Order Management in the Customization‐Responsiveness Squeeze*

Make‐to‐order (MTO) products may be either customized or standard, and customization can occur either at the configuration or component level. Consequently, MTO production processes can be divided into three customization gestalts: non‐customizers, custom assemblers, and custom producers. In this article, we examine how the multilevel nature of customization affects order management in processes that produce complex MTO products. We first empirically validate the existence of the three customization gestalts and subsequently, analyze the order management challenges and solutions in each gestalt in a sample of 163 MTO production processes embedded in seven different supply chains. In the analyses, we follow a mixed‐methods approach, combining a quantitative survey with qualitative interview data. The results show that important contingencies make different order management practices effective in different gestalts. Further qualitative inquiry reveals that some seemingly old‐fashioned practices, such as available‐to‐promise verifications, are effective but commonly neglected in many organizations. The results also challenge some of the conventional wisdom about custom assembly (and indirectly, mass customization). For example, the systematic configuration management methods—conventionally associated with project business environments—appear to be equally important in custom assembly.     

An Application of Master Schedule Smoothing and Planned Lead Time Control

Make‐to‐order (MTO) manufacturers must ensure concurrent availability of all parts required for production, as any unavailability may cause a delay in completion time. A major challenge for MTO manufacturers operating under high demand variability is to produce customized parts in time to meet internal production schedules. We present a case study of a producer of MTO offshore oil rigs that highlights the key aspects of the problem. The producer was faced with an increase in both demand and demand variability. Consequently, it had to rely heavily on subcontracting to handle production requirements that were in excess of its capacity. We focused on the manufacture of customized steel panels, which represent the main sub‐assemblies for building an oil rig. We considered two key tactical parameters: the planning window of the master production schedule and the planned lead time of each workstation. Under the constraint of a fixed internal delivery lead time, we determined the optimal planning parameters. This improvement effort reduced the subcontracting cost by implementing several actions: the creation of a master schedule for each sub‐assembly family of the steel panels, the smoothing of the master schedule over its planning window, and the controlling of production at each workstation by its planned lead time. We report our experience in applying the analytical model, the managerial insights gained, and how the application benefits the oil‐rig producer.     

Lowest Cost May Not Lower Total Cost: Using “Spackling” to Smooth Mass‐Customized Production

Consider a manufacturer who mass customizes variants of a product in make‐to‐order fashion, and also produces standard variants as make‐to‐stock. A traditional manufacturing strategy would be to employ two separate manufacturing facilities: a flexible plant for mass‐customized items and an efficient plant for standard items. We contrast this traditional focus strategy with an alternative that better utilizes capacity by combining production of mass‐customized and standard items in one of two alternate spackling strategies: (1) a pure‐spackling strategy, where the manufacturer produces everything in a (single) flexible plant, first manufacturing custom products as demanded each period, and then filling in the production schedule with make‐to‐stock output of standard products; or (2) a layered‐spackling strategy, which uses an efficient plant to make a portion of its standard items and a separate flexible plant where it spackles. We identify the optimal production strategy considering the tradeoff between the cost premium for flexible (versus efficient) production capacity and the opportunity costs of idle capacity. Spackling amortizes fixed costs of capacity more effectively and thus can increase profits from mass customization vis‐à‐vis a focus strategy, even with higher cost production for the standard goods. We illustrate our framework with data from a messenger bag manufacturer.     

PRODUCT OFFERING,PRICING, AND MAKE‐TO‐STOCK/MAKE‐TO‐ORDER DECISIONS WITH SHARED CAPACITY

In an era of mass customization, many firms continue to expand their product lines to remain competitive. These broader product lines may help to increase market share and may allow higher prices to be charged, but they also cause challenges associated with diseconomies of scope. To investigate this tradeoff, we considered a monopolist who faces demand curves, which for each of its potential products, decline with both price and response time (time to deliver the product). The firm must decide which products to offer, how to price them, whether each should be make‐to‐stock (mts) or make‐to‐order (mto), and how often to produce them. The offered products share a single manufacturing facility. Setup times introduce disceonomies of scope and setup costs introduce economies of scale. We provide motivating problem scenarios, model the monopolist's problem as a non‐linear, integer programming problem, characterize of the optimal policy, develop near‐optimal procedures, and discuss managerial insights.     

