Impact of taxation on international transfer pricing and offshoring decisions

A multinational company may move production to a foreign country to take advantage of low manufacturing cost, and/or experience tax savings. Transfer prices play an important and strategic role on income shifting by multinational companies. In this paper, we construct a framework for optimal decision making in global supply chains with uncertain and price-dependent demand, propose methods to improve global supply chain parties’ performance, and explore schemes to integrate global supply chains. The optimal pricing and offshoring decisions are investigated for different situations where the low foreign production cost and low foreign tax rate exist or only one of them is available. The case of low foreign tax rate without the advantage of low foreign production cost provides the most interesting findings that partial offshoring dominates when a certain threshold is met. In addition, the double marginalization is examined in decentralized global supply chains similar to the mechanism in newsvendor problems. Due to the existence of the tax jurisdiction, the double marginalization cannot be completely eliminated by coordinating schemes. Finally, the traditional buy back contract is found to be unable to coordinate global supply chains, while a modified sales sharing contract can improve the performance of the global supply chain.     

Possibilistic linear-programming approach for supply chain networking decisions

Supply chain networking decisions are very important for the medium- and long-term planning success of manufacturing companies. The inputs to supply chain planning models are subject to environmental and system uncertainties. In this paper, a fuzzy set theory-based model is proposed to deal with those uncertainties. For this purpose, a possibilistic linear programming (PLP) model is used to make strategic resource-planning decisions using fuzzy demand forecasts and fuzzy yield rates as well as other inputs such as costs and capacities. The objective of the proposed PLP is to maximize the total profit of the enterprise. The model is applied to Mercedes–Benz Türk, one of the largest bus-manufacturing companies in the world, and conclusions and suggestions for further research are provided.     

Multi-resource investment strategies: Operational hedging under demand uncertainty

Consider a firm that markets multiple products, each manufactured using several resources representing various types of capital and labor, and a linear production technology. The firm faces uncertain product demand and has the option to dynamically readjust its resource investment levels, thereby changing the capacities of its linear manufacturing process. The cost to adjust a resource level either up or down is assumed to be linear. The model developed here explicitly incorporates both capacity investment decisions and production decisions, and is general enough to include reversible and irreversible investment. The product demand vectors for successive periods are assumed to be independent and identically distributed. The optimal investment strategy is determined with a multi-dimensional newsvendor model using demand distributions, a technology matrix, prices (product contribution margins), and marginal investment costs. Our analysis highlights an important conceptual distinction between deterministic and stochastic environments: the optimal investment strategy in our stochastic model typically involves some degree of capacity imbalance which can never be optimal when demand is known.     

Capacity decisions for high-tech products with obsolescence

Modern high-tech products experience rapid obsolescence. Capacity investments must be recouped during the brief product lifecycle, during which prices fall continuously. We employ a multiplicative demand model that incorporates price declines due to both market heterogeneity and product obsolescence, and study a monopolistic firm’s capacity decision. We investigate profit concavity, and characterize the structure of the optimal capacity solution. Moreover, for products with negligible variable costs, we identify two distinct strategies for capacity choice demarcated by an obsolescence rate threshold that relates both to market factors and capacity costs. Finally, we empirically test the demand model by analyzing shipping and pricing data from the PC microprocessor market.     

Robust strategies for natural gas procurement

In order to serve their customers, natural gas local distribution companies (LDCs) can select from a variety of financial and non-financial contracts. The present paper is concerned with the choice of an appropriate portfolio of natural gas purchases that would allow a LDC to satisfy its demand with a minimum tradeoff between cost and risk, while taking into account risk associated with modeling error. We propose two types of strategies for natural gas procurement. Dynamic strategies model the procurement problem as a mean-risk stochastic program with various risk measures. Naive strategies hedge a fixed fraction of winter demand. The hedge is allocated equally between storage, futures and options. We propose a simulation framework to evaluate the proposed strategies and show that: (i) when the appropriate model for spot prices and its derivatives is used, dynamic strategies provide cheaper gas with low risk compared to naive strategies. (ii) In the presence of a modeling error, dynamic strategies are unable to control the variance of the procurement cost though they provide cheaper cost on average. Based on these results, we define robust strategies as convex combinations of dynamic and naive strategies. The weight of each strategy represents the fraction of demand to be satisfied following this strategy. A mean–variance problem is then solved to obtain optimal weights and construct an efficient frontier of robust strategies that take advantage of the diversification effect.     

