Integrated workforce capacity and inventory management under labour supply uncertainty

In a manufacturing environment with volatile demand, inventory management can be coupled with dynamic capacity adjustments for handling the fluctuations more effectively. In this study, we consider the problem of integrated capacity and inventory management under non-stationary stochastic demand and capacity uncertainty. The capacity planning problem is investigated from the workforce planning perspective where the capacity can be temporarily increased by utilising contingent workers from an external labour supply agency. The contingent capacity received from the agency is subject to an uncertainty, but the supply of a certain number of workers can be guaranteed through contracts. There may also be uncertainty in the availability of the permanent and contracted workers due to factors such as absenteeism and fatigue. We formulate a dynamic programming model to make the optimal capacity decisions at a tactical level (permanent workforce size and contracted number of workers) as well as the operational level (number of workers to be requested from the external labour supply agency in each period), integrated with the optimal operational decision of how much to produce in each period. We analyse the characteristics of the optimal policies and we conduct an extensive numerical analysis that helps us provide several managerial insights.     

Growth Incentives to Invest in a Network Externality Environment

Firms that operate in the telecommunications industry oftenhave to make large and risky investments in digital infrastructure.This paper examines how firm size affects the incentive to investin infrastructure projects in industry environments that incursubstantial network externalities. We suggest that, in the presenceof network externalities, a firm's rate of growth first declinesand then increases with the size of its user base. An implicationof this result is that firms may benefit from making investmentsin emerging digital infrastructure early enough to achieve asubstantial user base and to gain that user base before otherfirms' investments pre-empt them. The results mean that firmsthat undertake earlier successful investment may achieve pre-emptionvery quickly. The sources of network externalities contributeto the incidence of strategic alliances in network industries,particularly when coupled with the pressures of technical uncertainty.     

Coordination of marketing and production for price and leadtime decisions

We study a firm which serves customers that are sensitive to quoted price and leadtime, with pricing and leadtime decisions being made by the marketing and production departments, respectively. We analyze the inefficiencies created by the decentralization of the price and leadtime decisions. In the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm's profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments. We also provide insights on the sensitivity of the optimal decisions with respect to market characteristics, sequence of decisions and the firm's capacity level.     

Optimal capacity expansion for multi-product, multi-machine manufacturing systems with stochastic demand

We consider a discrete-time capacity expansion problem involving multiple product families, multiple machine types, and non-stationary stochastic demand. Capacity expansion decisions are made to strike an optimal balance between investment costs and lost sales costs. Motivated by current practices in the semiconductor and other high-tech industries, we assume that only minimal amounts of finished-goods inventories are held, due to the risk of obsolescence. We assume that when capacity is in short supply, management desires to ensure that a minimal service level for all product families is obtained. Our approach uses a novel assumption that demand can be approximated by a distribution whose support is a collection of rays emanating from a point and contained in real multi-dimensional space. These assumptions allow us to solve the problem as a max-flow, min-cut problem. Computational experiments show that large problems can be solved efficiently.     

On the effect of demand randomness on a price/quantity setting firm

Replenishment and pricing decisions are of great importance to firms. Traditionally replenishment and pricing strategies are determined by separate units of a firm, the former by production and the latter by marketing. In determining these two strategies, firms frequently have to face two challenges. One stems from a lack of coordination between production and marketing, which leads to revenue loss or excess inventory. The other is caused by lack of information when making these two decisions. That is, firms usually have to make decisions when market information is poor. We consider the joint effects of coordination and information when market demand becomes more variable in the sense of a specific mean preserving transformation. We have three main findings. First, when these two strategies are coordinated by the Headquarters (HQ) of the firm, the HQ uses pricing and inventory as two instruments to manage uncertainty. How exactly increasing demand variability affects the decisions depends on the type of demand uncertainty faced. In particular, for additive demand uncertainty, both price and service level decrease in demand variability, whereas for multiplicative demand uncertainty, they both increase in demand variability. Second, the value of information increases with the level of demand variability, suggesting that it is more beneficial to have information when demand is more variable. The impact of demand variability on the value of coordination, however, is indeterminate. Furthermore, perfect information has more value than perfect coordination if and only if demand variability is high. Third, coordinating these two decisions reduces the value of obtaining the demand information and similarly coordination is more valuable when the demand information is unavailable than otherwise.     

