Individual Customer Orders Research Articles

Lean and Industry 4.0 mitigating common losses in Engineer-to-Order theory and practice: an exploratory study

Companies employing an Engineer-to-Order (ETO) manufacturing strategy often develop, design and produce complex goods in single or small batches based on an individual customer order. The increased complexity caused by project business multiplies the engineering, cost, and change management efforts that are often required, resulting in lost productivity and more non-value-adding activities. To improve efficiency and reduce losses, ETO organizations strive to implement Lean practices and Industry 4.0 (I4.0) technologies, but the relevant literature in ETO industrial context is more exploratory and contains less empirical data. Building on the preliminary work categorizing the common losses in ETO organizations, the study examined how these can be reduced by Lean and I4.0 practices. Current literature was analyzed, and empirical data was collected using a survey questionnaire and semi-structured interviews with 16 companies from the construction, shipbuilding, and machinery and plant manufacturing industries. The scientific literature was compared with the empirical data to find out whether Lean methods and I4.0 technologies are known in research but not applied in practice and vice versa. As a result, there are several practices from both domains applied in practice to reduce losses, but the analyzed literature indicates few successful implementations. Future research should aim to provide more empirical data on the application of Lean and I4.0 practices to mitigate losses in companies with an ETO strategy and provide best practices and guidelines.

Identification and prediction of standard times in machining for precision steel tubes through the usage of data analytics

An approach to predict standard times for upcoming individual customer orders based on historical production orders is developed. The approach follows the CRISP-DM model with regard to the steps data preparation, modeling and evaluation. A precision steel tube straightening machine is provided as a validation example. Furthermore, the paper focuses on how the standard time prediction approach can be successfully integrated into the IT infrastructure of a production focused company. Especially data availability and accessibility in enterprise resource planning systems are discussed. The presented approach offers users the value of more precise scheduling in production than established approaches.

Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning

Companies with Make-to-Order (MTO) manufacturing have always faced the conflict of meeting a large volume of individual customer orders on time while remaining as flexible as possible. Unlike Make-to-Stock (MTS) manufacturing, planning the production requirements for MTO is a challenging task since incoming orders may vary in time and quantity, while also being subject to a number of variables. In some cases, the delivery time allocated by the customer can be less than the required Order Lead Time to fulfil the order. Manufacturers can respond to this with either with approached from production management or from data science. This paper presents a framework to leverage the benefit of both domains. We conduct a literature review and present the results of an expert workshop. We propose criteria for a suitable data enrichment and the application of Machine Learning (ML) methods in sales and demand forecasting. In conclusion, the proposed framework helps to equip manufacturing companies with a structured strategy for data management and to utilize the benefit of ML for sales and demand forecasting.

Scheduling and batching with evolutionary algorithms in simulation–optimization of an industrial formulation plant

Industrial scheduling problems are usually characterized by a high complexity and in many, if not all cases, a detailed representation of many specific features of the production process and of the constraints is necessary to ensure that the resulting schedules are feasible when they are executed. Such detailed representations are provided by commercial discrete-event simulation programs which are often employed in the planning phase to detect bottlenecks and to validate design decisions, but usually do not provide optimized schedules. In this paper, we combine a tailored Evolutionary Algorithm with a high-fidelity simulation model in a Simulation–Optimization approach in order to obtain optimized executable production schedules.The industrial production process considered here is a formulation plant that consists of a formulation stage and a filling stage with optional intermediate storage in a set of buffer tanks. The operational constraints include limited operator availability and shift constraints, sequence-dependent changeover times in both stages, product- and line-dependent processing rates, and the need for synchronization of operations. The objective is to minimize the tardiness of a set of orders some of which have long operation times and small slack between the earliest possible end times and the due dates.The orders consist of a certain number of batches, but if all the batches of an order are processed on the same equipment, meeting the due dates is not possible. Therefore the orders have to be decomposed into sub-orders (production orders) to realize parallel processing of urgent orders on several units with the goal to meet due dates. The decision to distribute the total demand of the individual customer orders to production orders is also called batching decision in the literature.A distinctive feature of our approach is that three independent encodings of the batching, allocation and sequencing decisions are used. The batching decisions lead to variable-length chromosomes for the allocation and sequencing encodings. For the treatment of variable-length chromosomes, new crossover and mutation operators, as well as repair algorithms are presented.The results for three test cases show that the introduction of the batching decisions significantly improves the tardiness of the production schedules. A thorough tuning of the categorical parameters of the Evolutionary Algorithm has been performed and it is shown that the resulting parameter setting yields reproducible results, while outperforming simpler approaches.

