Optimal decision making for online referral marketing

Widely available web 2.0 technologies not only bring rich and interactive user experiences, but also easily help users advertise products or services on their own blogs and social network webpages. Online referral marketing, for example, is a business practice that rewards customers who successfully refer other customers to a website or upon completion of a sale usually via their own social contacts. The referral rewards come in different forms such as shopping vouchers, redeemable points, discounts, prizes, cash payments, etc. We develop an analytical model to evaluate the business potential of incorporating an online referral marketing program into the firm's product selling strategies. Under different demand dynamics, we investigate the optimal decision making including the pricing and referral strategies to maximize the seller's profitability. We find that, under simple decision making environment such as fixed product price and myopic strategy, different demand dynamics yield the same prediction of the referral payment, which turns out to be a static policy. However, under complex market situations, both the optimal product pricing and referral offering critically depend on the demand side dynamics. Under the nonlinear demand dynamics, the referral payment is an all-or-nothing decision throughout the product selling horizon. In contrast, under the linear demand assumption, the referral payment can be partially offered in initial phase of the product introduction. We further offer some managerial insights to guide practical implementation of the online referral marketing strategy.     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

Pricing and production lot-size/scheduling with finite capacity for a deteriorating item over a finite horizon

Although the lately evolved manufacturing technologies such as enterprise resource planning (ERP) provide a unified platform for managing and integrating core business processes within a firm, the decision-making between marketing and production planning still remains rather disjoint. It is due in large parts to the inherent weaknesses of ERP such as the fixed and static parameter settings and uncapacitated assumption. To rectify these drawbacks, we propose a decision model that solves optimally the production lot-size/scheduling problem taking into account the dynamic aspects of customer's demand as well as the restriction of finite capacity in a plant. More specifically, we consider a single product that is subject to continuous decay, faces a price-dependent and time-varying demand, and time-varying deteriorating rate, production rate, and variable production cost, with the objective of maximizing the profit stream over multi-period planning horizon. We propose both coordinated and decentralized decision-making policies that drive the solution of the multivariate maximization problem. Both policies are formulated as dynamic programming models and solved by numerical search techniques. In our numerical experiments, the solution procedure is demonstrated, comparative study is conducted, and sensitivity analysis is carried out with respect to major parameters. The numerical result shows that the solution generated by the coordinated policy outperforms that by the decentralized policy in maximizing net profit and many other quantifiable measures such as minimizing inventory investment and storage capacity.Scope and purposeWe consider a manufacturing firm who produces and sells a single product that is subjected to continuous decay over a lifetime, faces a price-dependent and time-varying demand function, shortages are allowed and a completely backlogged, and has the objective of determining price and production lot-size/scheduling so as to maximize the total profit stream over multi-period planning horizon. We develop a tactical-level decision model that solves the production scheduling problem taking into account the dynamic nature of customer's demand which is partially controllable through pricing schemes. As analogous to the sales and operations planning, the proposed scheme can be used as a coordination center of the APS system within a generic enterprise resource planning framework which integrates and coordinates distinct functions within a firm.This paper differs from the existing works in several ways. First, we propose a dynamic version of the joint pricing and lot-size/scheduling problem taking into account the capacitated constraint. Second, several key factors being considered in the model, such as the demand rate, deteriorating rate, production rate, and variable production cost are assumed time-varying that reflect the dynamic nature of the market and the learning effect of the production system. A third difference between the past research and ours is that the price can be adjusted upward or downward in our model, making the proposed pricing policy more responsive to the structural change in demand or supply.     

Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy

New product development (NPD) is becoming an important competitive advantage in the marketing strategies of current businesses. Developing a new product will incur fixed and variable costs, which then determine the product prices. Although this is a fundamental issue of marketing theory and practice, only a few papers on marketing models deal with price levels. The objective of this paper is proposing a model based on fuzzy analytical hierarchy process (AHP) and multi-segment goal programming (MSGP) to help decision makers to select the best pricing strategy for NPD. A case study of NPD under market selection strategy in multiple segment pricing levels for a Taiwan-based Watch Company is presented to illustrate the proposed methodology. The proposed method will guide the product development team to select the best market strategy by taking into account the price level and product/market segmentation.     

An economic production lot size model with increasing demand, shortages and partial backlogging

In this paper, an economic production quantity model is developed for a production–inventory system where the demand rate increases with time, the production rate is finite and adjustable in each cycle over an infinite planning horizon and shortages are permitted. The cost of adjusting the production rate depends linearly on the magnitude of the change in the production rate. During the stock‐out period, a known fraction of the unsatisfied demands is backordered while the remaining fraction is lost. The model is formulated taking the demand rate as a general increasing function of time and the optimal production policy is obtained for the special case of a linearly increasing demand rate. The proposed model is also shown to be suitable for a prescribed time horizon. A procedure to find approximately the minimum total cost of the system over a finite time horizon is suggested. A numerical example is taken to illustrate the solution procedure of the developed model.     