Modelling joint outsourcing and offshoring decisions

Offshoring and outsourcing have emerged as two most essential and important strategies for the manufacturing firms globally. This has happened due to the relentless forces of competition and globalisation, which has forced modern manufacturing firms to disaggregate themselves and reach for foreign investments, technological inputs through collaboration and cooperation among markets and partners. Outsourcing and offshoring simultaneously has potential to help the firm to meet three strategic goals i.e. ‘efficiency’ through cost minimisation; ‘exploration’ through an access to the right and technologically capable firm vendors/ Suppliers/ players; and ‘exploitation’ from development of global markets. Both outsourcing and offshoring have grown to a great extent either by the number of companies involved or by the number of multiple nations are involved. Hence, addressing both offshoring and outsourcing strategies together can provides value to the supply chain in economical and efficient way. Therefore, this paper is an attempt to model joint outsourcing and offshoring decision integrated with fuzzy-MCDM approaches using fuzzy data to optimise total cost through right selection of suppliers to meet demand of global markets, thus, achieving efficiency, exploration and exploitation. The model addresses qualitative and quantitative criteria in joint decisions and is also illustrated to show the cost effectiveness.     

Competition, Learning and Investment in New Technology

Efforts to describe the evolution of production costs must simultaneously include the impact of investment in new technologies as well as experience-based learning. In this paper we formulate a dynamic model of technological investments, which includes the impacts of learning curve effects, investment levels in new technologies with uncertain timing, and competitive responses. Our results highlight the interaction of investment and output rate decisions in monopolistic and duopolistic situations, and illuminate the impact of the planning horizon length on such decisions. The model results elaborate upon how the attractiveness of new technologies is related to the firm's ability to learn using its existing technology, how competition can increase or decrease the market size for new technologies, and how investment levels are driven in opposite directions by considering longer horizons on one hand and the competitive response on the other.     

Optimal Price-Inventory Decisions: Profit vs. ROII

Making optimal price-inventory decisions in the face of alternate criteria is studied for a monopoly firm. When the firm's demand function belongs to the logarithmic concave class, necessary and sufficient optimality conditions are obtained for profit and return on inventory investment criteria. It is shown that decentralized price-inventory decision-making is optimal when the return criterion is used. The theoretical developments are elucidated with a detailed study of linear demand and a comprehensive numerical example.     

Managing two-stage serial inventory systems under demand and supply uncertainty and customer service level requirements

We consider a two-echelon serial inventory system with demand and supply uncertainty, non-zero lead times for component procurement and end-product assembly, and a minimum customer service level requirement. We present two supply models which incorporate both quantity and timing uncertainty; these models correspond to current and proposed supply environments. Assuming that installation base-stock ordering policies are followed and that the demand distribution is quasi-concave, we show that the chance-constrained problem of determining optimal base-stock levels which minimize the total inventory investment (cost-weighted stock levels) subject to a service constraint is a convex programming problem. We characterize the relation between the optimal base-stock levels of the component and the end-product. We also illustrate how an optimal internal (component) service level can be computed, which permits decomposition of the two-stage serial system into two coordinated single-echelon systems. Computational experiments illustrate insights on the effects of supply uncertainty and other problem parameters on stock-positioning in a two-echelon serial system. In particular, we evaluate the benefits of switching from one supply environment to another.     

Newsvendor decisions under supply uncertainty

We analyse the impact of supply uncertainty on newsvendor decisions. First, we derive a solution for a newsvendor facing stochastic supply yield, in addition to stochastic demand. While earlier research has considered independent uncertainties, we derive the optimal order quantity for interdependent demand and supply and provide a closed-form solution for a specific copula-based dependence structure. This allows us to give insights into how dependence impacts the newsvendor’s decision, profit and risk level. In addition to the theory, we present experimental results that show how difficult newsvendor decisions under supply uncertainty are for human subjects. In our experiment, the control group replicated a well-known newsvendor experiment, whereas the test group faced additional supply yield uncertainty. Comparison of these results shows that under low-profit condition, subjects are able to incorporate supply uncertainty quite well in their decisions. Under high-profit condition, the deviation from the optimum is much more significant. We discuss this asymmetry and also propose some ways to improve newsvendor decision-making.     

Analysis of investments in autonomous maintenance activities

In this paper, we model the situation where operator maintenance activities improve the failure process of equipment. We analyze the business decision to reduce both the mean and variance of the production cycle time and the overall inventory level through an investment in planned autonomous maintenance. We answer: (i) when do optimal autonomous maintenance decisions most improve inventory levels?; and (ii) how do capacity restrictions, equipment characteristics the maintenance response function, and product characteristics impact the autonomous maintenance investment decision? Extensive numerical analyses are performed to develop an approximation to the optimal response for both inventory and autonomous maintenance investments over a wide range of problem parameters. Our solutions provide guidelines on how much time should be invested in autonomous maintenance activities and describe when companies can most benefit from autonomous maintenance programs that increase equipment reliability. We determine the investment in autonomous maintenance activities as a function of available capacity, equipment reliability and demand characteristics.     