A decision support model for handling customer orders in business chain

One of the elements of the modern trade and services market is a business chain solution (chain store/retail chain). An example of a business chain is, e.g., a restaurant chain, where each restaurant in the chain has the same decor, organization, menu, and delivery method. Although such solutions have been known for decades, the rapid development of IT technology, the widespread access to the Internet as well as the development of mobile technologies have changed and modernized their formula. Many customers place orders remotely with the option of delivery to their door. This method of ordering and fulfilling orders is becoming more and more popular and ever more common in the recent period due to the pandemic and the resulting restrictions and limitations on the functioning of trade and services. The following key questions arise in relation to customer order processing for chain business managers: How to allocate individual customer orders to selected branches so that the cost of their processing (production and delivery) is the lowest?, How to deliver on time?, etc. To answer these questions, a decision support model has been developed, which combines routing, allocation and planning problems for restaurant/store chains. Two ways to implement the model have been proposed. The first one uses the methods of mathematical modeling and programming, and the other, which is a proprietary approach that integrates the mechanisms of evolution (specialized representations, repair mechanisms, genetic operators, etc.), uses constraint logic programming and dedicated heuristics. In addition, procedures for constraint handling and presolving have been developed.

COMPUTER AIDED ASSEMBLY PLANNING USING MS EXCEL SOFTWARE – A CASE STUDY

The issue of planning assembly operations remains crucial decision-making area for many of manufacturing companies. It becomes particularly significant in case of small and medium enterprises that perform unit or small-scale production, where the option of applying specialized software is often very limited – both due to high purchase price, but also due to its applicability to single unit manufacturing, that is executed based on individual customer orders. The present article describes the possibility of applying the MS Excel spreadsheet in the planning of machine assembly processes. It emphasises, in particular, the method for using the spreadsheet in subsequent stages of the process, and the identification of possible causes that have impact on problems with the planning process. We performed our analysis on the basis of actual data from one of the machine industry enterprises that manufactures in central Poland.

Mass Customization Demonstrator at an Industry 4.0 Learning Factory

The industry has been working to increase product variety while maintaining economies of scale for many years already. Mass customization strategies, product platforms, and modularity have provided the conceptual fundaments for increased product variety, targeting segmented customer needs in the last decades. Recently, technologies related to Industry 4.0 promise the needed production flexibility to support higher levels of product customization. Although the required concepts and technologies are already available, their integration for demonstration and teaching is still challenging. In this context, we discuss which technologies need to be integrated into a Learning Factory to create a comprehensive mass customization demonstrator. We also discuss how to teach mass customization leveraging the Learning Factory demonstrator. Therefore, a case study has been conducted. In order to create the mass customization demonstrator, a customizable modular product has been selected. The case product is a modular skateboard. Customization features include the wheel colors, options (rails and IoT connectivity), and the shock absorber levels (continuous scale from soft to hard). Based on an Industry 4.0 technology framework from the literature, the required technologies have been identified. The range of technologies includes: product configurator, ERP, MES, automatic computational vision quality control for the assembly, automatic torque wrench, augmented reality for assembly instructions, and additive manufacturing for the low scale, high variety production of options. Next, the technologies have been integrated. In the integration, the ERP and MES systems play a key role in managing the parts production and product assembly based on the product configurator’s individual customer orders. The demonstrator has been applied in teaching in a short-term continuing education executive program and undergraduate engineering courses. The results explore the potential benefits and limitations of the mass customization demonstrator based on Industry 4.0 technologies for supporting knowledge and technology transfer to the industry.

A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies

Mass customization and global competition require Cyber-Physical Systems of Systems (CPSoS) to become increasingly flexible. Modern CPSoS have to be able to create a wide and versatile variety of products, which takes centralized approaches to their limits. In addition, they have to produce these products as quickly as possible. Hence, they must be able to react promptly if problems arise, such as the failure of a single machine. Modern agent-based production systems provide the flexibility required to cope with these challenges. While resource agents (RAs) represent the available resources, i.e., machines, such as robots, individual customer orders can be represented by so-called product agents (PAs). However, a challenge in the design of agent-based production systems is still the amount of communication and computation that is necessary online. The PAs have to communicate their requests and the RAs their capabilities and capacities. On this basis, PAs must compute the appropriate production sequence. We propose to automatically initialize every agent with a knowledge base (KB) created a priori using semantic web technologies (SWT). On the one hand, the KBs of RAs describe the RAs' capabilities in terms of product features and production processes. Every KB of a PA, on the other hand, expresses all possible production sequences based on the customer specification and the CPSoS in question. This allows consistency checks regarding the specification as well as more purposeful communication that focuses on aspects that actually need to be determined at runtime, such as the resources' current capacities or failures. The framework presented aims to reduce both the communication and computational load necessary at runtime for agent-based CPSoS.

