Marketing in the emerging era of build-to-order manufacturing

Build-to-order (BTO) and lean manufacturing processes are changing the paradigms under which businesses-to-business marketers operate. For example, BTO processes allow marketers to customize products to a greater degree, creating a competitive advantage over traditional manufacturing. Business-to-business (B2B) marketers who take advantage of the operational efficiencies and effectiveness that emerge from BTO are outperforming firms that utilize traditional manufacturing processes in multiple industries, such as office furniture, personal computers, and windows. This paper examines the long-term impact of the adoption of build-to-order manufacturing strategies on the marketing function and identifies marketing strategies associated with successful BTO companies. Throughout, the paper highlights managerial implications and proposes directions for future research.     

Integrated production planning with sequence-dependent family setup times

This paper proposes an integrated optimization model of aggregate production planning (APP), family disaggregation planning, and family scheduling problems in hierarchical production planning (HPP) systems considering sequence-dependent family setup times. The model obtains the optimal production plan for each product type and product family in each period, together with the globally optimal production sequence of product families in all planning periods. The proposed model is tested with randomly generated experimental data consistent with what is prevalent in the manufacturing industry and its results are compared with those of the traditional HPP models. Our results show that the integrated model realizes greater cost savings.     

An integrated production planning model for molds and end items

This study develops a mathematical modelling framework for simultaneously generating production plans for molds and the end items that are made with them. The inputs considered are the item demand (assumed constant over an infinite planning horizon), holding costs and shortage costs, together with the molds’ statistical lifetime distribution (in terms of number of uses) and costs pertaining to amortization, preventive replacements and corrective replacements.     

An iterative approach to item-level tactical production and inventory planning

In this paper, we propose an iterative approach to jointly solve the problems of tactical safety stock placement and tactical production planning. These problems have traditionally been solved in isolation, even though both problems operate in the same decision making space and the outputs of one naturally serve as the inputs to the other. For simple supply chain network structures, two stages and one or many products, we provide sufficient conditions to guarantee the iteration algorithm’s termination. Through examples, we show how the algorithm works and prove its applicability on a realistic industrial-scale problem.     

Robust optimal policies of production and inventory with uncertain returns and demand

We consider an inventory and production planning problem with uncertain demand and returns, in which the product return process is integrated into the manufacturing process over a finite planning horizon. We first propose an inventory control model for the return and remanufacturing processes with consideration of the uncertainty of the demand and returns. Then a robust optimization approach is applied to deal with the uncertainty of the problem through formulating a robust linear programming model. Moreover, properties on the robust optimization model are studied, and an equivalent robust optimization model based on duality theory is obtained which allows the solutions to be derived more efficiently. Finally, we provide a set of numerical examples to verify the effectiveness of the approach and analyze the effects of the key parameters on the solutions.     

Integrating noncyclical preventive maintenance scheduling and production planning for a single machine

This paper deals with the problem of integrating noncyclical preventive maintenance and tactical production planning for a single machine. We are given a set of products that must be produced in lots during a specified finite planning horizon. The maintenance policy suggests possible preventive replacements at the beginning of each production planning period, and minimal repair at machine failure. The proposed model determines simultaneously the optimal production plan and the instants of preventive maintenance actions. The objective is to minimize the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. The problem is solved by comparing the results of several multi-product capacitated lot-sizing problems. The value of the integration and that of using noncyclical preventive maintenance when the demand varies from one period to another are illustrated through a numerical example and validated by a design of experiment. The later has shown that the integration of maintenance and production planning can reduce the total maintenance and production cost and the removal of periodicity constraint is directly affected by the demand fluctuation and can also reduce the total maintenance and production cost.     

Reengineering the production planning process in the food industry

This paper addresses managerial process reengineering and in particular the reengineering of the production planning process. The reengineered process highlights planning options to avoid the process imbalance and loss of production potential that can follow innovation in a facility that is fully committed to JIT production.The study was motivated by production problems following product innovation within the food industry but the reengineering conclusions and procedures are applicable to all similarly structured industries. An example based on data from a snack food manufacturing company illustrates the reengineered procedure for a plant that is typical of the food processing industry.     