Lead-time setting, capacity utilization, and pricing decisions under lead-time dependent demand

This research examines the lead-time setting, capacity utilization, and pricing decisions facing a firm serving customers that are sensitive to quoted lead-times. We model the firm's operations as an M/M/1 queue and treat the demand as being linear in price and quoted lead-time. We analyze the quoted lead-time, capacity utilization, and price that maximize revenues less total variable production costs, WIP holding costs, and lateness penalty costs. We use this analysis to show that the capacity utilization should be lower when (1) customers are more sensitive to lead-times and/or (2) the firm incurs higher congestion related costs and/or (3) the penalty for lateness is higher. Further, we study the robustness of optimal profit contributions when the model parameters are mis-estimated.     

Configuring a manufacturing firm's supply network with multiple suppliers

Consider a supply network consisting of a manufacturer and its suppliers. The manufacturer produces different types of products, using a common set of inputs (e.g., raw materials and/or component parts) from the suppliers: but, each product needs a different mix of these inputs. The manufacturer sells its finished products to the market at the end of the current decision horizon, facing an uncertain market demand. In situations where a manufacturer has outsourced its parts production to contract manufacturers, the contract manufacturer's capacity available for the given manufacturer in a particular time period may be limited by “capacity reservation” agreements made in advance. Thus, in making production mix decisions for the current planning horizon, the manufacturer has to take into account both its own and the component suppliers' capacity restrictions. We develop a mathematical model and an iterative algorithm that helps the manufacturer solve its supply configuration problem, that is, how much of each raw material and/or component part to order from which supplier, given capacity limits of suppliers as well as the manufacturer. The model takes into account such factors as market demand uncertainty, costs and product characteristics. We present an numerical example to illustrate the interacting effects among critical parameters in the model, and apply the model to a real-world case of a computer manufacturer.     

Optimisation of online retailer pricing and carrier capacity expansion during low-price promotions with coordination of a decentralised supply chain

In recent years, many online retailers in China set low prices on 11 November, which stimulates huge delivery demand and results in many problems although carriers make an effort to increase their delivery capacities temporarily. To circumvent this difficulty, we consider a supply chain consisting of an online retailer, who can set price to influence the demand, and a capacitated carrier, whose capacity can be expanded at a high cost. We derive the optimal decisions in the centralised and decentralised decision systems, and compare the performances of the two systems. We find that the optimal decisions, and which system has lower price, larger capacity increment, and more late delivered goods depends on the model parameters (the market scale, the late delivery costs, the capacity expansion cost, the delivery fee, and the demand uncertainty). Specially, we show that, contrary to the traditional channel, the online retailer in the decentralised system may set lower price and the carrier has less incentive to expand capacity in the decentralised system in some situations, which underlines the need for coordination. In addition, we propose coordination contracts to improve the overall performance of the supply chain under deterministic and random demands.     

供需随机和资金约束下新创企业的运营决策

考察由OEM供应商、新创企业和需求市场组成的供应链,其中新创企业通过自建产能或向OEM供应商采购来满足市场需求,研究受资金约束的新创企业以股东权益价值最大为目标时产能设置和订货数量的最优决策。研究表明,当债权融资金额无限制时,随着新创企业初始自有资金逐渐增加,新创企业的最优产能先是保持不变,然后逐渐增加,最后下降;当初始资金非常匮乏时完全采用外包策略以及在资金较充分时彻底抛弃外包都不是最佳选择。     

Replenishment planning in discrete-time, capacitated, non-stationary, stochastic inventory systems

In this paper, we examine a single-product, discrete-time, non-stationary, inventory replenishment problem with both supply and demand uncertainty, capacity limits on replenishment quantities, and service level requirements. A scenario-based stochastic program for the static, finite-horizon problem is presented to determine replenishment orders over the horizon. We propose a heuristic that is based on the first two moments of the random variables and a normal approximation, whose solution is compared with the optimal from a simulation-based optimization method. Computational experiments show that the heuristic performs very well (within 0.25% of optimal, on average) even when the uncertainty is non-normal or when there are periods without any supply. We also present insights obtained from sensitivity analyses on the effects of supply parameters, shortage penalty costs, capacity limits, and demand variance. A rolling-horizon implementation is illustrated.     

