Specific Customer Order Research Articles

A Systematic Literature Review: Design and Control of Warehouse Order Picking in Development of E-commerce Transaction

Order picking is the process of retrieving items from storage to meet a specific customer order, which is known to be the most labor-intensive and costly function among all the warehouse functions. This function is also important in that it has a critical impact on downstream customer service especially in facing the current covid-19 pandemic. This research aims to provide information, description and more comprehensively identifies the design and control of warehouse order picking by using empirical literature review. In this research, the stages are Identification of potential study, Study Selection based on criteria, quality assessment, data extraction, and data analysis. A systematic literature review identified 37 relevant articles for the research purpose. These articles focus on the design and control of warehouse order picking, aiming to enhance e-commerce transactions.

Digitalization for flexible and resilient production planning and scheduling in engineer-to-order manufacturing

Delivery reliability and customer satisfaction are critical for manufacturing companies that follow engineer-to-order manufacturing. Efficient production planning and scheduling is a vital requirement for flexible and prompt customer delivery. However, the uncertainty and volatility due to specific customer order requirements, production disturbances, and demand and product volume fluctuations result in frequent replanning or rescheduling of the production plans or schedules in engineer-to-order manufacturing. From a theoretical perspective, this article provides an overview of the strategies and solutions to enhance production planning and scheduling in engineer-to-order manufacturing through a literature study. From an empirical perspective, the article provides results from two industrial case studies where digital tools or digitalization were used to facilitate replanning or rescheduling and thereby attain flexibility and resilience in production planning and scheduling in engineer-to-order manufacturing.

A system dynamics archetype to mitigate rework effects in engineer-to-order supply chains

There are established archetypes that demonstrate the dynamic properties of make-to-order/stock and assemble-to-order production planning and inventory control systems and their impact on total on-costs, allowing for performance benchmarks to be established. However, the dynamic properties of engineer-to-order (ETO) production system, where products are designed and made to a specific customer order, are not well understood. Time and cost-overrun, poor capacity planning and high rates of rework are difficulties faced by ETO managers and, for now, solutions for these problems are still lacking.Therefore, this paper develops an ETO production model which merges a service-orientated design subsystem with a working-unit-orientated production subsystem to establish an order book-controlled ETO system. The developed model realises automatic capacity control to maintain the expected lead time and order book. At the same time, we also conduct transfer function and stability analysis on this holistic ETO model to investigate the system's dynamic properties using Control Theory and System Dynamics.This paper's contributions could be summarised from four perspectives. 1. It provides an automatic capacity-controlled archetype for practice benchmarking and demonstrating the advantage of a whole system level order book controller. 2. The order book proportional controller, at a whole system level rather than just in the local subsystems, can offset the rework's negative impact, while achieving target order book and service times. 3. The dynamic analysis provides transfer functions, demonstrating the dynamic relationship between demand (input) with order book and lead time (outputs). 4. The derived critical condition for system stability provides guidelines for system managers to prevent the system becoming unstable.The limitation of this paper is that we assume the rework could only happen in the production system and could be rectified in the production system. However, in practice, rework could happen and be detected everywhere. Further research could relax this assumption and explore the dynamics of these scenarios.

Sensitivity Analysis Modeling for the Evaluation of the Order Picking Process Performance

Order picking is the process of recovering goods from the picking locations to fulfil a specific customer order, which is known to be the most laborious and expensive function of all the warehousing functions. However, the effectiveness of the preparation process depends primarily on managers decisions, which are often based on intuition and experience. Due to the increasing complexity of the processes, resulting mainly from the volatility of demand and the multitude and uncertain nature of operating parameters, these decisions are still far from providing an optimal mode of operation. In this paper, an approach of dynamic modeling and control of the manual order picking process is proposed in order to carry out a sensitivity analysis. First, the model of the order picking process with its performance indicators based on dynamic modeling will be presented by detailing the different components of the system. Then the process of sensitivity analysis based on Monte Carlo simulation that will be run on the Simulink software will be outlined in order to locate the parameters most influencing the model’s outputs. The proposed approach aims to determine the reactivity of the operating processes allowing compliance with defined operational performance, which, for example, helps warehouse mangers making optimal tactical decisions in terms of means and resources to implement in a warehouse order picking activity.

Develop a Framework of strategic positioning for the order penetration point/decoupling point

Recent global challenges to appropriate implementation of manufacturing value chain, their specific product is linked to specific customer order. The order penetration point defined to value addition of manufacturing value chain. According to strategic perspective, different factors such as Market, product, and production factors are identified that affect the OPP positioning and the shifting of the OPP upstream or downstream in the manufacturing value chain. The major factors such as demand volume and volatility and their relationship between delivery lead time and production lead time and logistic service integration have identified. Comparison of manufacturing strategies must be generate between pre-OPP operations (i.e. upstream; forecast-driven) and post-OPP operations (i.e. downstream, customer order-driven), which has fundamental differences examine. And finally develop a framework to the order penetrations point or decoupling point which has affected in different factors and strategic decision categories to such as production planning and control.

