Product substitution and dual sourcing under random supply failures

Product substitution can mitigate supply chain disruptions. However, it may not be very effective without multiple sourcing. In this paper, we consider a supply chain with two downward substitutable products. The products can be ordered from an unreliable supplier or a reliable but more expensive supplier. It is found that in an optimal sourcing policy the higher-grade product should be preferred over the lower-grade product. A sufficient condition is given for an optimal policy where only the higher-grade product is dual-sourced. The effect of substitution is contrasted with the non-substitution case. Numerical study shows the impact of demand variability and correlation on the effect of product substitution and the corresponding optimal sourcing policy.     

Optimal pricing for build-to-order supply chain design under price-dependent stochastic demand

Build to order (BTO) is a supply chain disruption mitigation strategy. Whereas cost minimization is an operational objective, the goal of the BTO manufacturer is to maximize its profit by using pricing as its competitive decision-making strategy. In this paper, we study a BTO manufacturer who simultaneously determines its product prices and designs its supply chain network to maximize its expected profit under price-dependent stochastic demand. We propose an L-shaped decomposition with complete enumeration to solve for optimality and show that the expanded master problem remains convex programming, although the optimality cuts are quadratic inequalities. The computational results demonstrate that stocking up on differentiated components and allocating modules appropriately to meet realized demand is a resilient policy that sustains variations in demand. Furthermore, the pricing decision balances the expected revenue and expected operating cost with an increase in expected profit. The integration of pricing and operational planning results in a higher expected profit than by individual decisions. We also demonstrate that cost minimization may not provide the same level of profit if the manufacturer overestimates or underestimates its most profitable demand.     

Strategies for customer service level protection under multi-echelon supply chain disruption risk

We model a multi-echelon system where disruptions can occur at any stage and evaluate multiple strategies for protecting customer service if a disruption should occur. The strategies considered take advantage of the network itself and include satisfying demand from an alternate location in the network, procuring material or transportation from an alternate source or route, and holding strategic inventory reserves throughout the network. Unmet demand is modeled using a mix of backordering and lost sales. We conduct numerical analysis and provide recommendations on selecting strategic mitigation methods to diminish the impact of disruptions on customer service. We demonstrate that the greatest service level improvements can be made by providing both proactive inventory placement to cover short disruptions or the start of long disruptions, and reactive back-up methods to help the supply chain recover after long or permanent disruptions.     

Competition and disruption in a dynamic urban supply chain

Rapid changes and complexities in business environments have stressed the importance of interactions between partners and competitors, leading supply chains to become the most important element of contemporary business environments. There is a concomitant need for foresight in describing supply chain performance in all operating environments, including those involving punctuated disruptions. Furthermore, the urban metropolis is now widely recognized to be an environment which is especially vulnerable to supply chain disruptions and for which integrated supply chain decisions can produce very substantial net benefits. Accordingly, this paper presents a dynamic supply chain network model formulated as a differential variational inequality; the model is fashioned to allow consideration of supply chain disruption threats to producers, freight carriers, and retail enterprises. The DVI is solved using a fixed-point algorithm, and a simple numerical example, introduced to illustrate how the impacts of supply chain disruptions may be quantified, is presented.     

A single-period analysis of a two-echelon inventory system with dependent supply uncertainty

Disruptions and random supplies have been important sources of uncertainty that should be considered in the design and control of supply chains. There have been many real world examples in which a single catastrophic event has simultaneously degraded the capabilities of several suppliers leading to considerable erosion of profits and goodwill for a company. However, the literature on analytical models that account for the dependence nature of disruptions and its impact on supply chain performance is sparse.In this paper, we consider an m-manufacturer, 1-retailer, newsvendor inventory system with stochastically dependent manufacturing capacities, caused by random disruptions that may simultaneously inflict damages to the capacities of the manufacturers. We develop the structural/analytical properties of key performance measures and optimal inventory policies for the multi-source and assembly inventory systems. We show that stochastic dependence in disruptions can have opposite effects on system performance in the multi-source and assembly systems. While risk diversification is preferred in the multi-source system, risk concentration is preferred in the assembly system. Our results also suggest that, if the retailer ignores the effect of dependent disruptions, then in the multi-source structure, it would underestimate the cost, overestimate the fill rate, and order more units than the optimum; however, in the assembly structure, the opposite would happen. We perform a comprehensive numerical study to validate our analytical results and generate useful managerial and operational insights for effective risk management of supply chains in the presence of dependent supply uncertainty.     