Computer support for continuous improvements

Abstract. The view of manufacturing has changed in recent years. This is evident from how the focus of management's attention is shifting more and more towards the basics of operating the business. The challenge to manufacturers in the West today is how to introduce leaner practices in their operations. The objective of this paper is to demonstrate how computer technology and information systems can be applied to support the transition to leaner practices in a complex operation. The problem with the large information systems developed to manage the complex operations of many Western companies is that they were designed to help maintain performance and not to support finding opportunities for new improved practices. Results from the ESPRIT II project, CIM for Multi-supplier Operations (CMSO), show that an existing data infrastructure can be effectively utilized to develop inexpensive, customized tools to support a process of continuous improvements. In this project a set of tools for planning the material flow from supplier to the assembly line was developed for the Saab-Valmet automobile assembly plant in Uusikaupunki, Finland. Hypertext tools were used to quickly develop decision support applications that supported problem solving and finding improvement potentials.     

Production planning of mixed-model assembly lines: a heuristic mixed integer programming based approach

Process technology capabilities are becoming increasingly important as flexible manufacturing continues to be more prevalent, and as competition compels companies to provide expanded variety, at ever lower cost, so introducing plant and processes technological constraints. Model flexibility can also benefit from an appropriate production planning process, especially concerning mixed-model assembly lines, since it can facilitate master scheduling and line balancing activities, which are essential aspects of flexibility. Robust and practical planning approaches have to take into account two different aspects: the first consists in ensuring that the elaborated aggregate plan can be disaggregated into at least one detailed feasible plan for the realised demand, whereas the second in ensuring that this detailed plan is feasible at the operational level. This article faces the model flexibility challenge, reviewing and discussing the planning problem of a real world assembly manufacturing system, producing high volume and a variety of agricultural tractors and machines, analysing and resolving some important issues related to technological, organisational and managerial constraints. This article illustrates the implementation of an Advanced Planning System integrated with a mixed integer-programming model, which is solved by a new iterative heuristic approach capable of achieving interesting planning improvements for model-flexibility management.     

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.     

Scheduling two-stage production lines with multiple machines

Most manufacturing process maintain separate fabrication and assembly centres. Based on this observation, the author coincides a manufacturing process that contains two stages of production with multiple machines. The manufacturer produces a variety of products to satisfy customer demands, operates under a 'push' mode and in a ‘ make-to-order’ environment. Each customer order consists of known quantities of different products which must be delivered as a whole shipment. Periodically, the manufacturer schedules all the accumulated unscheduled customer orders. The scheduling objective is to minimize the sum of weighted customer order lead times. Such manufacturing systems are formulated as a mathematical programming problem. It is then shown that this problem is unary NP-hard and remains unary NP-hard even when all the weights are equal. Some insights about the structure of the optimal schedule(s) are provided and some special cases solved in polynomial time. Several polynomial time heuristics are proposed, and worst-case analysis of some of the heuristics are provided. Tight lower bounds are developed in order to measure the performance of the proposed heuristics. Numerical examples are presented and possible extensions are discussed.     

Measuring Modularity‐Based Manufacturing Practices and Their Impact on Mass Customization Capability: A Customer‐Driven Perspective

As uncertainty in markets and technology intensifies, more companies are adopting modular product and process architectures to cope with increasing demands for individually customized products. Modularity‐based manufacturing is the application of unit standardization or substitution principles to create modular components and processes that can be configured into a wide range of end products to meet specific customer needs. This study defines modularity‐based manufacturing practices (MBMP), develops a valid and reliable instrument to measure MBMP, builds a framework that relates customer closeness, MBMP, and mass customization capability, and tests structural relationships within this framework using LISREL. Based on 303 responses from members of the Society of Manufacturing Engineers, statistically significant and positive relationships were found among customer closeness, modularity‐based manufacturing practices, and mass customization capability. Managerial implications of the empirical findings of this study and future research directions are also discussed.     