A master production scheduling procedure for stochastic demand and rolling planning horizons

The problem of interest is a one product, uncapacitated master production schedule (MPS) in which decisions are made under rolling planning horizons. Demand is stochastic and time varying, and effectiveness is measured by inventory holding, production setup, and backorder costs.Typically, in both the research literature and the business practice the stochastic nature of the problem is modeled in an ad hoc fashion. The stochastic MPS problem is usually solved by adding safety stock to production quantities obtained from a deterministic lot-sizing algorithm. Here, the stochastic nature of the problem is explicitly considered, as an optimal algorithm for solving the static probabilistic dynamic lot-sizing problem is adapted to rolling planning horizons. The resulting algorithm is found to dominate traditional approaches over a wide variety of experimental factors, reducing total costs by an average of 16% over traditional methods.     

Flexibility and rigidity in customization and build-to-order production

This paper deals with the efforts of firms to tailor their products to the needs of individual customers. The overall aim is to analyse the features of the evolving activity structures where customized offerings are created with ‘near mass production efficiency’. The paper begins with a literature review outlining modularity and build-to-order production as key features of customizing. The empirical setting of the case study is Volvo Cars' activity structure for build-to-order production. This structure is compared with previous means of making products available. The conclusion of the study is that activity structures for build-to-order production require extensive coordination, exchange of information, and interaction across company boundaries. The analysis shows that these structures rely on flexibility in some dimensions, while in others they build on rigidity. The paper concludes with implications for suppliers regarding how to combine flexibility and rigidity in order to be able to customize at reasonable costs.     

Supply optimization for the production of raw sugar

Based on a case study from Venezuela, the production of raw sugar is investigated. Ideally, sugar mills operate at a constant production rate. However, safety stocks of the raw material cannot be maintained as sugar cane quality deteriorates very rapidly. Sugar cane is therefore continuously sourced in diverse quantities and qualities from hundreds of geographically dispersed haciendas and supplied to the milling process. Furthermore, due to weather conditions changing throughout the year, tight time windows must be observed for harvesting.The approach presented in this paper aims at preserving a constant supply while minimizing the associated costs. The entire planning problem is structured in a hierarchical fashion: (1) cultivation of the haciendas, (2) harvesting, and (3) dispatching of the harvesting crews and equipment. The corresponding optimization models and solution procedures are introduced and applied to the case study problem.     

Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems

In this paper, an integrated mathematical model of the multi-period cell formation and production planning in a dynamic cellular manufacturing system (DCMS) is proposed with the aim of minimizing machine, inter/intra-cell movement, reconfiguration, partial subcontracting, and inventory carrying costs. This paper puts emphasis on the effect of the trade-off between production and outsourcing costs on the re-configuration of the cells in cellular manufacturing systems (CMSs) under a dynamic environment, in which the product mix is different from a period to another resulting in the operational dynamism in the cells. The proposed model is verified by a number of numerical examples and related sensitivity analysis.     

Modeling the response capability of a production system

This work is aimed at investigating the impact of labor flexibility and machine flexibility in the response capability of a production system in the clothing industry. To do this the response capability of a production system is modeled as a function of different flexibilities. From this model a 3² factorial experimental design is configured, which is implemented in the Arena 7.01 simulation language. The results show greater importance of machine flexibility compared to labor flexibility for the types of variability studied, and the performance of a production system when a high level of flexibility is used is similar to that in which a medium level is used.     

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.     

Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations

A semiconductor supply network involves many expensive steps, which have to be executed to serve global markets. The complexity of global capacity planning combined with the large capital expenditures to increase factory capacity makes it important to incorporate optimization methodologies for cost reduction and long-term planning. The typical view of a semiconductor supply network consists of layers for wafer fab, sort, assembly, test and demand centers. We present a two-stage stochastic integer-programming formulation to model a semiconductor supply network. The model makes strategic capacity decisions, (i.e., build factories or outsource) while accounting for the uncertainties in demand for multiple products. We use the model not only to analyze how variability in demand affects the make/buy decisions but also to investigate how the correlation between demands of different products affects these strategic decisions. Finally, we demonstrate the value of incorporating demand uncertainty into a decision-making scheme.     

