Keep or return? Managing ordering and return policies in start-up companies

Start-up companies are considered an important factor in the success of a nation’s economy. We are interested in the decisions for long-term survival of these firms when they have considerable cash restrictions. In this paper we analyse several inventory control models to manage inventory purchasing and return policies. The Markov decision models are formulated for both established companies that look at maximising average profit and start-up companies that look at maximising their long-term survival probability. We contrast both objectives, and present properties of the policies and the survival probabilities. We find that start-up companies may need to be riskier if the return price is very low, but there is a period where a start-up firm becomes more cautious than an established company and there is a point, as it accumulates capital, where it starts behaving as an established firm. We compare the various models and give conditions under which their policies are equivalent.     

Loans,ordering and shortage costs in start-ups: a dynamic stochastic decision approach

Start-up companies are a vital ingredient in the success of a globalised networked world economy. We believe that such companies are interested in maximising the chance of surviving in the long term. We present a Markov decision model to analyse survival probabilities of start-up manufacturing companies. Our model examines the implications of their operating decisions, in particular how their inventory strategy is influenced by purchasing, shortage, transportation and ordering costs, as well as loans to the firm. It is shown that although the start-up company should be more conservative in its component purchasing strategy than if it were a well-established company it should not be too conservative. Nor is its strategy monotone in the amount of capital available.     

How useful is commonality? Inventory and production decisions to maximize survival probability in start-ups

This paper deals with component commonality in start-up manufacturingfirms. We present a two-product Markov decision model that examinesthe implications of the inventory and production strategiesfor the survival probability of the firm. The advantage of usingcomponent commonality is studied for varying costs, demand correlationsand order replenishment lead times. Optimal policies are derived,and minimum stock levels for survival are obtained. Moreover,we state the conditions under which simplified production decisionscan be made. It is shown that commonality is not only usefulas a way of dealing with demand uncertainty, but that its increaseduse is preferred for strongly substitutable products, and shorterreplenishment lead times.     

Optimal control of a dual service rate M/M/1 production-inventory model

We analyse a dual-source, production-inventory model in which the processing times at a primary manufacturing resource and a second, contingent resource are exponentially distributed. We interpret the contingent source to be a subcontractor, although it could also be overtime production. We treat the inventory and contingent sourcing policies as decision variables in an analytical study and, additionally, allow the primary manufacturing capacity to be a decision variable in a subsequent numerical study. Our goal is to gain insight into the use of subcontracting as a contingent source of goods and whether it can fulfill real-world managers' expectations for improved performance. We prove that a stationary, non-randomised inventory and subcontracting policy is optimal for our M/M/1 dual-source model and, moreover, that a dual base-stock policy is optimal. We then derive an exact closed-form expression for one of the optimal base stocks, which to our knowledge is the first closed-form solution for a dual-source model. We use that closed-form result to advantage in a numerical study from which we gain insight into how optimal capacity, subcontracting, and inventory policies are set, and how effectively a contingent source can reduce total cost, capacity cost, and inventory cost. We find that (i) the contingent source can reduce total cost effectively even when contingent sourcing is expensive and (ii) contingent sourcing reduces capacity cost more effectively than it does inventory cost.     

Joint management of capacity and inventory in make-to-stock production systems with multi-class demand

We evaluate the benefits of coordinating capacity and inventory decisions in a make-to-stock production environment. We consider a firm that faces multi-class demand and has additional capacity options that are temporary and randomly available. We formulate the model as a Markov decision process (MDP) and prove that a solution to the optimal joint control problem exists. For several special cases we characterize the structure of the optimal policy. For the general case, however, we show that the optimal policy is state-dependent, and in many instances non-monotone and difficult to implement. Therefore, we consider three pragmatic heuristic policies and assess their performance. We show that the majority of the savings originate from the ability to dynamically adjust capacity, and that a simple heuristic that can adjust production capacity (based on workload fluctuation) but uses a static production/rationing policy can result in significant savings.     