USE OF NETWORK THINKING METHODOLOGY FOR ANALYZING FACTORS AFFECTING ORGANIZATIONAL RISK MANAGEMENT IN CUSTOMIZED MANUFACTURING SYSTEMS

The article discusses the use of the network thinking methodology for analyzing factors affecting organizational risk management in a manufacturing system used to produce products to individual customer order. In their introduction, the authors make a case for customized manufacture of goods intended to cater to specific preferences of individual customers. This is followed by a general description of the concepts of risk, operational risk and organizational risk and an outline of the possible consequences of disruptions resulting from inadequately arranging a manufacturing system. The section that follows discusses the application of the network thinking methodology to identifying mutual relationships among elements as well as their impacts on the efficiency of organizational risk management. The methodology has helped a team of expert scholars and a practitioner to isolate elements that influence organizational risk management. After relationships among individual elements were examined, such elements could be assessed and assigned to one of the four reactivity categories of active, passive, critical or lazy. On that basis, such potential actions were defined as are designed to influence the effectiveness of organizational risk management.

Event-driven Production Planning and Control Based on Individual Customer Orders

Manufacturing companies have to increase their flexibility to obtain their competitive advantage in the future. Therefore it is necessary for companies to become adaptable and to be able to react on changing events immediately. In a large number of cases, a quick dissemination of information is prevented by the human factor. The usage of new possibilities in technology enables manufacturing processes to meet problems resulting from imperfect information sharing, cooperation and coordination. For example, the planning and control of production systems is still subject to human decisions and every production order requires the release of the responsible employee. In case of order changes, an optimized order release is prevented due to a lack of information in human centered PPC. Several approaches deal with different strategies of order releases such as autonomous or decentralized PPC. This paper introduces an event driven PPC system in order to shorten the reaction time and increasing the utilization of work load in production systems. A special focus is set on the bill of material. Traditionally, only parts and their quantitative composition are listed. If this traditional structure is extended with physical information for each part, the system will be able to derive specific actions according to the production process for the respective part autonomously. A simulation visualizes the feasibility of the approach by using an exemplary production program and the extended bill of material of individual customer orders.

Scheduling and lot sizing problems for variable range of products using GA-based method

Article proposes a method based on genetic algorithm to determine the lot size and production scheduling for variable ranges of products-couplings. The simulation computer checks the established deadlines for the various orders and strives to minimize the total cycle for production times and machine changeover times. The authors present the assumptions of the model and its results in the generation of an optimal production sequence for the analyzed couplings. The paper also comprise the analysis if it is reasonable to combine orders or share their according to individual customer orders.

Product configuration, ambidexterity and firm performance in the context of industrial equipment manufacturing

The practice of configuring products to individual customer orders has found application in a variety of industry contexts, but little is known about the specific capabilities that firms develop to successfully compete when offering configurable products. Our research begins to fill this gap in the context of industrial equipment manufacturing. Drawing from the ambidexterity literature, we argue that firms have to balance dual goals of reducing variation and promoting variation in their product configuration activities by fostering two distinct firm-level capabilities: product configuration effectiveness (PCE) and product configuration intelligence (PCI). Specifically, we hypothesize that the simultaneous presence of PCE and PCI—that is, product configuration ambidexterity (PCA)—drives superior firm responsiveness and, indirectly firm sales and operating margin. However, we also contend that responsiveness gains through PCA can diminish with product complexity and can increase operating cost. We test these hypotheses by collecting both primary and secondary data from a sample of 108 European industrial equipment manufacturing firms. Results from our analyses indicate that PCA has an indirect effect through responsiveness on sales and operating cost but not on operating margin, with this effect diminishing with product complexity. Taken together, our results suggest that investment in developing PCA may represent a conundrum for industrial equipment manufacturing firms, because it translates into market but not financial advantages, and it is intertwined with product design decisions. We conclude this study with a discussion of the findings for theory and practice.

Column generation heuristics for multiple machine, multiple orders per job scheduling problems

Front opening unified pods (FOUPs) are used to store and transport silicon wafers in 300-mm semiconductor wafer fabs. To achieve production efficiencies, wafers are grouped together in FOUPs without regard to the originating customer placing the order. In the resulting multiple orders per job (moj) scheduling problem, scheduling is performed at the FOUP (i.e., aggregated order) level, while scheduling performance is assessed per individual customer order. Column generation heuristics are presented for single and parallel machine moj scheduling problems to minimize total weighted order completion time. The proposed heuristics obtain near-optimal solutions very quickly, outperforming competing approaches in the literature.