产品平台在基于大规模定制范式的生产系统中的作用建模

本文围绕一致的质量、合理的成本、快速、灵活、可靠地大规模交付定制化产品或服务的运营战略,概要讨论了面向大规模定制的三种主要的生产系统(交付定制型、装配定制型、制造定制型)的运营特征,详细分析了产品平台的体系结构,首次提出了基于产品平台实施大规模定制的整合策略,对产品平台在面向大规模定制的生产系统的作用进行了建模。本文的研究工作已经在企业应用实践中得到了验证。     

Linking Work Design to Mass Customization: A Sociotechnical Systems Perspective*

Mass customization has gained increasing importance in recent years due to its ability to provide customized products efficiently and effectively, and manufacturing companies are continuously searching for ways to develop their mass‐customization ability. Despite extensive literature focusing on mass customization, few studies have systematically examined the impact of work‐design practices on a company's mass‐customization ability. Using the Sociotechnical Systems theory as a foundation, we link work‐design practices with mass‐customization ability, specifically identifying ten work‐design practices and examining their impact on mass‐customization ability using survey data and empirical research methods. The results support our hypothesized links and suggest that work‐design practices that manage both the technical and the social dimensions for achieving organization success have significant impact on a company's ability to achieve mass customization.     

MIXED ASSEMBLY AND DISASSEMBLY OPERATIONS FOR REMANUFACTURING

In this paper we consider the problem of designing a mixed assembly‐disassembly line for remanufacturing. That is, parts from the disassembly and repair of used products can be used to build “new” products. This is a problem common to many OEM remanufacturers, such as Xerox or Kodak. We study two main configurations, under the assumption that the disassembly sequence is exactly the reverse of the assembly sequence. Under a parallel configuration, there exist two separate dedicated lines, one for assembly and one for disassembly, which are decoupled by buffers—from both disassembly operations, which have preference, as well as parts from an outside, perfectly reliable supplier. Under a mixed configuration, the same station is used for both disassembly and assembly of a specific part. The problem is studied using GI/G/c networks, as well as simulation. Due to a loss of pooling, we conclude that the parallel configuration outperforms the mixed line only when the variability of both arrivals and processing time are significantly higher for disassembly and remanufacturing than for assembly. Via a simulation, we explore the impact of having advanced yield information for the remanufacturing parts. We find that advanced yield information generally improves flow times; however, there are some instances where it lengthens flow times.     

A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.     

Hierarchical production planning for multi-product lines in the beverage industry¶

Multi-commodity production and distribution scheduling is one of the most complex and crucial problems facing many manufacturing companies. For a major European manufacturer specialising in bottling juices and drinks, we have designed and developed a hierarchical decomposition approach to the solution of the multi-commodity production planning problem. In this paper we focus our attention on the coarsest decomposition level, called multi-commodity aggregate production planning (MCAP). It concerns the choice of the best feasible production plan for a set of products (commodities) over an extended time horizon so as to meet forecast aggregate demands throughout the horizon. At this level, the problem constraints include hard constraints (such as production lines having a maximum capacity and products having short life-times), and soft constraints (budgetary concerns.) The objective is to determine the production plan that covers each period's demands as best as possible, while minimizing all relevant costs. Our method for solving MCAP produces optimal plans in negligible times in commodity PC workstations.     

基于个性化需求的产品竞争供应链结构选择

基于"制销分离"与"定制一体"两种结构选择,构建个性化产品供应链处于非竞争与竞争环境下的博弈模型,在引入一个实际案例的基础上,运用算例仿真比较分析两种运营模式下的供应链运作策略和盈利差异。结果显示：与制销分离结构相比,个性化产品供应链选择定制一体结构,有利于匹配产品个性化制造、提升产品个性化水平和市场需求、增加供应链的期望收益;个性化产品供应链选择制销分离结构时,适度的批发价格激励能够提升产品个性化水平、更好满足消费者个性化需求、改善供应链的运营绩效,分销商适度让利加大批发价格激励力度可显著促进产品个性化制造互动、提升产品个性化水平;竞争将消减个性化产品供应链的运营绩效,但选择定制一体结构可显著提升竞争力。     

The Ordered Cutting Stock Problem

The one‐dimensional cutting stock problem (CSP) is a classic combinatorial optimization problem in which a number of parts of various lengths must be cut from an inventory of standard‐size material. The classic CSP ensures that the total demand for a given part size is met but ignores the fact that parts produced by a given cutting pattern may be destined for different jobs. As a result, applying the classic CSP in a dynamic production environment may result in many jobs being open (or partially complete) at any point in time—requiring significant material handling or sorting operations. This paper identifies and discusses a new type of one‐dimensional CSP, called the ordered CSP, which explicitly restricts to one the number of jobs in a production process that can be open, or in process, at any given point in time. Given the growing emphasis on mass customization in the manufacturing industry, this restriction can help lead to a reduction in both in‐process inventory levels and material handling activities. A formal mathematical formulation is provided for the new CSP model, and its applicability is discussed with respect to a production problem in the custom door and window manufacturing industry. A genetic algorithm (GA) solution approach is then presented, which incorporates a customized heuristic for reducing scrap levels. Several different production scenarios are considered, and computational results are provided that illustrate the ability of the GA‐based approach to significantly decrease the amount of scrap generated in the production process.     