Environmental safety stock: The impacts of regulatory and voluntary control policies on production planning,inventory control,and environmental performance

This paper analyzes the impacts of different pollution control policies on a firm’s decisions of production planning and inventory control. Based on a stochastic model with both demand and environmental uncertainties, we derive the optimal policies of production planning and inventory control under both regulatory and voluntary pollution control approaches, and investigate their operational and environmental effects. We establish that the conventional wisdom which suggests that reduction of environmental waste at the end of a production process also decreases the stock and throughput levels of a production system is not necessarily true. Rather, a regulatory environmental standard that limits the total amount of waste may induce the firm to raise its planned stock level, which would lead to a higher expected amount of environmental wastes before the standard is enforced as well as environmental risks at other stages of the production process. The additional planned stock level, which is termed “environmental safety stock,” can be reversed by using the voluntary control approach that provides the firm with the flexibility to occasionally exceed the environmental standard. We also conduct numerical experiments to analyze the effects of different values of model parameters under different control approaches. The analytical results provide new insights to the impacts of a firm’s production and inventory decisions on the natural environment as well as to the choices of pollution control approaches by decision makers in both the private and public sectors.     

A two-level hedging point policy for controlling a manufacturing system with time-delay,demand uncertainty and extra capacity

This paper focuses on the production control of a manufacturing system with time-delay, demand uncertainty and extra capacity. Time-delay is a typical feature of networked manufacturing systems (NMS), because an NMS is composed of many manufacturing systems with transportation channels among them and the transportation of materials needs time. Besides this, for a manufacturing system in an NMS, the uncertainty of the demand from its downstream manufacturing system is considered; and it is assumed that there exist two-levels of demand rates, i.e., the normal one and the higher one, and that the time between the switching of demand rates are exponentially distributed. To avoid the backlog of demands, it is also assumed that extra production capacity can be used when the work-in-process (WIP) cannot buffer the high-level demands rate. For such a manufacturing system with time-delay, demand uncertainty and extra capacity, the mathematical model for its production control problem is established, with the objective of minimizing the mean costs for WIP inventory and occupation of extra production capacity. To solve the problem, a two-level hedging point policy is proposed. By analyzing the probability distribution of system states, optimal values of the two hedging levels are obtained. Finally, numerical experiments are done to verify the effectiveness of the control policy and the optimality of the hedging levels.     

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 stationary policies in a 3-stage serial production-distribution logistic chain facing constant and continuous demand

In this paper, we study a 3-stage production-distribution system that faces constant and continuous demand. The system is made up of three storing sites: a production facility, a warehouse and a retailer. This system was constructed to study the need for coordinating distribution and production in the decision of lot-sizes. Hence, the production capacity is limited. Our objective is to compute lot-sizes that leads to the lowest possible cost in such a system. We consider stationary inventory management policies. In such a policy, a fixed lot-size is supplied from one stage to the next with a constant time interval. We introduce the concept of multiple policies and give a solution to find the best one. We then prove that multiple policies are guaranteed to cost less than 1.51% more than the best stationary policy. We improve this guarantee to 0.51% by adding some simple but non-multiple policies to our candidate policies.     

Marketing-production decisions under independent and integrated channel structure

The topic of channel structure has recently attracted much attention among researchers in the marketing and economics area. However, in a majority of the existing literature the cost considerations are extremely simplified with the major focus being pricing policy. What happens when cost incurring decisions are strongly connected with pricing policies? This is the theme we wish to explore in the present paper. The non-trivial costs considered are production, inventory, and retailer effort rate, i.e. we seek to explore the marketing-production channel. We have used the methodology of differential games. The open-loop Stackelberg solution concept has been used to solve the manufacturer and retailer's problem. The Pareto solution concept has been used to solve the problem of the vertically integrated firm. The production, pricing, and effort rate policies thus derived have been compared to obtain insights into the impact of channel structure on these policies. Also, to examine the relation between channel structure and the retailing operation requiring effort, we derive the Stackelberg and Pareto solutions with and without effort rate as a decision variable. We show that once the production rate becomes positive, it does not become zero again. This implies production smoothing. However, none of the gains of production smoothing are passed on to the retailer. The optimal production rate and the inventory policy are a linear combination of the nominal demand rate, the peak demand factor, the salvage value, and the initial inventory. Also, as opposed to some of the existing literature, the optimal policies need not necessarily be concave in nature. In the scenario where the relating operation does not require effort, the pricing policies of the manufacturer and the retailer, and the production policy of the manufacturer have a synergistic effect. However, in the scenario where the retailing operation does benefit from effort, the retailer's pricing policy need not necessarily be synergistic with other policies. With regard to channel structures, it seems that production smoothing will be done more efficiently in the integrated setup. Also, we show that the price paid by the consumer need not necessarily be lower in the integrated setup. But despite higher prices, the channel profits are higher in the integrated setup. This implies a conflict between the interests of the consumers and the firm. Also, this contradicts the results of some of the earlier papers that have used simple static models.     