Manual order consolidation with put walls: the batched order bin sequencing problem

Batching and zoning are popular picking strategies to speed up picker-to-parts order fulfillment systems. On the negative side, these strategies require a subsequent order consolidation to separate the customer orders. In a widespread setup implemented by many online retailers put walls are applied to consolidate orders manually. A batch of orders picked under a batching and/or zoning strategy and, thus, distributed over multiple bins arrives via a conveyor system in the consolidation area. Here, a logistics worker retrieves the items from the successively arriving bins and—guided by a put-to-light mechanism—places them into the put wall. The wall is a simple reach-through rack separated into multiple shelves each temporarily dedicated to a specific customer order. On the other side of the wall reside packing workers, who empty shelves and pack completed orders into cardboard boxes. We aim to optimize the bin sequence in which a batch arrives at the consolidation area, such that the probability of starving packers waiting idle for completed orders is reduced. For this purpose, we formulate the batched order bin sequencing problem and derive suited optimization procedures. In our computational study, we investigate under which circumstances optimized bin sequences are especially valuable to reduce the packers’ idle times.

Integrated decision making model for pricing and locating the customer order decoupling point of a newsvendor supply chain

A multi-product two echelon supply chain within a newsvendor framework is studied, in which semi-finished products are produced by a supplier and customized according to specific customer orders. The focus of this paper is to investigate a situation where the manufacturer wishes to determine the fraction of production performed by the supplier, its optimal semi-finished product order size, and price for each product type. In order to make the problem more practical, capacity and budget constraints are considered. The computability of the presented model is explored by a numerical example with Poisson arrival demands.

Employing Order Allocation Flexibility in Cyber-physical Production Systems

One of the main goals of Industrie 4.0 research is to enable the usage of so far unutilized flexibility potentials in production and logistics. While Industrie 4.0 research generally considers most flexibility types, such as material handling flexibility, machine flexibility and operation flexibility, order allocation flexibility in particular is so far rarely investigated. Order allocation flexibility describes the flexibility potential that occurs when half-finished goods are not assigned to a specific customer order in a production system employing the make-to-order strategy, but instead can be freely assigned to any order that might require this half-finished good with its current specifications. Furthermore, this allocation from half-finished goods to orders might even change during the production process. Employing order allocation flexibility directly increases the degree of freedom for the other aforementioned flexibility types and has therefore already found application in the production of customer specific goods, e.g. in the form of a multi-phased order release in the automotive industry. In this context, cyber physical production systems with their capability of self-optimization and control theoretically offer the possibility to determine the allocation of half-finished goods to orders in near real time during any production step, leading to a further increase of flexibility in the overall production system. This work contributes to the state of the art in Industrie 4.0 research by first analyzing the potentials of a stronger consideration of order allocation flexibility into Industrie 4.0 enabled production systems and discussing the compatibility of existing research approaches with this flexibility type. Second, a conceptual model for production planning and control employing order allocation flexibility in a cyber-physical production system will be derived.

Complexity Management in Mass Customization SMEs

As mass customization is being widely adopted, manufacturing companies are faced with an increasing challenge of establishing and maintaining manufacturing systems with sufficient flexibility to meet customers’ diverse needs and yet be efficient enough to be competitive. This is also the case in Small and Medium Sized Enterprises (SMEs), which are also experiencing a demand for increased product variety. However, increased variety in the product portfolio often implies increased complexity in manufacturing costs, which combined with low manufacturing volumes in SMEs often implies that parts of the product portfolio may prove unprofitable. This is often found to be a product management issue, where complexity increases over time, as new variety is introduced in the product portfolio, not following an explicit procedure for assessing impact of increased product variety and complexity. This paper investigates the impact of excess product variety and complexity in four different SMEs, and describes experiences with using a structured approach to assessing variety and consolidating the product portfolio. The issues found in the cases are SME specific, as the volume-variety relationship as well as the organizational resources available to perform product management in relation to new product development and adaption of the product portfolio to accommodate requirements for specific customer orders.

Sustainability in the Supply Chain through Synchronization of Demand and Supply in ETO-Companies

In the Engineer-to-Order (ETO) industry every product is unique and therefore, engineering design, production and installation is made to the specific customer order (batch-of-one production). Traditionally, Engineering, Fabrication and Installation work as separate departments connected in series. This means that customer orders are pushed from Engineering to the Installation on-site which causes long lead times, inefficient material transport and high and uncontrolled levels of Work-in-Progress (WIP). As a result, resource inefficiencies occur (costly labour delays) when the required quantity or quality of drawings or materials is not on hand in fabrication and on-site. This work presents a concept for aligning demand with supply using the Constant Work in Progress (CONWIP) control circuit and thus enabling a sustainable supply chain for ETO-companies. An industrial case study shows the current implementation in an ETO company acting as a first tier construction supplier.

