The impact of dual-market on supply chain configuration for new products

Given a firm’s supply chain network, the key objective of supply chain configuration (SCC) is to determine a subset of supply chain partners to be involved in development, sourcing, production, distribution and support of a new product at the highest level of efficiency and expected responsiveness. Current literature on SCC realises the importance of considering the demand dynamics associated with the new product diffusion (NPD). However, these studies assume one-segment market for new products, a single homogenous consumer segment. Recent research in marketing indicates that such simplification might be fatal because a diverse and significant number of product categories may experience a dual-market structure, namely early and main markets, and generate a different demand dynamics. The objectives of this study are to: (i) develop a hybrid optimisation model, capturing both SCC decisions and the demand dynamics of dual-market NPD process; (ii) based on real-world data for a host of electronic product categories, various SCC networks and NPD demand dynamics, examine the new integrated optimisation model under one- and two-segment market; and (iii) present relevant managerial implications and guidelines for supply chain and marketing managers. Our extensive comparative computational experiment with 26 categories of consumer electronic products show that on average the relative net profit may improve significantly, when the market is considered as two-segment.     

The role of spot market in a decentralised supply chain under random yield

We consider a decentralised supply chain with demand uncertainty, wherein a manufacturer sells a single product that consists of two components to the end consumer through an independent distributor. Assume that one component has a random yield, the manufacturer may rely on the spot market to replenish any shortage components after the random yield is realised. We identify the optimal product order, component production and replenishment decisions under two different situations. One is the traditional arrangement that the distributor decides the product procurement quantity and the manufacturer chooses the production quantity. The other is vendor-managed inventory (VMI) arrangement in which the manufacturer is responsible for both the order and the production decisions. The results show that the role of spot market can effectively offset the negative effects from production yield’s, market demand’s and component spot price’s variances. Consequently, the spot market leads to the cooperation between the manufacturer and the distributor, and improves the channel’s overall performance, in particular under the VMI arrangement when the wholesale price is sufficiently high.     

A manufacturer’s production and pricing strategies with a salvage channel

We study a manufacturer’s production quantity and pricing decisions when the manufacturer has an opportunity to sell surplus inventory through a salvage channel. Before sales begin, the manufacturer determines the production quantity without knowing customer demand. After demand is realised, the manufacturer first satisfies the demand through primary channel while charging a fixed price or adjusting price to maximise profit. At the end of the selling season, the manufacturer resells surplus inventory through the salvage channel, which can be either integrated with or independent of the manufacturer. The manufacturer’s optimal production quantity and expected profit are investigated under different salvage channel structures. We show that the salvage channel improves the manufacturer’s profitability as well as the availability of the product to potential customers through both primary and salvage channels.     

Assessing the benefits of labelling postponement in an export-focused winery

Supporting wine production operations in an increasingly global market has grown ever more challenging. Export-focused wineries supply many foreign clients, often requiring different labels for the same kind of wine. Order forecasts tend to be highly inaccurate, and wineries must be able to quickly react to changes, making lot-sizing an important consideration. One tool to reduce product misallocation is postponing product differentiation, where the natural decoupling point for premium wine is the labelling process. However, the double handling involved incurs additional costs and time penalties. We analyse the performance impact of postponing the labelling of bottled wines by developing a multi-stage mixed-integer stochastic programming model with full recourse for demand scenarios. The underlying data and policies are based on an unnamed Chilean export-focused winery. The model supports lot-sizing under several winery production policies. We experiment with different levels of capacity tightness, demand variability and demand correlation between wines, optimising for reducing order backlogs, inventory levels and line set-ups. While we find that some amount of postponement will always be recommended, the exact mix and degree depend on these external factors. Postponement has the most benefits when production capacity is moderately tight, demand variability is high and wines have negatively correlated demands.     

Mitigating supply and production uncertainties with dynamic scheduling using real-time transport information

Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty.     

