higher product variability. The MC concept is realised by determining Customer Order Decoupling Point (CODP). Besides MC, Theory of Constraint (TOC) concept is also applied in order to address problem that may occurred after MC implementation. Based on a simulation analysis, it shows that CODP implementation could improve delivery lead time and product variability performance dramatically up to 5.15 times compared to existing system. An optimisation algorithm is also proposed for further study in order to improve cost performance of the products.

Why do nonlinearities matter? The repercussions of linear assumptions on the dynamic behaviour of assemble-to-order systems

The hybrid assembly-to-order (ATO) supply chain, combining make-to-stock and make-to-order (MTS-MTO) production, separated by a customer order decoupling point (CODP), is well recognised in many sectors. Based on the well-established Inventory and Order Based Production Control Systems (the IOBPCS family), we develop a hybrid ATO system dynamics model and analytically study the impact of nonlinearities on its dynamic performance. Nonlinearities play an important, sometimes even a dominant, role in influencing the dynamic performance of supply chain systems. However, most IOBPCS based analytical studies assume supply chain systems are completely linear and thereby greatly limit the applicability of published results, making it difficult to fully explain and describe oscillations caused by internal factors. We address this gap by analytically exploring the non-negative order and capacity constraint nonlinearities present in an ATO system. By adopting nonlinear control engineering and simulation approaches, we reveal that, depending on the mean and amplitude of the demand, the non-negative order and capacity constraints in the ATO system may occur and their significant impact on system dynamics performance should be carefully considered. Failing to monitor non-negative order constraints may underestimate the mean level of inventory and overestimate the inventory recovery speed. Sub-assemblers may suffer increased inventory cost (i.e. the consequence of varying inventory levels and recovery speed) if capacity and non-negative order constraints are not considered at their production site. Future research should consider the optimal trade-off design between CODP inventory and capacity and the exploration of delivery lead-time dynamics.

Model Konseptual Customer Order Decoupling Point pada Agroindustri Udang

Shrimp agro-industry is one of the food processing industries, which converts raw shrimp into high-value shrimp processed products. The production system used in shrimp agroindustry to respond market demand is a hybrid make to stock (MTS) and make to order (MTO). The decision to determine which products will be stored as supply sources and which products are made to fulfill orders is a critical decision that must be made on the MTS-MTO hybrid system. The concept of customer order decoupling point (CODP) is used to identify material flow points that connect a product with different consumers. This study aims to present a conceptual model in the MTS-MTO hibrid system based on customer order decoupling point (CODP) which is adjusted to product characteristics, production characteristics and market characteristics in the shrimp agroindustry. The results of the study are presented in a business process model template that describes the order of decisions for storing products or making products to fulfill orders and conceptual models in the form of diagrams named the CODP conceptual model on shrimp agroindustry.

Determination of production planning policies for different products in process industries: using discrete event simulation

The increasing attention to customer demands in the product process, and the inevitable features and costs of production processes have led researchers and artisans to manage orders and choose the right policy for production planning. This article identifies the structure for determining the optimal location of the Customer Order Decoupling Point (CODP) and the optimal production planning policy as one of the most important strategic decisions in the production process. So, we developed a discrete-event simulation model for realistic calculation of cost and flow time, under different scenarios, and we used the production and sales information of a dairy production plant for validation and implementation of the model. The results suggest that the use of a hybrid production planning policy reduces the cost and delivery time.

Dynamic analysis and design of a semiconductor supply chain: a control engineering approach

The combined make-to-stock and make-to-order (MTS-MTO) supply chain is well-recognised in the semiconductor industry in order to find a competitive balance between agility, including customer responsiveness and minimum reasonable inventory, to achieve cost efficiency while maintaining customer service levels. Such a hybrid MTS-MTO supply chain may suffer from the bullwhip effect, but few researchers have attempted to understand the dynamic properties of such a hybrid system. We utilise a model of the Intel supply chain to analytically explore the underlying mechanisms of bullwhip generation and compare its dynamic performance to the well-known Inventory and Order-Based Production Control System (IOBPCS) archetype. Adopting a control engineering approach, we find that the feedforward forecasting compensation in the MTO element plays a major role in the degree of bullwhip and the Customer Order Decoupling Point (CODP) profoundly impacts both the bullwhip effect and the inventory variance in the MTS part. Thus, managers should carefully tune the CODP inventory correction and balance the benefit between CODP inventory and bullwhip costs in hybrid MTS-MTO supply chains.

Decision model for the customer order decoupling point considering order insertion scheduling with capacity and time constraints in logistics service supply chain

In the context of mass customization logistics service, we explore how a new inserted order affects the logistics service integrator (LSIs) decision regarding the location of the customer order decoupling point (CODP). Considering a two-echelon logistics service supply chain (LSSC), consisting of an LSI and several functional logistics service providers (FLSPs), we establish a CODP decision model based on time scheduling with capacity and time constraints. The optimization objectives of the model include minimizing the average unit operation cost for the LSI and maximizing the average satisfaction of the FLSPs. In order to verify the viability of our model, with specific data derived from the daily scheduling operation records of the Baoyun Logistics Company, we perform a numerical analysis using genetic algorithms based on multiple datasets where a single parameter is changed each time while the other parameters remain unchanged. The results show that the CODP moves earlier with the increase of the volume of new order. Furthermore, as the new order's similarity coefficient increases or the new order's completion time requirement is relaxed, the CODP moves later in the LSSC. With a given similarity coefficient or time patience variance related to the new order, the maximum insertion volume decreases and the CODP moves later in the LSSC.

IOBPCS based models and decoupling thinking

The inventory and order based production control system (IOBPCS) is mainly a model of a forecast driven production system where the production decision is based on the forecast in combination with the deviation between target inventory and actual inventory. The model has been extended in various directions by including e.g. WIP feedback but also by interpreting the inventory as an order book and hence representing a customer order driven system. In practice a system usually consists of one forecast driven subsystem in tandem with a customer order driven subsystem and the interface between the two subsystems is represented by information flows and a stock point associated with the customer order decoupling point (CODP). The CODP may be positioned late in the flow, as in make to stock systems, or early, as in make to order systems, but in any case the model should be able to capture the properties of both subsystems in combination. A challenge in separating forecast driven from customer order driven is that neither the inventory nor the order book should be allowed to take on negative values, and hence non-linearities are introduced making the model more difficult to solve analytically unless the model is first linearized. In summary the model presented here is based on two derivatives of IOBPCS that are in tandem, and interfaces between them related to where the demand information flow is decoupled and the position of the CODP.