Supply management in assembly systems with random yield and random demand

In this paper, we consider an assembly system where a firm faces random demand for a finished product which is assembled using two critical components. The components are procured from the suppliers who, due to production yield losses, deliver a random fraction of the order quantity. We formulate the exact cost function where the decision variables are the target level of finished products to assemble, and the order quantity of the components from the suppliers. Since the exact cost function is analytically complex to solve, we introduce a modified cost function and derive bounds on the difference in the objective function values. Using the modified cost function, we determine the combined component ordering and production (assembly) decisions for the firm. The benefit of coordinating ordering and assembly decisions is numerically demonstrated by comparing the results with two heuristic policies commonly used in practice. In an extension to the model, we consider the case when the firm has the added option of ordering both the components in a set from a joint supplier. First, we consider the case when the joint supplier is reliable in delivery and obtain dominance conditions on the suppliers to be chosen. The maximum price a firm would be willing to pay to ensure reliable supply of components is determined. Later, we consider the uncertainty in the deliveries from the joint supplier and determine conditions under which there is no diversification, that is, either the individual suppliers are used, or the joint supplier is used, but never both.     

Two-period vs. multi-period model for supply chain disruption management

A novel two-period modelling approach is developed for supply chain disruption mitigation and recovery and compared with a multi-period approach. For the two-period model, planning horizon is divided into two aggregate periods: before disruption and after disruption. The corresponding mitigation and recovery decisions are: (1) primary supply and demand portfolios and production before a disruption, and (2) recovery supply, transshipment and demand portfolios and production after the disruption. In the multi-period model, a multi-period planning horizon is applied to account for a detailed timing of supplies and production. The primary and recovery portfolios are determined simultaneously and for both approaches the integrated decision-making, stochastic mixed integer programming models are developed. While the simplified two-period setting may overestimate (for best-case capacity constraints) or underestimate (for worst-case capacity constraints) the available production capacity, it can be easily applied in practice for a fast, rough-cut evaluation of disruption mitigation and recovery policy. The findings indicate that for both two- and multi-period setting, the developed multi-portfolio approach leads to computationally efficient mixed integer programming models with an embedded network flow structure resulting in a very strong linear programming relaxation.     

Variety and volume dynamic management for value creation in changeable manufacturing systems

In today’s uncertain market and continuously evolving technology, managing manufacturing systems are more complex than ever. This paper studies the dynamics of managing variety and volume to enhance value creation in manufacturers implementing system-level advanced and automated manufacturing technology (AAMT). The demand is composed of heterogeneous customers who make purchasing decisions depending on the variety levels and lead times of the firm’s product offerings. The cost structure adopted calculates profit as the difference between customer value creation rate (VCR) and costs associated with the process of creating this value. Reported results contribute to the variety and volume management literature by offering analytical clarity of factors affecting product platforms and capacity scalability management for systems with AAMT. In addition, insightful answers to the trade-offs between profit maximising market coverage and investments, smoothing demand policies and system stability for this type of environment are presented. Furthermore, the value of market information in deciding the industrial technology investment and also the impact of product life cycle on the same investment is captured.     

Inventory competition and incentives to back-order

In this paper we consider the issue of inventory control in a multi-period environment with competition on product availability. Specifically, when a product is out of stock, the customer often must choose between placing a back-order or turning to a competitor selling a similar product. We consider a competition in which customers may switch between two retailers (substitute) in the case of a stock-out at the retailer of their first choice. In a multi-period setting, the following four situations may arise if the product is out of stock: (i) sales may be lost; (ii) customers may back-order the product with their first-choice retailer; (iii) customers may back-order the product with their second-choice retailer; or (iv) customers may attempt to acquire the product according to some other more complex rule. The question we address is: how do the equilibrium stocking quantities and profits of the retailers depend on the customers' back-ordering behaviors? In this work we consider the four alternative back-ordering scenarios and formulate each problem as a stochastic multi-period game. Under appropriate conditions, we show that a stationary base-stock inventory policy is a Nash equilibrium of the game that can be found by considering an appropriate static game. We derive conditions for the existence and uniqueness of such a policy and conduct a comparative statics analysis. Analytical expressions for the optimality conditions facilitate managerial insights into the effects of various back-ordering mechanisms. Furthermore, we recognize that often a retailer is willing to offer a monetary incentive to induce a customer to back-order instead of going to the competitor. Therefore, it is necessary to coordinate incentive decisions with operational decisions about inventory control. We analyze the impact of incentives to back-order the product on the optimal stocking policies under competition and determine the conditions that guarantee monotonicity of the equilibrium inventory in the amount of the incentive offered. Our analysis also suggests that, counterintuitively, companies might benefit from making their inventories “visible” to competitors' customers, since doing so reduces the level of competition, decreases optimal inventories and simultaneously increases profits for both players.