An integrated operations-marketing perspective for making decisions about order penetration point in multi-product supply chain: a queuing approach

This study is dedicated to strategic decision-making regarding order penetration point (OPP), which is the boundary between make-to-order (MTO) and make-to-stock (MTS) policies. This paper considers a supply chain in which a manufacturer produces semi-finished items on an MTS basis for a retailer that will customise the items based on MTO policy. This two-echelon supply chain offers different products to a market comprised of homogenous customers who have different preferences and willingness to pay. The retailer wishes to determine the optimal OPP, the optimal semi-finished goods buffer size, and the price of the products with assumption of price sensitive demand function. Moreover, we consider both shared and unshared capacity models in this paper. A matrix geometric method is utilised to evaluate various performance measures for this system, and then, optimal solutions are obtained by enumeration techniques. The suggested queuing approach is based on a new perspective between the operations and marketing functions which captures the interactions between several factors including inventory level, product pricing, OPP, and delivery lead time. Finally, parameter sensitivity analyses are carried out and the effect of demand on the profit function, the effect of prices ratio on completion rates ratio and buffer sizes ratio and the variations of profit function for different prices, completion percents, and buffer sizes are examined.     

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.     

考虑随机市场需求和不同博弈地位的双渠道供应链策略分析

为研究供应链成员不同博弈地位下双渠道定价及随机库存问题,分别构建制造商主导、零售商主导及双方同等博弈地位的双渠道供应链博弈模型,考察随机市场需求对定价、库存及利润的影响,分析具有不同博弈地位的供应链策略差异,并通过数值仿真探讨最佳响应策略。结果表明,主导方凭借地位优势拥有更强的价格把控权及库存调配能力,获得更高收益;制造商主导型供应链具有更强的渠道整合能力,供应链的整体利润水平高于零售商主导型;同等博弈地位情形的博弈双方可根据共同市场信息及竞争对手策略更新自身策略,具有较低的库存风险,渠道定价越低,但双重边际效应降低了双方收益;市场需求波动越大,渠道定价、库存量及供应链利润均越大,此时主导方利润增幅大于追随者。     

Lead-time analysis and a control policy for production lines

We address a control problem for a production line that produces one product to stock and faces random demand. During stockouts, the system quotes a fixed response time for each arriving order, and the customers place their orders only if the response time promised meets their deadlines. Customer orders are filled on a first come, first served basis. A penalty cost is incurred whenever a customer is served later than promised. A two-parameter admission/inventory control policy is implemented that maintains a bounded backlog and a constant inventory position (total inventory minus backlog) in the system. For production lines with exponential processing times and Poisson demand, the mean profit rate of the system is computed analytically using closed queueing network formulae. For systems with general processing or interarrival time distributions, the profit rate is estimated via simulation. Simple properties are established that ensure that the profit maximising control parameters can be determined in finite time using exhaustive search. Numerical results show that the proposed policy performs better than the make-to-order/zero-inventory and the lost-sales/make-to-stock policies.     

Supply chain capacity and outsourcing decisions: the dynamic interplay of demand and supply uncertainty

We study the interplay of demand and supply uncertainty in capacity and outsourcing decisions in multi-stage supply chains. We consider a firm's investment in two stages of a supply chain (Stage 1 models the “core” activities of the firm, while Stage 2 are the “non-core” activities). The firm invests in these two stages in order to maximize the multi-period, discounted profit. We consider how non-stationary stochastic demand affects the outsourcing decisions. We also consider how investment levels are affected by non-stationary stochastic supply when the market responds to the firm's investments. We characterize the optimal capacity investment decisions Tor the single- and multi-period versions of our model and focus on how changes in supply and demand uncertainly affect the extent of outsourcing. We find that as the responsiveness of the market to investments made by the firm increases, the reliance on outsourcing generally increases. While greater supply and greater demand have the expected effect on investments, decreases in variability are not as straightforward. Greater supply uncertainty increases the need for vertical integration while greater demand uncertainty increases the reliance on outsourcing. In the multi-period model, we find that the nature of adjustments in capacity based on changes in demand or supply follows from the comparative statics of the single-period model, although whether outsourcing increases or decreases depends on the costs of adjusting capacity.     