Designing a supply chain resilient to major disruptions and supply/demand interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents a hybrid robust-stochastic optimization model and a Lagrangian relaxation solution method for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose risk of occurrence and magnitude of impact can be mitigated through fortification investments. We study a realistic problem where a disruption can cause either a complete facility shutdown or a reduced supply capacity. The probability of disruption occurrence is expressed as a function of facility fortification investment for hedging against potential disruptions in the presence of certain budgetary constraints. Computational experiments and thorough sensitivity analyses are completed using some of the existing widely-used datasets. The performance of the proposed model is also examined using a Monte Carlo simulation method. To explore the practical application of the proposed model and methodology, a real world case example is discussed which addresses mitigating the risk of facility fires in an actual oil production company. Our analysis and investigation focuses on exploring the extent to which supply chain design decisions are influenced by factors such as facility fortification strategies, a decision maker's conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints.     

Measuring supply chain efficiency based on a hybrid approach

Supply chain management has a tremendous impact on the success of a company. One of the critical issues for gaining competitive advantages for companies is improving supply chain performance. Most studies about the application of Data Envelopment Analysis (DEA) Supply chain models do not identify the benchmarking units for inefficient supply chains. On the other, measuring the short run and long run of the supply chain efficiency is another challenge for decision makers in supply chain management. Hence, we propose a methodology of DEA for measuring of the supply chain. We integrated two approaches as special cases of the hybrid model and compare the short and long run strategies of supply chain and can be identified benchmarking.     

Green supply chain network design to reduce carbon emissions

We consider a supply chain network design problem that takes CO₂ emissions into account. Emission costs are considered alongside fixed and variable location and production costs. The relationship between CO₂ emissions and vehicle weight is modeled using a concave function leading to a concave minimization problem. As the direct solution of the resulting model is not possible, Lagrangian relaxation is used to decompose the problem into a capacitated facility location problem with single sourcing and a concave knapsack problem that can be solved easily. A Lagrangian heuristic based on the solution of the subproblem is proposed. When evaluated on a number of problems with varying capacity and cost characteristics, the proposed algorithm achieves solutions within 1% of the optimal. The test results indicate that considering emission costs can change the optimal configuration of the supply chain, confirming that emission costs should be considered when designing supply chains in jurisdictions with carbon costs.     

On the fundamental diagram and supply curves for congested urban networks

Macroscopic fundamental diagrams (MFD) of traffic for some networks have been shown to have similar shape to those for single links. They have erroneously been used to help estimate the level of travel in congested networks. We argue that supply curves, which track vehicles in their passage through congested networks, are needed for this purpose, and that they differ from the performance curves generated from MFD. We use a microsimulation model, DRACULA and two networks, one synthesizing the network for Cambridge, England, and one of the city of York, England, to explore the nature of performance curves and supply curves under differing patterns of demand.We show that supply curves differ from performance curves once the onset of congestion is reached, and that the incorrect use of performance curves to estimate demand can thus seriously underestimate traffic levels, the costs of congestion, and the value of congestion relief measures. We also show that network aggregated supply curves are sensitive to the temporal distribution of demand and, potentially, to the spatial distribution of demand. The shape of the supply curve also differs between origin–destination movements within a given network.We argue that supply curves for higher levels of demand cannot be observed in normal traffic conditions, and specify ways in which they can be determined from microsimulation and, potentially, by extrapolating observed data. We discuss the implications of these findings for conventional modelling of network management policies, and for these policies themselves.     

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.     

A bi-criteria indicator to assess supply chain network performance for critical needs under capacity and demand disruptions

In this paper, we develop a supply chain/logistics network model for critical needs in the case of disruptions. The objective is to minimize the total network costs, which are generalized costs that may include the monetary, risk, time, and social costs. The model assumes that disruptions may have an impact on both the network link capacities as well as on the product demands. Two different cases of disruption scenarios are considered. In the first case, we assume that the impacts of the disruptions are mild and that the demands can be met. In the second case, the demands cannot all be satisfied. For these two cases, we propose two individual performance indicators. We then construct a bi-criteria indicator to assess the supply chain network performance for critical needs. An algorithm is described which is applied to solve a spectrum of numerical examples in order to illustrate the new concepts.     