Dynamic optimisation of price,warranty length and production rate

This study investigates a marketing and production problem that uses price, warranty length and production rate as simultaneous dynamic decision variables. Furthermore, this study was conducted under a policy of free replacement of defective items; and under conditions where demand was dynamic and dependent on price, warranty and cumulative sales. A continuous profit maximisation model was formulated, which first considers the expected warranty cost per item. Then, it considers the steps for dynamic optimisation, which eventually derive the optimal price, warranty length and production rate. Discretisation was then applied to the profit maximisation model and a digital computer was used to identify the optimal control paths, obtaining a finite solution that is a set of real numbers for practical application. A model-driven Decision Support System is finally established, which provides a graphical user interface for overcoming the complexity of the analytical process. Subsequently, the proposed system was tested and the analytical solution was verified using several demand functions for additive lifetime distributions, thereby demonstrating the system's effectiveness.     

Production risk management system with demand probability distribution

As market globalization has changed the nature of their business, the types of products are multiplied by customizing in order to seize each market segment. Moreover, model lifecycle have been shortened by releasing new models for stimulating customers. That makes it difficult to control the proper timing and volume of the product supply. Manufactures face the overstock risks and stock shortage risks. Each product needs its customized supply control to keep profitability. This study proposes a reproduction decision support system that measures demand risks through a sales forecasting method. This system gives manufactures the proper volume and timing guidance for daily reproduction of products. The system is applied to the case of a Japanese publisher. A three-month operational test of the system proved it able to provide the optimized supply volume options based on manufacturer’s decision strategies. The ratio of surplus stock decreased from 40% to 34% as a result.     

Application of particle swarm optimisation for solving deteriorating inventory model with fluctuating demand and controllable deterioration rate

In most of the inventory models in the literature, the deterioration rate of goods is viewed as an exogenous variable, which is not subject to control. In the real market, the retailer can reduce the deterioration rate of product by making effective capital investment in storehouse equipments. In this study, we formulate a deteriorating inventory model with time-varying demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. The objective is to find the optimal replenishment and preservation technology investment strategies while minimising the total cost over the planning horizon. For any given feasible replenishment scheme, we first prove that the optimal preservation technology investment strategy not only exists but is also unique. Then, a particle swarm optimisation is coded and used to solve the nonlinear programming problem by employing the properties derived from this article. Some numerical examples are used to illustrate the features of the proposed model.     

Using a strategy-aligned fuzzy competitive analysis approach for market segment evaluation and selection

This study applies Five Forces Analysis to evaluate and select market segments for international business using a strategy-aligned fuzzy approach. An illustration segment evaluation procedure is used to demonstrate that our procedure is an effective quantification approach for integrating five forces, generic strategies and marketing information in a group decision-making process. The final decision-maker (DM) synthesizes the total crisp scores of individual alternatives by choosing judgmental coefficients λ based on individual attitude towards core business competitiveness and market risks to accommodate differences among market segments to the specific environment with a better understanding of the decision problem and individual decision-making behavior. In the illustration presented here, the final solution is then obtained by identifying the best market segment for further development and negotiation.     

价格波动下的生产-库存控制研究

研究原材料价格波动下多级生产-库存系统的控制问题.所有的原材料价格、半成品加工成本、成品的生产成本、库存费用率和产品的需求率都随时间变动,为此,分析了最优采购、加工、生产决策的必要和充分条件,得到了在某些假设条件下的最优生产-库存策略为JIT(Just-in-time)采购、加工、生产策略,或者为在最开始阶段以最大能力进行采购、加工、生产活动的Bang-Bang策略.     

Developing a marketing decision model using a knowledge-based system

This paper describes using a knowledge-based system for developing a marketing decision model. The approach used in this study uses a decision table as a knowledge engineering tool. The decision table is used as a means of representing a set of decision rules to construct a developed marketing decision model. To support the modeling process, Prologa, an existing decision table engineering workbench, is used. The developed marketing decision model is used to determine the entrance time of a new product into market by utilizing knowledge-based systems. Presentation of a new product to the market at the best time will provide an advantage to competing companies and will increase their market share.     

To innovate or not to innovate?

In this paper, we analyze the evolution of output decisions of adaptive firms in an environment of oligopolistic competition. The firm might either choose to produce one of several existing product variants or try to establish a new product variant on the market. The demand for each individual product variant is subject to a life cycle, but aggregate demand for product variants is constant over time. Every period each firm has to decide whether to produce the product again, introduce a new product variant itself (which generates an initial advantage on that market), or follow another firm and change to the production of an already established product. Different firms have heterogeneous abilities to develop products and imitate existing designs; therefore, the effects of the decision whether to imitate existing designs or to innovate differ between firms. We examine the evolution of behavior in this market using an agent-based simulation model. The firms are endowed with simple rules to estimate market potentials and market founding potentials of all firms, including themselves, and make their decisions using a stochastic learning rule. Furthermore, the characteristics of the firms change dynamically due to “learning by doing” effects. The main questions discussed are how the success and the optimal strategy of a firm depend on the interplay between characteristics of the industry and properties of the firm     