A robust optimisation model for hybrid remanufacturing and manufacturing systems under uncertain return quality and market demand

In remanufacturing research, most researchers predominantly emphasised on the recovery of whole product (core) rather than at the component level due to its complexity. In contrast, this paper addresses the challenges to focus on remanufacturing through component recovery, so as to solve production planning problems of hybrid remanufacturing and manufacturing systems. To deal with the uncertainties of quality and quantity of product returns, the processing time of remanufacturing, remanufacturing costs, as well as market demands, a robust optimisation model was developed in this research and a case study was used to evaluate its effectiveness and efficiency. To strengthen this research, a sensitivity analysis of the uncertain parameters and the original equipment manufacturer’s (OEM’s) pricing strategy was also conducted. The research finding shows that the market demand volatility leads to a significant increase in the under fulfilment and a reduction in OEM’s profit. On the other hand, recovery cost reduction, as endogenous cost saving, encourages the OEM to produce more remanufactured products with the increase in market demand. Furthermore, the OEM may risk profit loss if they raise the price of new products, and inversely, they could gain more if the price of remanufactured products is raised.     

Joint production control and product quality decision making in a failure prone multiple-product manufacturing system

This paper considers joint production control and product quality specifications decision making in unreliable multiple-product manufacturing system. This is with the knowledge that an optimum compromise should guide the decision making process. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with a higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, in unreliable manufacturing context the decision maker should adopt an adequate production policy to hedge against future capacity shortages caused by machine failures in order to meet customer demand. This paper intends to extend previous findings to tackle this problem and study the overall decision making process aiming to guide the production and quality specification decisions in multiple-product context. The overall optimal decision policy is defined here as one that maximises the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations.     

Dynamic pricing for fashion goods with partial backlogging

In today’s very competitive business environment, one way that companies endeavour to cut their costs is by reducing inventory. In addition to the use of inventory management, retailers of fashion goods usually employ dynamic pricing to influence market demand, such as non-periodic clearance sales or markdowns to increase sales volume and deplete stocks of goods. This paper deals with the specific demand style of fashion goods under the condition in which partial backlogging is allowed and the number of replenishments is constrained in order to fit in the real world situation of limited supply. The optimal replenishment quantities and the corresponding intervals are obtained with consideration of customers’ reserve prices and their influence on demand. In addition, discrete dynamic pricing is considered to frequently change the product prices to meet the customer requirements in different periods and to avoid high inventory holding costs from unsold products. A numerical example is used to validate the feasibility and effectiveness of the proposed approach, and sensitivity analyses are performed to investigate the important factors that influence the replenishment cycle and profit.     

An inventory model with emergency orders under explicit energy cost considerations

The inventory model in this paper is targeted to production systems with constant production rates but underlying possibilities for undesirable circumstances to threaten the production schedule. The inventory policy proposed explicitly considers energy cost when determining optimal size for order quantity, safety stock and inventory cycle length such that the total expected cost per unit time is minimised. The results are compared to a traditional inventory policy that does not consider the direct impact of energy cost. An analysis of the model reveals three production environment characteristics in which inventory policies are most significantly affected by changes in energy cost: heavy product weight, high regular product demand or high emergency product demand. If any one of the three key factors increases, then changes of the inventory decisions or related logistics costs become more significant. The cost effectiveness of implementing the proposed inventory policy also becomes more significant as any one of the three key factors increase with respect to energy cost.     

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.     