Survey analysis of supply chain adjustment for Taiwanese information technology firms

Owing to the globalization of industry, the explosion in customer services and product life-cycle compression, most manufacturers have been adjusting the processes and activities of their supply chains to remain competitive and optimize total profit. The aim here was to explore the issues surrounding the changes in manufacturing supply chains and the consequential impact on freight transport demand. A questionnaire to survey Taiwanese information technology (IT) firms was designed and conducted to chart in detail the changes in supply chains of manufacturers, the trends in the international division of labour, and the strategic adjustment of manufacturing and logistics strategies. Surveys and interviews led to the conclusions that (1) vertical integration and international division of labour are very conspicuous among Taiwanese IT firms, (2) there are different strategies of supply chain adjustment at various stages of the product life-cycle, (3) with the transformation of the supply chain, manufacturers require faster transport services, i.e. the demand for air transport may increase and the demand for sea transport may decrease, and (4) once the firms decide on foreign investment, the availability of international transport services becomes one of the considerations for factory offshore relocation. However, transport cost is not a major consideration for firms' supply chain adjustments.     

Shipment forecasting for supply chain collaborative transportation management using grey models with grey numbers

The sharing of forecasts is vital to supply chain collaborative transportation management (CTM). Shipment forecasting is fundamental to CTM, and is essential to carrier tactical and operational planning processes such as network planning, routing, scheduling, and fleet planning and assignment. By applying and extending grey forecasting theory, this paper develops a series of shipment forecasting models for supply chain CTM. Grey time-series forecasting and grey systematic forecasting models are developed for shipment forecasting under different collaborative frameworks. This paper also integrates grey numbers with grey models for analyzing shipment forecasting under partial information sharing in CTM frameworks. An example of an integrated circuit (IC) supply chain and relevant data are provided. The proposed models yield more accurate prediction results than regression, autoregressive integrated moving average (ARIMA), and neural network models. Finally, numerical results indicate that as the degree of information sharing increases under CTM, carrier prediction accuracy increases. This paper demonstrates how the proposed forecasting models can be applied to the CTM system and provides the theoretical basis for the forecasting module developed for supply chain CTM.     

Reducing emissions through speed optimization in supply vessel operations

This paper examines how optimizing sailing speeds can reduce supply vessels emissions in the upstream supply chain to offshore installations. We introduce several speed optimization strategies to be used in construction of periodic vessel schedules. The strategies consider vessel waiting times before the start of service at installations and at supply base. Tests carried out on real instances from Statoil’s activities on the Norwegian continental shelf indicate that a 25% emissions and fuel cost reductions can be achieved without fleet size increase.     

Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study

We present an approach to systematically analysing the vulnerability of road networks under disruptions covering extended areas. Since various kinds of events including floods, heavy snowfall, storms and wildfires can cause such spatially spread degradations, the analysis method is an important complement to the existing studies of single link failures. The methodology involves covering the study area with grids of uniformly shaped and sized cells, where each cell represents the extent of an event disrupting any intersecting links. We apply the approach to the Swedish road network using travel demand and network data from the Swedish national transport modelling system Sampers. The study shows that the impacts of area-covering disruptions are largely determined by the level of internal, outbound and inbound travel demand of the affected area itself. This is unlike single link failures, where the link flow and the redundancy in the surrounding network determine the impacts. As a result, the vulnerability to spatially spread events shows a markedly different geographical distribution. These findings, which should be universal for most road networks of similar scale, are important in the planning process of resource allocation for mitigation and recovery.     

Modal split in offshore supply network under the objective of emissions minimization

We study modal split under the objective of emissions minimization in the transportation of cargo from centralized vendors in the oil and gas industry to decentralized supply bases on the Norwegian coast. The supply network includes direct road transport and a sea route along the coast. To gain insight into modal split decisions between road and sea transport from the shipper’s perspective multi-period mixed integer optimization models are formulated. Particularly the models give possibilities to examine how weekly demand patterns at supply bases, cargo commitments to sea transport, storage possibilities at supply bases, and shipper’s responsibility for a certain share of vessel capacity may effect the emissions and the modal split. Experiments on real data from an oil and gas company operating offshore show that the size of the share of vessel capacity and the possibility for storage at supply bases are the major determinants for a larger shift to environmentally friendly sea transport. The models can be used as means for making decisions regarding how a shipper can commit to sea transport to achieve less emissions.     

