Dynamic pricing models for ERP systems under network externality

Enterprise Resource Planning (ERP) systems vendors face great challenges to enhance their market position and maximize their profits. Being able to simultaneously predict the diffusion of an ERP and to determine the right price to charge to a customer is a complex task. Earlier work has demonstrated evidence of network effect in the diffusion of ERP software in industries. In light of this evidence, we investigate in this paper the benefit of a dynamic pricing strategy for ERP systems vendors in a business network governed by a quantitative diffusion model. Based on a real scenario in the automotive industry, those quantitative models are integrated into a simulation-based optimization approach to tackle the problem. Our findings are promising and establish the foundation of a powerful decision support tool for ERP systems vendors.     

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.     

Constraint programming for project-driven manufacturing

Project-driven manufacturing, based on the make-to-order or the build-to-order principle and predominant in small and medium-size enterprises (SMEs), calls for an efficient solution of large combinatorial problems, especially in such areas as task scheduling or resource management. This paper addresses the problem of finding a computationally effective approach to scheduling a new project subject to constraints imposed by a multi-project environment. A constraint programming (CP) modeling framework is discussed in the context of an efficient decomposition of the constraint satisfaction problem (CSP) and the evaluation of strategies for pruning the search tree. The proposed approach is illustrated through examples of its application to the evaluation of a new production order.     

Revenue management for make-to-order manufacturing systems with a real-life application

Abstract

Due to successful applications of revenue management in the airline industry, in recent years, there has been a growing interest to adopt revenue management in make-to-order (MTO) manufacturing systems. Several interrelated decision problems such as order acceptance/rejection, short-term capacity planning, due date assignment, and order scheduling need to be studied simultaneously in order to manage revenues effectively in MTO manufacturing systems. Both the producer’s and customer’s requirements need to be taken into account through some negotiation mechanisms that are sensitive to the service-level reputation of the manufacturing companies. In this article, we propose a new dynamic bid price–based revenue management model that considers all of the aforementioned decision problems simultaneously. A simulation optimization approach is utilized in order to determine the best possible values of control parameters for bid price, due date assignment, and price increment/reduction mechanisms. The performance of the proposed integrated revenue management model is tested on both a hypothetical example and a real problem of a bridal gown company. The computational results show that the proposed model provides significant improvements in total revenue compared to other static and dynamic bid price policies.     

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–distribution model for the dynamic location and allocation problem with safety stock optimization

The design and management of a multi-stage production–distribution system is one of the most critical problems in logistics and in facility management. Companies need to be able to evaluate and design different configurations for their logistic networks as quickly as possible. This means coordinating the entire supply chain effectively in order to minimize costs and simultaneously optimize facilities location, the allocation of customer demand to production/distribution centers, the inbound and outbound transportation activities, the product flows between production and/or warehousing facilities, the reverse logistics activities, etc.Full optimization of supply chain is achieved by integrating strategic, tactical, and operational decision-making in terms of the design, management, and control of activities. The cost-based and mixed-integer programming model presented in this study has been developed to support management in making the following decisions: the number of facilities (e.g. warehousing systems, distribution centers), the choice of their locations and the assignment of customer demand to them, and also incorporate tactical decisions regarding inventory control, production rates, and service-level determination in a stochastic environment. This paper presents an original model for the dynamic location–allocation problem with control of customer service level and safety stock optimization. An experimental analysis identifies the most critical factors affecting the logistics cost, and to finish, an industrial application is illustrated demonstrating the effectiveness of the proposed optimization approach.     

Preparing industrial suppliers for customer integration

In real world markets and business markets in particular, the idea of customized products and services receives increasing attention. A management tool called customer integration has been developed as an approach to master the challenges originating from customization for a supplier. Customer integration competence is the organizational quality that results from this tool after being implemented. Putting this together, we can hypothesize a causal relation between customer integration competence and market success. In order to confirm this hypothesis, the paper presents a conceptualization of customer integration competence. To accomplish this, references are made to competence models from psychology, success factor research and organizational science. The concept was validated through a survey with 78 industry experts from business markets responding. Customer integration competence measured in this way was then contrasted to a second measure capturing market success. Regression analysis supports our hypothesis strongly. Finally, implications for marketing research as well as marketing practice are presented.     

Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation

We present a guideline for selecting the inventory management policy for a specialty chemical plant that produces products with limited shelf life, by producing multiple grades of bulk chemical and packaging them into a large variety of bottles. The policy for each product was selected based on a cost-benefit analysis, using the multiple objectives of maximizing the on time order fulfillment, minimizing the production (cleaning and shelf life expiration) and inventory costs. To generate realistic cost estimates, we developed a discrete event simulation model that included demand variability, planning and scheduling policies, and operational details. We evaluated three different policies: make-to-stock (MTS), postponement, and combined MTS/postponement. We also evaluated different storage media and optimized the number of storage units for products selected for postponement. A combined MTS/postponement policy, in which postponement is applied to products with low to medium demand and high expiration provided lowest costs without significantly impacting the customer on time order fulfillment. For all other products, a MTS policy is applied to maximize order fulfillment on time, reduce cleaning costs and to reduce number of storage units.     

Scheduling a multi-stage fabrication shop for efficient subsequent assembly operations

Consideration is given to scheduling the operation of a multi-stage fabrication shop producing the component parts of a major commercial refrigerators producer. The objective of this scheduling problem is the determination of both production sequences and lot quantities at each stage so as to meet production targets and ensure continuous operation of subsequent assembly stations. After describing the problem within its natural context, we present an algorithm for the dynamic scheduling of the fabrication shop. This applies general planning principles adapted to the needs of the environment under consideration and makes use of existing heuristic rules for arriving at sequencing decisions. Although in no sense optimal, the algorithm can provide good feasible solutions to a previously not formally analysed problem. After being incorporated into a systematic computer-aided scheduling procedure, the algorithm has been actually implemented, demonstrating considerable improvements over previous fabrication shop scheduling practice.     

Small Firm Marketing: A Comparison of Eastern and Western Marketing Practices

This article reports on the analysis of 110 published, undisguised stories about Chinese owner-managers in Hong Kong and determines whether Chinese small firms exhibit the same marketing activities and behavior as their Western counterparts. Content analysis suggests that Chinese small firms are production oriented, whereas small American firms are customer oriented. Chinese small firms are also found to have limited marketing expenditure while their owner-managers have limited marketing expertise. The findings also suggest that Chinese small firms do little strategic marketing planning. One possible reason for this may be that Chinese owner-managers apply innovative tactics and change their marketing planning techniques to suit the characteristics of their firms and their particular circumstances. Thus, it is not unusual to find that Chinese small firm marketing can be described as entrepreneurial and their marketing planning activities as unstructured.     

The transition from products to solutions: External business model fit and dynamics

This article provides empirical evidence and contributes to theory building concerning business model fit and dynamics in the area of solutions business. Business models are seen in this context as going beyond considerations such as offerings and internal processes or even relationships, and as including network and market considerations. Indeed the paper highlights the fact that a business model is not firm-focused, nor dyad-focused, but rather network-, and even market-focused, demonstrating that a business model is not static, but dynamic. Manufacturer and customer continuously shift form and content of their respective business models to adapt both to the needs of the counterpart and to market context. A qualitative case study approach is adopted, with subsequent content analysis. The case study relates to the aerospace industry with focus on a complex engineering firm, one of the largest aircraft manufacturers in the world, its customer — a national airline — and their network partners of various kinds. The data were collected through multiple face-to-face interviews with managers in both companies, as a part and parcel of a network of actors that influences and is influenced by the supplier–buyer relationship. Relationships over time between these firms and network partners are described, highlighting the interplay of products and services related to the provision of solutions. Findings highlight the dynamic nature of business models over the relationship lifecycle between supplier and customer in a complex engineering environment, and the need for reciprocal adjustment of models.     