Price markdown scheme in a multi-echelon supply chain in a high-tech industry

This paper studies the price markdown scheme in a supply chain that consists of a supplier, a contract manufacturer (CM), and a buyer (retailer). The buyer subcontracts the production of the final product to the CM. The CM buys the components from the supplier and charges the buyer a service fee for the final product produced. The price markdown is made possible by the supplier with the development of new manufacturing technologies that reduce the production cost for the sourced component. Consequently, the buyer adjusts the retail price in order to possibly stimulate stronger demand that may benefit both the supplier and the buyer. Under this scenario, we identify the optimal discount pricing strategies, capacity reservation, and the stocking policies for the supplier and the buyer. We also investigate the optimal inventory decision for the CM to cope with the price discount by considering both demand and delivery uncertainties. Our results suggest that higher production cost accelerates the effects of higher price sensitivity on lowering the optimal capacity and stocking policies in the supply chain. The effect of mean demand error on the optimal prices is relatively marginal compared with that from price sensitivity. We also found that increasing the standard deviation of the random demand does not necessarily increase the stocking level as one would predict. The results show that delivery uncertainty plays an important role in the inventory carried beyond the price break. We discuss potential extensions for future research.     

Optimal timing decisions for the introduction of new technologies

High technology industries, such as the communications industry, are characterized by frequent development of new technologies. These new technologies are often available before the capacities of existing facilities that use an old technology are exhausted. Whenever a new technology facility is introduced, a fixed set-up cost is generally incurred; however, the annual operating costs are often reduced. The optimal timing of the introduction of new facilities is therefore of interest.In this paper, we examine such timing decisions. The study was motivated by an application involving electronic plug-in units that enhance the operation of communication facilities. First, we develop optimal timing decisions for linearly growing demand. The analysis is then extended to nonlinear demand. For linear demand, one of two decisions is optimal: Either introduce the new technology immediately, or as late as possible. However, for nonlinear demand, these decisions may be nonoptimal.     

Pricing decisions for substitutable products with a common retailer in fuzzy environments

The pricing problem of substitutable products in a fuzzy supply chain is analyzed by using game theory in this paper. There are two substitutable products produced by two competitive manufacturers respectively and then sold by one common retailer to the consumers. Both the manufacturing cost and the customer demand for each product are characterized as fuzzy variables. How the two manufacturers and the common retailer make their own pricing decisions about wholesale prices and retail prices are explored under four different scenarios, and the corresponding expected value models are developed in this paper. Finally, a numerical example is given to illustrate the effectiveness of the proposed supply chain models.     

Capacitated procurement planning with price-sensitive demand and general concave-revenue functions

This paper develops effective solution methods for discrete-time, finite-horizon procurement planning problems with economies of scale in procurement, price-sensitive demand, and time-invariant procurement capacities. Our models consider general concave-revenue functions in each time period, and seek to maximize total revenue less procurement and inventory holding costs. We consider the case in which prices may vary dynamically, as well the important practical case in which a constant price is required during the planning horizon. Under mild conditions on the revenue function properties, we provide polynomial-time solution methods for this problem class. The structural properties of optimal solutions that lead to efficient solution methods also serve to sharpen intuition regarding optimal demand management strategies in complex planning situations.     

Robust empty container repositioning considering foldable containers

Because of the extreme imbalance in intercontinental trade, the repositioning of empty containers creates a significant problem for shipping companies. There are many efforts to reduce the cost of repositioning empty containers, one of which is a foldable container. This paper proposes a robust formulation for the empty container repositioning problem considering foldable containers under demand uncertainty. The robust formulation can be used as a tractable approximation of a multistage stochastic programming formulation which is computationally intractable. Moreover, the robust formulation requires only limited information about the distribution of demand to replicate real-world situations. Computational results show that the proposed formulation performs well in terms of operating costs and there exists a significant cost-saving effect when foldable containers are used in maritime transportation.     

Forecasting demand variation when there are stockouts

This paper addresses the common problem of forecasting demand when there are a large number of stockouts. The well-known single period stochastic inventory (or ‘newsboy’) problem provides the optimum, single period, stocking level for a product subject to stochastic demand. There are many situations where repetitive ‘newsboy’ solutions are implemented to guide stocking of repeat, but related, products, such as newspapers, magazines, or perishable groceries. Implementation of the ‘newsboy’ solution requires forecasts of the distribution of demand, although there are many plausible cost parameters that lead to optimum stocking policies where there is a high probability of a stockout. The company is, therefore, faced with the problem of attempting to forecast demand when a high percentage of the available sales data reflects the stock available for sale, rather than the true demand.A procedure has been developed¹ to improve estimates of the mean and variance of the distribution of demand when there are stockouts, but this procedure fails when the percentage of stockouts increases above 50%. A modified stockout adjustment procedure is presented in this paper, and it is shown that use of this new procedure can lead to greatly improved estimates of demand parameters, and greatly improved profitability, when there are a high percentage of stockouts.     