Modeling and analysis for multi-period, multi-product and multi-resource production scheduling

This study presents a framework for solving the multi-period, multi-product and multi-resource production-scheduling (M³PS) problem. Practically, the main concern for an M³PS problem is how to satisfy two management policies: (1) each product is manufactured in a continuous manner so that once the product is on a production line, it will complete its production procedure without interruption, and (2) the number of the product's types is limited during one period. By defining the decision variables and taking into account the machine's capacity and the customers' demand, a mixed integer programming (MIP) Model is formulated. To solve this MIP problem, a two-phase approach is proposed. In phase 1, the search space of the MIP Model is transformed into a preliminary pattern by a heuristic mining algorithm so that a hyper assignment problem can be formed as a reference model to be solved. In phase 2, a stochastic global optimization procedure that incorporates a genetic algorithm with neighborhood search techniques is designed to obtain the optimal solution. A numerical experiment is presented with an illustration, and it shows that the proposed model is adequate to cope with complicate scheduling problems.     

Application of optimal control theory to product pricing and warranty with free replacement under the influence of basic lifetime distributions

This study investigates optimal policies for determining price and warranty length when free replacement of defective items is the business policy and the demand is dependent on price, warranty, and cumulative sales. We apply optimal control theory to a profit-maximization model that takes into consideration the expected warranty cost per item. The maximization of market values is based on a pre-determined life cycle and spans across both expansion and saturation phases of the market. We further investigate the optimal policies for a number of basic lifetime distributions, when the demand function is under the setting of separable and logistic growth. We found that as the growth of a product enters different phases of the market, optimal decision policies are characterized by simultaneously increasing or reducing both price and warranty length.     

A note on the optimal pricing and production decisions with price‐driven substitution

Recently, Kim and Bell ( 2011 ) developed a revenue managemnent pricing model with price‐driven substitution. The authors considered production decisions under unlimited production capacity and investigated the impact of price‐driven substitution on a firm's pricing and production decisions. The authors modeled the consumer demands for each market segment as linear additive demand function based on exogenous variables, where demand substitution occurred as a function of price differences between the two products. In this article, we extend this work to examine the impact of a production capacity constraint on the firm's joint pricing and inventory decisions. Based on this extended model, we investigate the impact of price‐driven substitution on a firm's pricing and production decisions where there is a limit on total capacity. We show how revenue managers should adjust prices and production levels to take into account price‐driven substitution under a capacity constraint setting. Both deterministic and stochastic models are developed, and the impact of price‐driven substitution and a capacity constraint on the optimal prices, production levels, and revenues is illustrated.     

Multiproduct production/inventory control under random demands

Studies the optimal production/inventory control policy for a single machine multiproduct production system. The machine produces to fill the end-product inventory stock and the demand is satisfied from the inventory when available; unsatisfied demand is backlogged until the product becomes available as the result of production. For each product, the demand follows a Poisson process and the unit processing time is known. When the machine switches production from one product to another, it incurs a set-up time and a set-up cost. The relevant costs include the set-up cost, a cost per unit time while the machine is running, and linear costs for inventory and backlogging. This problem is modeled as a semi-Markov decision process using the criterion of minimizing expected total cost with discounting over an infinite horizon. Procedures for computing near-optimal policies and their error bounds are developed. The error bound given by the authors' procedure is shown to be much tighter than the one given by the “norm-based” approach. Computational test results are presented to show the structure of the near-optimal policy and how its accuracy is affected by the system characteristics such as capacity utilization and set-up time     

Optimal pricing, lot-sizing and marketing planning in a capacitated and imperfect production system

In the classical economic production quantity (EPQ) problem demand is considered to be known in advance. However, in the real-world, demand of a product is a function of factors such as product’s price, its quality, and marketing expenditures for promoting the product. Quality level of the product and specifications of the adopted manufacturing process also affect the unit product’s cost. Therefore, in this paper we consider a profit maximizing firm who wants to jointly determine the optimal lot-sizing, pricing, and marketing decisions along with manufacturing requirements in terms of flexibility and reliability of the process. Geometric programming (GP) technique is proposed to address the resulting nonlinear optimization problem. Using recent advances in optimization techniques we are able to optimally solve the developed, highly nonlinear, mathematical model. Finally, using numerical examples, we illustrate the solution approach and analyze the solution under different conditions.     

Optimal software pricing in the presence of piracy and word-of-mouth effect

We develop an analytical model that embeds empirical findings on software diffusion to examine optimal pricing strategies for a spreadsheet software product under coalescing effects of piracy and word-of-mouth through its entire life cycle. We find that the demand of the innovators has the most significant impact on the firm's pricing decision. Our research recommends market skimming pricing strategy if innovators' demand is high and the market penetration pricing strategy is preferred otherwise. Surprisingly, the increase of conversion rate of imitators to buyers never significantly alters the pricing strategy pre-determined by the demand of innovators. Most interestingly, the optimal profit from instituting a two prices policy for a software product with five years lifespan outperforms that from a one price policy by no more than 4%, a finding that corroborates the common one price policy observed in reality.