An Analytical Model Describing the Lateness Distribution of Storage Processes

Enterprises are increasingly being challenged by a high diversity of variants, shortening product life cycles, and growing cost pressure. One possibility of facing these challenges is to improve the efficiency of logistics. As the borderline between a company's production with and without reference to a specific customer order, the customer order decoupling point (CODP) is a good position to significantly increase the efficiency of logistics. Apart from shortening lead times, it may also diminish the lateness at the end of the supply chain. There is currently no comprehensive analytical description of actual schedule variances within a supply chain. This paper presents a Lateness Histogram to serve as an analytical model designed to describe the mean and maximum delivery delay in a store (e.g. CODP). It is fed with the distribution of schedule and quantity variances at the store input as well as the distribution of demand at the store output. Thus, the model is another step towards analytically describing the potentials of implementing a CODP or moving it along the production process in order to obtain a possibility of quantifying the amount of lateness reduction as the CODP moves downstream. This is followed by empirical simulation studies aimed at validating the model.

Strategies for customized shipbuilding with different customer order decoupling points

The ship design and construction industry serves a considerable range of market segments, with different levels of required customization, different demand volumes, and other product and market variations. In order to effectively respond to various market requirements, strategies and related work processes need to be differentiated. An important strategic concept to make distinctions among strategies is the customer order decoupling point, that is, the point in the value chain where the product is linked to a specific customer order. This article aims to analyse and compare strategies for customized, low-volume ship design and construction from the perspective of the customer order decoupling point and to link them to product and market characteristics. It is based on a case study at the Ulstein Group, an established Norwegian ship designer and builder. The study allowed us to define ‘customized design’ and ‘standardized design’ as two different strategies that fundamentally differ in terms of the customer order decoupling point. In the former, customized ships are offered in a process where most activities are driven by the expectations and requirements of a particular customer. In the latter, the customer is given only a limited choice of predefined, standardized, and well-proved options. We conclude that customer order decoupling point positions upstream in the value chain imply high levels of flexibility and customization, while downstream positions allow short lead times, high delivery precision, and lower prices. The customer order decoupling point perspective provides a useful framework in which to analyse the ship design and construction industry.

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.

A hybrid case-GA-based decision support model for warehouse operation in fulfilling cross-border orders

The decision-making process is one of the complicated processes involved in warehouse operation for efficiently fulfilling various specific customer orders. This is especially true if the orders require cross-border delivery activities, such as palletization of the delivery goods according to regulation requirements. Case-based reasoning is an intelligent method for complex problem solving that uses past cases to find a solution to new problems. To achieve an appropriate solution, retrieving useful prior cases effectively for the problem is essential. However, current case retrieval methods are mainly based on a fixed set of attributes for different type of orders in which specific order features for case groups are neglected. In this paper a hybrid approach called the case-genetic algorithm-based decision support model (C-GADS), is proposed in classifying new customer orders into case groups with the highest similarity value, allowing for effectively selecting the most similar cases among the group. The proposed model also suggests the types of features considered in each case group. It helps enhance the effectiveness of formulating warehouse order operations based on grouping similar cases. To validate the feasibility of the proposed model, a case study is conducted and the results show that planning effectiveness is enhanced.

오더피킹 성능 : 전략, 이슈, 측도

This paper is to review and organize performance strategies, issues, and measures for the efficient operation of order picking function. Order picking is the process of retrieving items from storage to meet a specific customer order, which is known to be the most labor-intensive and costly function among all the warehouse functions. This function is also important in that it has a critical impact on downstream customer service. For understanding the background of order picking and related performance issues, we will briefly introduce warehousing functions. Then we will introduce material handling within a warehouse and order picking strategies. Lastly, we will discuss about performance issues and measures in the domain of order picking operations. Productive and quality measures will be reviewed in more detail.

A Web-based system for proactive management of supply exceptions

To avoid or mitigate the ripple effect of a supply exception (defined here as a partial and/or late delivery) in the supply chain, it is imperative to identify the exceptional event early and handle it effectively and efficiently between the affected parties. This paper proposes a Web-based system which enables the early identification of supply exceptions and provides structured business processes for handling them. Particularly, through the dynamic mapping of a specific customer order with finished goods inventory and the progress of internal work orders against a pre-defined milestone model for each product, the status of the customer’s order is determined. In case of an exception (i.e. a customer order cannot be fulfilled as promised in the order schedule), a standard process is triggered to handle it collaboratively by business partners through fast, effective negotiations. To achieve these objectives, a top-down system design approach was employed, which covers conceptualization of the key co-ordination processes, methodology development, design of system architecture and the integrative processes and subsequent system implementation. To demonstrate the effectiveness of the Web-based system, an example is used to compare the current process with the one proposed. The system was evaluated in the context of the IST project Co-OPERATE. Finally, the presented work is concluded and future research directions are indicated.