Demand selection decisions for a multi-echelon inventory distribution system

In this paper, we study an integrated demand selection and multi-echelon inventory control problem that generalizes the classical deterministic single distribution centre (DC) multi-retailer model by incorporating demand selection decisions. In addition to the ordering and holding cost components, a concave operating cost of the DC and a capacity on the total market demand served are also considered. For given revenue and cost parameters, the problem is to determine which sets of demand to fulfill and which multi-echelon inventory control policy to implement so as to maximize the net profit. We show that the problem can be formulated as a nonlinear discrete optimization model. We analyse the structural properties of the model and, based on these, outline an approach to solve the model efficiently. We also present some interesting managerial insights obtained from the numerical experiments.     

Joint pricing and inventory replenishment decisions with returns and expediting

We study a single-item periodic-review model for the joint pricing and inventory replenishment problem with returns and expediting. Demand in consecutive periods are independent random variables and their distributions are price sensitive. At the end of each period, after the demand is realized, a buyer can return excess stocks to a supplier. Or, if there are stockouts, the buyer can place an expediting order at the supplier to reduce the amount of shortage. Unfilled demands are fully backlogged. We characterize the optimal dynamic policy that determines the pricing, inventory replenishment, and adjustment decisions in each period so that the total expected discounted profit is maximized. For a very general stochastic demand function, we can show that the optimal replenishment policy is a modified base-stock policy, the optimal pricing policy is a modified base-stock-list-price policy, and the optimal policy for inventory adjustment follows a dual-threshold policy. We further study the operational effect of returns and expediting. Analytical and numerical results demonstrate that returns and expediting lead to a significant profit increase in a number of situations, including limited supply capacity, sufficient flexibility of the expediting order, high demand uncertainty, and a price-sensitive market.     

Supporting offshoring and nearshoring decisions for mass customization manufacturing processes

Offshore countries attract companies for a possible relocation of production processes through extremely low worker wages. Particularly, mass production processes seem to be highly appropriate for a relocation. However, while the impact of wage reductions can be directly estimated, an appropriate determination of additional cost consequences proves to be a complex task. For instance, on account of lower education standards and higher fluctuation rates, the average worker skills in offshore countries are often significantly lower than in high-wage countries like the United States. In order to appropriately analyze and evaluate the resulting tradeoff between wages and worker skills for mass customization manufacturing systems, this paper introduces a new approach that comprises a detailed mixed-model assembly line balancing. This approach provides a direct comparison of the estimated variable manufacturing costs by generating a country-dependent line layout for all competing locations. In order to validate the efficiency of the balancing approach and, in particular, derive general implications for management, several test series with various country configurations were executed. First, by attaining improvement rates of up to 40%, the capability of a generated Tabu Search procedure for finding appropriate line layouts was proven. Second, as the main result, the complexity of the variant program was identified as a crucial factor for offshoring decisions since it substantially affects variable manufacturing costs. This was particularly proven for countries with low worker skills, which attract offshoring/nearshoring through exceptionally low labor costs. Hence, companies that consider outsourcing production systems to those countries are strongly hold to examine these decisive effects thoroughly. Regarding this, offshoring becomes very promising for manufacturing processes characterized by a moderate variant complexity level.     

Aggregate production planning for process industries under oligopolistic competition

We consider a competitive version of the traditional aggregate production planning model with capacity constraints. In the general case, multiple products are produced by a few competing producers (oligopoly) with limited capacities. Production quantities, prices and consequently profits depend on production and allocation decisions of each producer. In addition, there is competition for the raw material whose supplies are limited, and where prices reflect these limitations. Such situations have recently occurred in several process industry settings including petro-refining, petrochemicals, basic chemicals, cement, fertilizers, pharmaceuticals, rubber, paper, food processing and metals. We use a successive “Bertrand–Cournot” framework to address this problem and to determine optimal production quantities, prices and profits at the producers and at the raw material supplier. Our analysis allows a new way to understand and evaluate the marginal value of additional capacity when there is competition for the market and raw materials.     