Short-term decision support system for maintenance task prioritization

Maintenance operations have a direct influence on production performance in manufacturing systems. Short-term production analysis is imperative to enable manufacturing operations to optimally respond to dynamic changes in the system behavior. However, most of the conventional decision support systems for production and maintenance focus on long-term statistic analysis, which is usually not applicable to a short-term period. Maintenance task prioritization is crucial and important for short-term analysis to reduce unnecessary or improper maintenance activities, especially when availability of maintenance resources is limited. The existing methods for maintenance priority assignment are often through heuristic methods or experience, which could cause unscheduled downtime and production losses. In this paper, a short-term decision support system for maintenance task prioritization based on the system operating conditions is introduced. The impact factor for priority assignment is obtained theoretically. A case study based on the simulation of an automotive assembly line illustrates that the proposed short-term system improves the system performance with a lower cost than the long-term method.     

Improving the quality of corporate technical planning: dynamic analogues of QFD

This paper proposes a forecasting methodology based on a combination of QFD and S-curve analysis, In process industries there is a need to strengthen the linkages between process attributes, product attributes, and customer requirements. Industry planning processes accept the relationship between technological positioning, project portfolios, and market life cycles, but specific methods are seldom discussed.
QFD(Quality Function Deployment) can be used to translate customer requirements into product specifications and in turn to specify the process capabilities required to meet those customer requirements. The paper recommends that managements use analogues of QFD adapted to the need for dynamic changes in process capability.
This approach would focus on the interaction between key variables of customer requirements and the technological capabilities of the firm and its competitors, at present and in the future. Historical industry-wide capabilities would be projected through S-curve analysis, while customer requirements can be related to these capabilities through information from QFD studies focused on future customer requirements. Because of their potential complexity, these analyses should deal with only a very limited number of interacting attributes and special care should be given to the management of their implementation.     

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.     

Multi-agent-based transport planning in the newspaper industry

In many cases of today's planning tasks, the synchronization of production and distribution is becoming increasingly important in order to minimize costs and to maximize customer satisfaction. This is especially the case if transport schedules are closely connected to production schedules, as it is in the newspaper industry—where perishable goods are distributed immediately after production. In order to achieve the above mentioned competing objectives, a special kind of vehicle routing problem, the vehicle routing problem with time windows and cluster-dependent tour starts (VRPTWCD), has to be solved. Moreover, the varying print and post-processing schedules due to unknown editorial deadlines lead to the need for a dynamic online control of the newspaper production and distribution process. In this contribution, the outlined dynamic transport problem is solved online under consideration of unforeseen changes in production schedules. The solution concept is based on a multi-agent system consisting of, amongst others, several Edition and Vehicle Agents. This system is exemplarily applied to a real life application case of one of the largest German newspaper companies. It is shown that a static (centralized) optimization of the underlying problem would even lead to worse results in comparison to the current situation and that the appliance of the multi-agent system is suitable in the newspaper industry.     

A revised EMQ/JIT production-run model: An examination of inventory and production costs

Production-runs are an important cost minimizing scheduling and production planning activity. Commonly used classic lot-sizing models (i.e., economic manufacturing quantity or EMQ models) do not reflect current just-in-time (JIT) lot-sizing cost realities. The purpose of this paper is to present a cost comparison of the classic EMQ model and a revised EMQ/JIT model to show efficacy of a more cost inclusive model.     

Analysis of a general Markovian two-stage continuous-flow production system with a finite buffer

Fluid flow models are used in the performance evaluation of production, computer, and telecommunication systems. In order to develop a methodology to analyze general Markovian continuous material flow production systems with two processing stages with an intermediate finite buffer, a general single-buffer fluid flow system is modelled as a continuous time, continuous-discrete state space stochastic process and the steady-state distribution is determined. Various performance measures such as the production rate and the expected buffer level are determined from the steady-state distributions. The flexibility of this methodology allows analysis of a wide range of models by specifying only the transition rates and the flow rates associated with the discrete states of each stage. Therefore, the method is proposed as a tool for performance evaluation of general Markovian continuous-flow systems with a finite buffer. The solution methodology is illustrated by analyzing a production system where each machine has multiple up and down states associated with their quality characteristics.     

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.