Mathematical model for warehouse design and product allocation

The two primary functions of a warehouse include (1) temporary storage and protection of goods and (2) providing value added services such as fulfilment of individual customer orders, packaging of goods, after sales services, repairs, testing, inspection and assembly. To perform the above functions, the warehouse is divided into several functional areas such as reserve storage area, forward (order collation) area and cross-docking. The paper presents a mathematical model and a heuristic algorithm that jointly determine product allocation to the functional areas in the warehouse as well as the size of each area using data readily available to a warehouse manager.

Performance of approximations for compound Poisson distributed demand in the newsboy problem

This paper considers the classical single period inventory model. The number of customer orders during the period follows a known Poisson distribution and individual customer order sizes are independent random variables. Two costs are incurred: a cost per unit of unsatisfied demand and a cost per unit of stock purchased. The objective is to minimise the expected sum of these two costs. A customer order which cannot be met in full is met to the extent that available stock permits. Although the sizes of all customer orders are known at the time only regularly updated estimates of the first two moments of the customer order size distribution are maintained. Therefore, aggregate demand follows a compound Poisson distribution for which the moments are known but for which the exact distribution is unknown. The immediate objective of this research is to explore the effectiveness of a number of approaches for approximating a compound Poisson distribution in a single period setting. The longer-term objective is to find relatively simple but effective ways of handling a compound Poisson demand process in more general inventory settings.

Impacts of information management on customized vehicles and after-sales services

Original Equipment Manufacturers (OEMs) and their suppliers are confronted with a paradigm shift from Variant Production to Mass Customization. In contrast to variants, mass customized vehicles are configured according to each customer's individual requirements and, nevertheless, are sold at a price comparable to standard vehicles. In order to economically offer customized vehicles and after-sales services, OEMs and suppliers must closely cooperate and apply appropriate information management approaches. Starting with an overview of interconnected business processes between an OEM and supplier, the focus of this contribution lies on grasping customer's requirements as well as generating and utilizing a customer's profile. Consistency of customer's requirements is checked by car configuration and simulation tools. Subsequently, the individual customer's order is transferred into the OEM's and supplier's data management systems. In addition to data generated during the order and production process, the customer's profile is supplemented step by step during car use. Based on a detailed customer's profile, individualized after-sales services can be offered which fulfil each customer's unique requirements and thus significantly contribute to the company's revenue as well as to high customer loyalty rates.

Product family modeling and design support: An approach based on graph rewriting systems

Earlier research on product family design (PFD) often highlights isolated and successful empirical studies with a limited attempt to explore the modeling and design support issues surrounding this economically important class of engineering design problems. This paper proposes a graph rewriting system to organize product family data according to the underpinning logic and to model product derivation mechanisms for PFD. It represents the structural and behavioral aspects of product families as family graphs and related graph operations, respectively. The derivation of product variants becomes a graph rewriting process, in which family graphs are transformed to variant graphs by applying appropriate graph rewriting rules. The system is developed in the language of programmed graph rewriting systems or PROGRES, which supports the specification of hierarchical graph schema and parametric rewriting rules. A meta model is defined for family graphs to factor out those entities common to all product families. A generic model is defined to describe all specific entities relevant to particular families. An instance model describes all product variants for individual customer orders. A prototype of a graph-based PFD system for office chairs is also developed. The system can provide an interactive environment for customers to make choices among product offerings. It also facilitates design automation of product families and enhances interactions and negotiations among sales, design, and manufacturing.

A customer‐focused planning approach to make‐to‐order production

Presents a customer‐focused approach to effective planning of make‐to‐order production, in which production activities are driven by customer orders and all products are made to customers’ specifications. The approach plans, schedules, and co‐ordinates production activities, based on the needs of individual customer orders. In particular, an integrated bill of material and routeing data structure is used to effectively organise production data in response to product specifications of customer orders. It facilitates the creation of production jobs with varying routeings and material requirements. A job‐oriented finite capacity scheduling system is used to effectively accommodate specific needs of individual customer orders. It allows for realistic setting of delivery dates and negotiation of order changes. Key features of the approach presented show its effectiveness in planning multi‐item customer orders and multi‐level products.

Customer-driven manufacturing in Japan

This paper describes the evolution of manufacturing systems in Japan through four phases, from highly product oriented to highly customer oriented. The fourth phase culminates in a customer-driven approach that satisfies individual customer orders. One example of an advanced third phase company and three examples of companies in the fourth phase are presented. Some of the requirements for achieving a fourth phase orientation are provided and a discussion of the functional coordination in these advanced companies is presented.