Average shadow price and equilibrium price: A case study of tradable pollution permit markets

Shadow prices indicate implicit values of limited resources at the margin and provide important information in decision making for resource management. In continuous economic models, shadow prices associated with demand-supply balance constraints represent consumers’ willingness to pay and producers’ marginal cost, hence they correspond to market equilibrium prices. As well known, however, marginal analysis fails in the case of discrete optimization, such as mixed integer programming. An alternative concept has been introduced in the literature to measure the value of an extra unit of a limited resource in such cases. This concept is based on average rather than marginal values, thus called the average shadow price, and interpreted in the same way as conventional shadow prices. Whether average shadow prices in a discrete economic model can serve as market equilibrium prices has not been addressed in the related literature. The present paper addresses this issue in an empirical setting. Using a tradable pollution permit market as an example, where firms’ YES/NO type technology adoption decisions are represented by binary variables, we show that the average shadow price of tradable permits can be interpreted as the equilibrium price only when certain conditions related to the cost structure and emission levels hold. On the other hand, we show that an iterative procedure based on individual firms’ cost minimizing behavior presents a better approach for finding a price that can eliminate or reduce the gap between demand and supply of permits in the market.     

Leader–Follower Dynamic Game of New Product Diffusion

The objective of this paper is to study optimal pricing strategies in a duopoly, under an asymmetric information structure, where the appropriate solution concept is the feedback Stackelberg equilibrium. In order to take into account effects such as imitation (e.g., word of mouth) and saturation, the demand (state equation) is assumed to depend on past cumulative sales, market potential, and both players' prices. We assume also that the unit production cost decreases with cumulative production (learning effects). Each player maximizes his total discounted profit over the planning horizon.The problem is formulated as a two-player discrete-time finite-horizon game. Existence results are first obtained under rather mild conditions. Since the solution of this problem is intractable by analytical methods, we use a numerical approach. Thus, we design a numerical algorithm for the computation of feedback Stackelberg equilibria and use it to obtain strategies in various representative cases. The numerical results presented are intented to give some insights into the optimal pricing strategies in the context of an asymmetrical feedback information structure.     

Global plant capacity and product allocation with pricing decisions

Trade-offs in global manufacturing decisions involve markets, resource costs, trade-barriers, currency exchange rates, joint ventures and investments. We develop a model that optimizes plant investment decisions, while ensuring that the plant investment overhead is optimally absorbed by products produced from that plant. The model also, simultaneously, determines prices by products and countries. The special structure of the model is exploited to construct a fast solution procedure. The model is used to study the implications of labor cost, transportation cost, demand, and import tariff on production quantities, investment, and overhead absorption pattern. Implications of changes in other global parameters such as local-content rule, local taxes, size of the market in a country, and long-term exchange rates are also studied.     

Optimizing inventory decisions in a two-level supply chain with order quantity constraints

A fundamental assumption in traditional inventory models is that all of the ordered items are of perfect quality. A two-level supply chain is considered consists of one retailer and a collection of suppliers that operate within a finite planning horizon, including multiple periods, and a model is formulated that simultaneously determines both supplier selection and inventory allocation problems in the supply chain. It is supposed that the ordered products dependent on the suppliers include a certain percentage of imperfect quality products and have different prices. In this paper, we study the impact of the retailer’s financial constraint. On the other hand, suppliers have restricted capacities and set minimum order quantity (MOQ) policy for the retailer’s order amount happened in each period. So, the problem is modeled as a mixed integer nonlinear programming. The purpose of this model is to maximize the total profit. The nutrients, fishery and fruitage industries give good examples for the proposed model. A numerical example is presented to indicate the efficiency of the proposed model. Considering the complexity of the model, a genetic algorithm (GA) is presented to solve the model. We demonstrate analytically that the proposed genetic algorithm is suitable in the feasible situations.     

Cyclic behavior in dynamic investment decisions for deregulated energy markets

In the first years after the deregulation of the electricity industry, investment into new generation capacity has not taken place on a large scale in any central european country. Recent increases in prices indicate that investment could be very profitable. However, the fear is that the need for new capacity can be overestimated and that could lead to a decrease in prices and profits and consequently to a reduction/stop of new investments. The aim of this paper is to model and analyze factors that influence the stability of electricity prices. The electricity market is modeled using a Cournot game and the stability of electricity prices is analyzed by simulations. The research was supported by the grant 1/3001/06 of the Grant Agency of Slovak Republic (VEGA) and grant VVGS 36/2006.