Managing capacity flexibility in make-to-order production environments

This paper addresses the problem of managing flexible production capacity in a make-to-order (MTO) manufacturing environment. We present a multi-period capacity management model where we distinguish between process flexibility (the ability to produce multiple products on multiple production lines) and operational flexibility (the ability to dynamically change capacity allocations among different product families over time). For operational flexibility, we consider two polices: a fixed allocation policy where the capacity allocations are fixed throughout the planning horizon and a dynamic allocation policy where the capacity allocations change from period to period. The former approach is modeled as a single-stage stochastic program and solved using a cutting-plane method. The latter approach is modeled as a multi-stage stochastic program and a sampling-based decomposition method is presented to identify a feasible policy and assess the quality of that policy. A computational experiment quantifies the benefits of operational flexibility and demonstrates that it is most beneficial when the demand and capacity are well-balanced and the demand variability is high. Additionally, our results reveal that myopic operating policies may lead a firm to adopt more process flexibility and form denser flexibility configuration chains. That is, process flexibility may be over-valued in the literature since it is assumed that a firm will operate optimally after the process flexibility decision. We also show that the value of process flexibility increases with the number of periods in the planning horizon if an optimal operating policy is employed. This result is reversed if a myopic allocation policy is adopted instead.     

Base stock policies for production/inventory problems with uncertain capacity levels

In this paper we consider a single item, stochastic demand production/inventory problem where the maximum amount that can be produced (or ordered) in any given period is assumed to be uncertain. Inventory levels are reviewed periodically. The system operates under a stationary modified base stock policy. The intent of our paper is to present a procedure for computing the optimal base stocl level of this policy under expected average cost per period criterion. This procedure would provide guidance as to the appropriate amount of capacity to store in the form of inventory in the face of stochastic demand and uncertain capacity. In achieving this goal, our main contribution is to establish the analogy between the class of base stock production/inventory policies that operate under demand/capacity uncertainty, and the G/G/1 queues and their associated random walks. We also present example derivations for some important capacity distributions.     

Production control of an unreliable manufacturing system under the assumption of no backlog

This paper deals with an unreliable manufacturing system in which no backlog is allowed. The hedging point policy is used to control production. This paper's objective is to find the optimum hedging point so as to minimize the average inventory cost. Firstly, for a tentative hedging point, we derive the limiting distribution of the inventory level. This derivation is accomplished by comparison between this system and a finite storage-production system. Based on it we calculate the average inventory cost and find the optimum hedging point.This research was supported in part by the National Natural Science Foundation of China. Bin Liu was partially supported by ITDC, contract No. 105-82150.     

Using profit maximizing scheduling models to structure operational trade-offs and manufacturing strategy issues

Manufacturing plays an increasingly important role in determining the competitiveness of the firm. However, corporate strategy is often formulated with little regard for how these decisions affect operations within the manufacturing system. Detailed models provide a necessary link between manufacturing performance and the functional policies followed by the firm, so that the strengths of the manufacturing system can be consistently reflected in strategic decisions.This paper presents a scheduling model that relates the strategic decisions that determine the type of work that must ultimately be processed by the manufacturing system with the detailed decisions that determine how this work should be scheduled. The model accounts for varying processing time, delay penalty, and revenue characteristics among the jobs available for processing by a single facility, with jobs partitioned in multiple classes such that a setup is incurred each time two jobs of different classes are processed in succession. Given limited processing capacity, the objective is to simultaneously determine the subset of jobs to accept for processing and the associated order in which accepted jobs should be sequenced to maximize the total profit realized by the facility. Problem formulations and dynamic programming algorithms are presented for both the special case in which all available work is from a single job class, and the more general case involving multiple job classes. The insight derived from these models concerning the operational implications of strategic decisions is illustrated through a series of example problems, first focusing on the coordination of marketing and manufacturing policy, and finally by considering important issues related to manufacturing focus.     

Cost-effective inventory control in a value-added manufacturing system

This paper considers the cost-effective inventory control of work-in-process (WIP) and finished products in a two-stage distributed manufacturing system. The first stage produces a common WIP, and the second stage consists of several production sites that produce differentiated products with different capacity and service level requirements. The unit inventory holding cost is higher at the second stage. This paper first uses a network of inventory-queue model to evaluate the inventory cost and service level achievable for given inventory control policy, and then derives a very simple algorithm to find the optimal inventory control policy that minimizes the overall inventory holding cost and satisfies the given service level requirements. Some managerial insights are obtained through numerical examples.     

Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale

In this paper, we formulate an analytical model for the joint determination of an optimal age-dependent buffer inventory and preventive maintenance policy in a production environment that is subject to random machine breakdowns. Traditional preventive maintenance policies, such as age and periodic replacements, are usually studied based on simplified and non-realistic assumptions, as well as on the expected costs criterion. Finished goods inventories and the age-dependent likelihood of machine breakdowns are usually not considered. As a result, these policies could significantly extend beyond the anticipated financial incomes of the system, and lead to crises. In order to solve this problem, a more realistic analysis model is proposed in this paper to consider the effects of both preventive maintenance policies and machine age on optimal safety stock levels. Hence, a unified framework is developed, allowing production and preventive maintenance to be jointly considered. We use an age-dependent optimization model based on the minimization of an overall cost function, including inventory holdings, lost sales, preventive and corrective maintenance costs. We provide optimality conditions for the manufacturing systems considered, and use numerical methods to obtain an optimal preventive maintenance policy and the relevant age-dependent threshold level production policy. In this work, this policy is called the multiple threshold levels hedging point policy. We include numerical examples and sensitivity analyses to illustrate the importance and the effectiveness of the proposed methodology. Compared with other available optimal production and maintenance policies, the numerical solution obtained shows that the proposed age-dependent optimal production and maintenance policies significantly reduce the overall cost incurred.     

Integer programming approach to reactive scheduling in make-to-order manufacturing

New algorithms based on mixed integer programming formulations are proposed for reactive scheduling in a dynamic, make-to-order manufacturing environment. The problem objective is to update a long-term production schedule subject to service level and inventory constraints, whenever the customer orders are modified or new orders arrive. Different rescheduling policies are proposed, from a total reschedule of all remaining and unmodified customer orders to a non-reschedule of all such orders. In addition, a medium restrictive policy is considered for rescheduling only a subset of remaining customer orders awaiting material supplies. Numerical examples modeled after a real-world scheduling/rescheduling of customer orders in the electronics industry are presented and some results of computational experiments are reported.     

Sourcing decisions with capacity reservation contracts

By committing to long-term supply contracts, buyers seek to lower their purchasing costs, and have products delivered without interruption. When a long-term contract is available, suppliers are less pressured to find new customers, and can afford to charge a price lower than the prevailing spot market price. We examine sourcing decisions of a firm in the presence of a capacity reservation contract that this firm makes with its long-term supplier in addition to the spot market alternative. This contract entails delivery of any desired portion of a reserved fixed capacity in exchange for a guaranteed payment by the buyer. We investigate rational actions of the two parties under two different types of periodic review inventory control policies used by the buyer: the two-number policy, and the base stock policy. When typical demand probability distributions are considered, inclusion of the spot market source in the buyer’s procurement plan significantly reduces the capacity commitments from the long-term supplier.     

Risk minimising strategies for revenue management problems with target values

Consider a risk-averse decision maker in the setting of a single-leg dynamic revenue management problem with revenue controlled by limiting capacity for a fixed set of prices. Instead of focussing on maximising the expected revenue, the decision maker has the main objective of minimising the risk of failing to achieve a given target revenue. Interpreting the revenue management problem in the framework of finite Markov decision processes, we augment the state space of the risk-neutral problem definition and change the objective function to the probability of failing a certain specified target revenue. This enables us to obtain a dynamic programming solution that generates the policy minimising the risk of not attaining this target revenue. We compare this solution with recently proposed risk-sensitive policies in a numerical study and discuss advantages and limitations.     

Inventory control for point-of-use locations in hospitals

Most inventory management systems at hospital departments are characterised by lost sales, periodic reviews with short lead times, and limited storage capacity. We develop two types of exact models that deal with all these characteristics. In a capacity model, the service level is maximised subject to a capacity restriction, and in a service model the required capacity is minimised subject to a service level restriction. We also formulate approximation models applicable for any lost-sales inventory system (cost objective, no lead time restrictions etc). For the capacity model, we develop a simple inventory rule to set the reorder levels and order quantities. Numerical results for this inventory rule show an average deviation of 1% from the optimal service levels. We also embed the single-item models in a multi-item system. Furthermore, we compare the performance of fixed order size replenishment policies and (R,?s,?S) policies.