Online MTO lead-time scheduling: a probabilistic approach

This paper considers a lead time (or due date) scheduling problem faced by a manufacturer that produces customised products according to specific customer orders. It uses a single sever model to design an effective, yet simple heuristic procedures for both sequencing and lead time determination decisions. Typically, a series of jobs with individual arrival times and weights must be sequenced on a single machine. With each arrival, a lead time must be determined, and the job completed before the promised due date. An on-line heuristic may be developed in certain situations, based on limited information about the future, with the objective of minimising the average (or total) weighted due date for large size instances. Computational testing shows that this heuristic is more effective than the conventional scheduling rules not only for the average weighted due date, but also for the average weighted lead time, for both stable systems and heavily loaded ones.

Strategic positioning of the order penetration point

The order penetration point (OPP) defines the stage in the manufacturing value chain, where a particular product is linked to a specific customer order. Different manufacturing environments such as make-to-stock (MTS), assemble-to-order (ATO), make-to-order (MTO) and engineer-to-order all relate to different positions of the OPP. These may be considered as product delivery strategies, having different implications for manufacturing objectives such as customer service, manufacturing efficiency and inventory investment. Furthermore, the OPP may differ between products and over time for a particular manufacturing firm. In this paper, the positioning of the OPP is treated from a strategic perspective. Market, product, and production factors are identified that affect the OPP positioning and the shifting of the OPP upstream or downstream in the manufacturing value chain. The major factors are demand volume and volatility, and the relationship between delivery and production lead times. These factors are included in a model that allows the manufacturing firm to choose the right product delivery strategy. Different manufacturing strategies must be developed for pre-OPP operations (i.e. upstream; forecast-driven) vs. post-OPP operations (i.e. downstream, customer-order-driven), since these two stages are fundamentally different. As a consequence, a manufacturing firm that has an ATO product delivery strategy must differentiate between MTS operations (upstream the OPP) and MTO operations (downstream the OPP). For example, the competitive priorities differ: price for pre-OPP operations but delivery speed and flexibility for post-OPP operations. Therefore, decision categories, such as production planning and control, and performance measurement must be designed accordingly. Guidelines are provided for this strategic choice.

Inventory policies for all-or-nothing demand processes

When there is insufficient stock to meet a specific customer order, a common assumption is that the remaining stock will be used to satisfy the order partially while the balance of the order is backordered or lost – this is the ‘partial backorder’ or ‘partial lost sales’ assumption. In this paper we consider what happens when the nature of the demand process is such that orders must be met in full or not at all – this is the ‘all-or-nothing’ demand assumption. We concentrate, for the purpose of illustration, on the context of a continuous review model, with a Poisson customer arrival rate, with a fixed lead time and for which not more than one replenishment order may be outstanding at any time.

With agility and adequate partnership strategies towards effective logistics networks

In the mid-1970s, there was a change in many areas of the economy — caused by the laws of supply and demand — from sellers' markets to buyers' markets. This macroeconomic phenomenon — i.e., economics — had decisive consequences for logistics partnerships among companies, and thus for microeconomics. Individual companies today seldom possess all the necessary technological competence to develop and manufacture products of a certain complexity rapidly and economically efficient with advanced technology. Single organizational units no longer handle development and manufacturing. Instead, these tasks are distributed among various companies or among several organizational units within a company. In a logistics network, the companies involved form customer–supplier links. With the exception of the consumer, each customer is a co-producer within the logistics network, and thus is also a supplier. The world of practice reveals that through the course of time, and in dependency upon the concrete demands, different partnership strategies evolve in logistics networks. These may be classified accordingly. We will examine the consequences, which appear gradually, upon the length and intensity of cooperation among companies in a logistics network and derive some plausible future scenarios. For the duration and intensity of cooperation within a logistics network, there are various strategies of partnership. According to supply and demand, as well as the type of product, all of these forms are valid today. The present contribution focuses upon four strategies in particular, namely, the “traditional” customer–supplier relationship, supply management, supply chain management, and virtual organizations. The latter three forms of cooperation were developed in response to short lead times. They are all longer-term in nature. Here, virtual organizations attempt — on the basis of a long-term network — to achieve short-term company cooperation for specific customer orders. Fundamental principles of effective logistics networks concern the agility of a company. Agile competitors, are competitors who understand how to remain competitive by means of proactive amassing of knowledge and competency. Automation with broad implementation of information technology supports agility.