Optimal production and pricing policies in a combined make-to-order/make-to-stock system

In this paper, we consider a production system which is capable to produce two types of products. The first type of products is make-to-order, while the second type is make-to-stock whose demand is satisfied by the on-hand inventory. The demand arrival rates of both types of products are price-sensitive. The excess demand that cannot be satisfied immediately is either backlogged or lost. The system costs include the holding costs of product inventories and shortage costs of unsatisfied demand. The objective is to maximise the total discounted profit over an infinite planning horizon by coordinating the production process and pricing decisions. By analysing the properties of objective functions, we characterise the optimal control policy by two switch curves and the optimal price is also given for different ordering and inventory levels. We also explore the monotonicity of both switch curves which will reduce the computation effort. Numerical experiments are conducted to demonstrate the use of the switch curves in managing the production system and illustrate that compared with the static pricing policy, the optimal integrated price and inventory control policy can result in a significant profit improvement in the make-to-order/make-to-stock system that is much higher than in a single-product system.     

Measure of the bullwhip effect considering the market competition between two retailers

In order to consider the market competition, a new supply chain with one supplier and two retailers is established in this paper. Two retailers employ different AR(1) demand processes, respectively, and an order-up-to inventory policy characterises the inventory decisions. The bullwhip effect in this supply chain is measured under the moving average forecasting technique. We investigate the effects of the lead time, the span of forecast, market competition and the consistency of demand volatility on the bullwhip effect using the algebraic analysis and numerical simulation. Conclusions indicate that different factors lead to the bullwhip effect following different patterns in the supply chain. Moreover, some suggestions are present to help managers to control parameters that yield the lower bullwhip effect.     

Value chain management for commodities: a case study from the chemical industry

We present a planning model for chemical commodities related to an industry case. Commodities are standard chemicals characterized by sales and supply volatility in volume and value. Increasing and volatile prices of crude oil-dependent raw materials require coordination of sales and supply decisions by volume and value throughout the value chain to ensure profitability. Contract and spot demand differentiation with volatile and uncertain spot prices, spot sales quantity flexibility, spot sales price–quantity functions and variable raw material consumption rates in production are problem specifics to be considered. Existing chemical industry planning models are limited to production and distribution decisions to minimize costs or makespan. Demand-oriented models focus on uncertainty in demand quantities not in prices. We develop an integrated model to optimize profit by coordinating sales quantity, price and supply decisions throughout the value chain. A two-phase optimization approach supports robust planning ensuring minimum profitability even in case of worst-case spot sales price scenarios. Model evaluations with industry case data demonstrate the impact of elasticities, variable raw material consumption rates and price uncertainties on planned profit and volumes.     

资金约束时零售商融资与订货决策及价值分析

针对供应链中存在资金约束的情形,建立了包含制造商（M）、零售商（R）双层结构、不考虑残次品率、库存成本和融资周期长短的简洁供应链决策模型。研究了当市场需求不确定和零售商资金不足情形下,零售商选择是否融资这两种情况下,其订货决策的变化,以及零售商和制造商的收益变化。通过有无融资条件下零售商不同数量自有资金时订货量和期望收益对比和零售商自有资本一定时订货量随周期变化的数值分析,证明了零售商的融资决策与订货量决策会随着其自有资金及市场需求的变化而变化;验证了融资可以提高零售商和供应链的收益,而且能够使得供应链的总产品尽快达到最佳产量,有效满足消费者需求。     

Lateral inventory transshipment problem in online-to-offline supply chain

Online-to-offline (OTO) is a new commercial model with enormous market potential. Online customer orders are forwarded to the offline brick-and-mortar store to fulfil, which is a combination of dual-channel supply chain. OTO overcomes many disadvantages of the traditional dual-channel supply chain, but still faces uncertain market demand. To reduce the inventory risk caused by demand uncertainty, lateral inventory transshipment is employed in this paper to pool inventory risk in OTO supply chain. We model centralised OTO and decentralised OTO with/without transshipment, and then analyse different scenarios. Our results demonstrate that there exists a unique Nash equilibrium of inventory order levels in dual channels and an optimal transshipment price to maximise the profit of the entire supply chain. Finally, we provide a numerical example of uniform demand distribution. Our analyses offer many managerial insights and show that transshipment always benefits the OTO supply chain.