A dynamic model for the optimization of decoupling point and production planning in a supply chain

In this paper, we propose a dynamic model to simultaneously determine the optimal position of the decoupling point and production-inventory plan in a supply chain such that the total cost of the deviation from the target production rate and the target inventory level is minimized. Using the optimal control theory, we derive the closed form of the optimal solution when the production smoothing policy and the zero-inventory policy are applied. The result indicates that under the production smoothing policy, the overestimation of demand rate during the pre-decoupling stage guarantees the existence of the optimal decoupling point; meanwhile the optimal decoupling point exists under zero-inventory policy when the demand rate is underestimated. Also we perform mathematical analysis on the behavior of the optimal production rate and the inventory level and the effect of problem parameters such as the length of the product life cycle and the forecast error on the performance.     

Lean planning in the semi-process industry, a case study

The lean approach is an idealizing improvement approach that has an enormous impact in the field of operations management. It started in the automotive industry and has since been widely applied in discrete manufacturing. However, extensions to the (semi-) process industry have been much slower. Resource characteristics of the (semi-) process industry obstruct a straightforward application. The notion of the point of discretization for the (semi-) process industry is helpful here. This notion builds on the fact that in most (semi-) process industries there is a point in production where process production turns into discrete production. Downstream of this point lean principles are applicable in a straightforward manner, while upstream lean needs to be interpreted in a more liberal way. In this article we address this issue by a case study. The study considers how the principles of ‘flow’ and ‘pull’ production - suggesting a regular, demand-driven product flow - may be implemented for the (semi-) process industry by introducing cyclic schedules. The conjectures guiding the case study are: (i) Cyclic schedules fit in a lean improvement approach for the semi-process industry, (ii) Cyclic schedules help to improve production quality and supply-chain coordination and (iii) Discrete event simulation is a useful tool in facilitating a participative design of a cyclic schedule. The case study is extensively described to be able to judge how the context of the changes and the intervention process contribute to the results of the intervention.     

Order release planning with clearing functions: A queueing-theoretical analysis of the clearing function concept

We consider the problem of planning future order releases in hierarchical production planning and control systems. An established research direction is the clearing function concept: the planned material flow through a production unit is modelled by inventory balance equations for WIP and final products, and the consequences of the stochastic properties of the material flow are modelled by clearing functions, which is the functional relationship between the level of WIP and the maximum output of a work centre in a period.Using a transient M/M/1 model, our paper shows that the usual definition of a nonlinear clearing function suffers from substantial shortcomings concerning both the definition of the function and empirical estimation of its parameters. We propose an alternative transient clearing function and derive a procedure for its parameterization.     

Innovation in product architecture—A study of the Chinese automobile industry

The purpose of this paper is to put forward the hypothesis that a transformation from closed integral architecture to quasi-open modular architecture contributes to the achievement of a low-cost competitive strategy by Chinese carmakers. The question, “What is quasi-open architecture in Chinese industry?” is addressed in the theoretical section, followed by the Geely case in the passenger car sector. Two perspectives form the basis of the analysis: that of the carmaker and that of its relationship with suppliers. The findings offer an insight into the competitive strategy of firms in emerging economies and some valuable observations concerning the “catching up” trajectory of Chinese firms.     

A method for reducing inter-departure time variability in serial production lines

The inter-departure time variability is an important measure in production lines. Higher variability means added work-in-process and less predictability in output. It can be a primary obstacle towards achieving on-time delivery. The effects of line parameters (e.g., line length or buffer capacity) on inter-departure time variability have been studied in recent years but no method has been proposed for its reduction. In this paper, such a strategy is proposed and studied via simulation. Results indicate that significant reductions (of more than 20%) in inter-departure time variability can be achieved for as little as 0.5% increase in the mean inter-departure time or without any increase at all, for a majority of the line parameter values experimented. This was found to be the case for symmetrical (uniform) processing time distributions as well as for asymmetrical skewed (exponential) distributions. Similar results have also been obtained in the application of the proposed strategy for the case when one station has a higher variance than the others. Therefore, in situations where output predictability is more of a problem than capacity, this strategy constitutes an effective alternative.     

Interdependencies between network and activity-analytical descriptions of production relationships in the implementation of large-scale projects – illustrated by textbook production

Interdependencies between network-based production planning in the case of large-scale projects and activity-analytical aspects of those processes are elaborated by reference to the production of a study letter for teaching purposes. Applying dynamic activity analysis, the networks can be evaluated in respect of their efficiency in consuming resources, and therefore, in turn, from performance and disposition perspectives.