Planning of a decentralized distribution network using bilevel optimization

This paper proposes a bilevel optimization problem to model the planning of a distribution network that allows us to take into account how decisions made at the distribution stage of the supply chain can affect and be affected by decisions made at the manufacturing stage. Usually, the distribution network design problem decides on the opening of depots and the distribution from the depots to customers only and pays no attention to the manufacturing process itself. By way of example, the paper discusses the implications of formulating a bilevel model to integrate distribution and manufacturing, maintaining the hierarchy existing in the decision process. The resulting model is a bilevel mixed integer optimization problem. Hence, only small instances can be optimally solved in an acceptable computing time. In order to be able to solve the optimization model for realistic large systems, a metaheuristic approach based on evolutionary algorithms is developed. The algorithm combines the use of an evolutionary algorithm to control the supply of depots with optimization techniques to determine the delivery from depots to customers and the supply from manufacturing plants to depots. A computational experiment is carried out to assess the efficiency and robustness of the algorithm.     

The Impact of Organizational Structure on Mass Customization Capability: A Contingency View

This study investigates the role of organizational structure in facilitating the development of mass customization (MC) capability in various manufacturing settings. Specifically, three dimensions of organizational structure are considered—flatness, centralization, and employee multifunctionality. We model organizational structure as a second‐order factor whose value is captured on a mechanistic‐organic continuum, where the organic form is characterized by a flat, decentralized structure with a wide use of multifunctional employees. We propose that a positive relationship exists between the organic organizational structure and MC capability. Additionally, building upon contingency theory, we argue that this positive relationship is moderated by mass customizer type—full mass customizers, which customize products at the design or fabrication stage of the production cycle, versus partial customizers, which customize products only at the assembly or delivery stages. Based on a study of 167 manufacturing plants from three industries and eight countries, we find that, for the overall sample, organic structure plays a significant role in enabling firms to pursue MC capability. However, an analysis of full versus partial mass customizers shows that the positive impact of organic structure on MC capability is statistically significant only for full mass customizers, not for partial mass customizers.     

A New Framework for Manufacturing Planning and Control Systems*

This paper presents a new framework for manufacturing planning and control systems which we call iterative manufacturing planning in continuous time (IMPICT) that appears to have several advantages over the well-known material requirements planning (MRP) framework. IMPICT explicitly considers capacity constraints and total system cost (including tardiness) to determine order sizes, order release/due dates, and operation schedules in a deterministic, multi-level, finite horizon, dynamic demand environment. Continuous time scheduling variables allow setups to be carried over from one period to the next. Three new heuristics built on the IMPICT framework are presented and tested in a simulation-based, full-factorial experiment with a wide variety of problem environments. The benchmark for the experiment was materials requirements planning with operations sequencing (MRP/OS) implemented with best-case, fixed planned lead times. The experiment showed that all three heuristics were statistically better than MRP/OS. The total cost for the order merging (OM) heuristic was 25 percent better than the total cost for MRP/OS. Computational times for OM were substantially larger than for MRP/OS; however, the computational times in the experiment suggest that OM is still computationally viable for large-scale batch manufacturing environments found in industry. IMPICT is superior to standard MRP systems because it explicitly considers capacity constraints and total system costs when it creates a materials plan. IMPICT is superior to linear programming-based approaches to finite loading and scheduling found in the literature because it allows setups to be carried over from one period to another and because it is computationally viable for realistic-sized problems.     

Some structural properties for the just-in-time level schedule problem

Just-in-time (JIT) is a pull concept applied mainly in repetitive manufacturing systems, and it is characterized by a scenario where only the required units are produced in the required quantities at the required times. It particularly aims at eliminating wastes associated with inventories in the system. A level schedule is desirable for a JIT assembly system, as it serves as an approximation for all forms of smoothing. The min-sum formulation of the assembly line level schedule problem is one of those that has been mainly used in the literature. Using this formulation as a base, we develop some useful structural properties for the problem. Among other things, it is shown that a level schedule would tend to be more difficult to achieve for products (models) with comparatively fewer units in the products composition structure.