Measuring supply chain risk: Predicting motor carriers’ ability to withstand disruptive environmental change using conjoint analysis

Supply chain risk measurement is an expanding research stream that considers the ability of networked firms to anticipate and respond to significant environmental risks, including major disruptions and unexpected events. However measuring and quantifying supply chain risk has proved an enormous challenge and this research contributes to this goal by developing a risk assessment scorecard, using conjoint analysis, for motor carrier firms. The resultant motor-carrier scorecard has been scaled from 300 to 900, to resemble the well-known FICO score for assessing consumer creditworthiness. Our scoring model enables motor carriers – and the firms that depend upon them in intermodal supply chains – to assess carriers’ ability to withstand major disruptive events, which are broadly defined as events which might lead to a significant drop in carriers’ income and profitability (e.g., such as that which occurred on September 11, 2001). Carriers with weaker risk scores (<600, on a 300–900 scale) are more likely to experience financial distress (and as a result possibly exit the industry itself); those with scores above 600 are less likely to depart. The model correctly identified 77 percent of motor carriers that ultimately exited the trucking industry following the significant environmental disruption caused by 9/11. Our computational experience indicates that the model accuracy, quantified in terms of Type I and Type II errors, compares favorably to prior results reported in the credit scoring literature.     

Reliable logistics networks design with facility disruptions

This paper studies a strategic supply chain management problem to design reliable networks that perform as well as possible under normal conditions, while also performing relatively well when disruptions strike. We present a mixed-integer programming model whose objective is to minimize the nominal cost (the cost when no disruptions occur) while reducing the disruption risk using the p-robustness criterion (which bounds the cost in disruption scenarios). We propose a hybrid metaheuristic algorithm that is based on genetic algorithms, local improvement, and the shortest augmenting path method. Numerical tests show that the heuristic greatly outperforms CPLEX in terms of solution speed, while still delivering excellent solution quality. We demonstrate the tradeoff between the nominal cost and system reliability, showing that substantial improvements in reliability are often possible with minimal increases in cost. We also show that our model produces solutions that are less conservative than those generated by common robustness measures.     

Disruption risk management in railroad networks: An optimization-based methodology and a case study

We propose an optimization-based methodology for recovery from random disruptions in service legs and train services in a railroad network. A network optimization model is solved for each service leg to evaluate a number of what-if scenarios. The solutions of these optimization problems are then used in a predictive model to identify the critical disruption factors and accordingly design a suitable mitigation strategy. A mitigation strategy, such as adding flexible or redundant capacity in the network, is an action that is deliberately taken by management in order to hedge against the cost and impact of disruption if it occurs. It is important that managers consider the trade-offs between the cost of mitigation strategy and the expected cost of disruption. The proposed methodology is applied to a case study built using the realistic infrastructure of a railroad network in the mid-west United States. The resulting analysis underscores the importance of accepting a slight increase in pre-disruption transportation costs, which in turn will enhance network resiliency by building dis-similar paths for train services, and by installing alternative links around critical service legs.     

不同主导权结构下的双渠道供应链定价策略及协调机制研究

基于制造商开辟线上直销渠道与线下零售渠道构成双渠道供应链的背景下,结合实际的服务影响因素,对不同主导权结构下双渠道供应链各成员的定价策略和协调机制进行研究。分别构建制造商主导、零售商主导、垂直均衡三种分散决策下的两阶段Stackelberg博弈模型,研究发现相较于集中决策,分散决策下供应链成员在价格、需求、利润上均有所损失。为缓解渠道之间的矛盾与冲突,引入一种双向收益共享契约用于协调供应链。研究表明,在一定的收益共享范围内,双向收益共享契约可以完美协调供应链;对于主导企业而言,主导权越向自身集中,所需共享的收益比例也越小;随着零售商服务水平的增大,制造商的利润不断增大,零售商的利润不断减小,供应链整体利润呈先增后减趋势。