We study a single-product setting in which a firm can source from two suppliers, one that is unreliable and another that is reliable but more expensive. Suppliers are capacity constrained, but the reliable supplier may possess volume flexibility. We prove that in the special case in which the reliable supplier has no flexibility and the unreliable supplier has infinite capacity, a risk-neutral firm will pursue a single disruption-management strategy: mitigation by carrying inventory, mitigation by single-sourcing from the reliable supplier, or passive acceptance. We find that a supplier's percentage uptime and the nature of the disruptions (frequent but short versus rare but long) are key determinants of the optimal strategy. For a given percentage uptime, sourcing mitigation is increasingly favored over inventory mitigation as disruptions become less frequent but longer. Further, we show that a mixed mitigation strategy (partial sourcing from the reliable supplier and carrying inventory) can be optimal if the unreliable supplier has finite capacity or if the firm is risk averse. Contingent rerouting is a possible tactic if the reliable supplier can ramp up its processing capacity, that is, if it has volume flexibility. We find that contingent rerouting is often a component of the optimal disruption-management strategy, and that it can significantly reduce the firm's costs. For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare.supply uncertainty, dual-sourcing, volume flexibility

Lee (2004) articulated that alignment, adaptability, and agility are the basic ingredients for managing supply chain risks. While it is clear that flexibility (agility) enhances supply chain resiliency, it remains unclear how much flexibility is needed to mitigate supply chain risks. Without a clear understanding of the benefit associated with different levels of flexibility, firms are reluctant to invest in flexibility especially when reliable data and accurate cost and benefit analysis are difficult to obtain. In this paper, we present a unified framework and 5 stylized models to illustrate that firms can obtain significant strategic value by implementing a risk reduction program that calls for a relatively low level of flexibility. Some of our model analyses are based on or motivated by models presented in recent literature. Our findings highlight the power of flexibility, and provide convincing arguments for deploying flexibility to mitigate supply chain risks.

Industry 4.0 connotes a new industrial revolution centered around cyber-physical systems. It posits that the real-time connection of physical and digital systems, along with new enabling technologies, will change the way that work is done and therefore, how work should be managed. It has the potential to break, or at least change, the traditional operations trade-offs among the competitive priorities of cost, flexibility, speed, and quality. This article describes the technologies inherent in Industry 4.0 and the opportunities and challenges for research in this area. The focus is on goods-producing industries, which includes both the manufacturing and agricultural sectors. Specific technologies discussed include additive manufacturing, the internet of things, blockchain, advanced robotics, and artificial intelligence.

Surveys suggest that supply chain risk is a growing issue for executives and that supplier reliability is of particular concern. A common mitigation strategy observed in practice is for the buying firm to expend effort improving the reliability of its supply base. We explore a model in which a firm can source from multiple suppliers and/or exert effort to improve supplier reliability. For both random capacity and random yield types of supply uncertainty, we propose a model of process improvement in which improvement efforts (if successful) increase supplier reliability in the sense that the delivered quantity (for any given order quantity) is stochastically larger after improvement. We characterize the optimal procurement quantities and improvement efforts, and generate important insights. For random capacity, improvement is increasingly favored over dual sourcing as the supplier cost heterogeneity increases but dual sourcing is favored over improvement if the supplier reliability heterogeneity is high. In the random yield model, increasing cost heterogeneity can reduce the attractiveness of improvement, and improvement can be favored over dual sourcing if the reliability heterogeneity is high. A combined strategy (improvement and dual sourcing) can provide significant value if suppliers are very unreliable and/or capacity is low relative to demand.

We connect the mix-flexibility and dual-sourcing literatures by studying unreliable supply chains that produce multiple products. We consider a firm that can invest in product-dedicated resources and totally flexible resources. Product demands are uncertain at the time of resource investment, and the products can differ in their contribution margins. Resource investments can fail, and the firm may choose to invest in multiple resources for a given product to mitigate such failures. In comparing a single-source dedicated strategy with a single-source flexible strategy, we refine the common intuition that a flexible strategy is strictly preferred to a dedicated strategy when the dedicated resources are costlier than the flexible resource. We prove that this intuition is correct if the firm is risk neutral or if the resource investments are perfectly reliable. The intuition can be wrong, however, if both of these conditions fail to hold, because there is a resource-aggregation disadvantage to the flexible strategy that can dominate the demand pooling and contribution-margin benefits of the flexible strategy when resource investments are unreliable and the firm is risk averse. We investigate the influence that resource attributes, firm attributes, and product-portfolio attributes have on the attractiveness of various supply-chain structures that differ in their levels of mix flexibility and diversification, and we investigate the influence these attributes have on the optimal resource investments within a given supply-chain structure. Our results indicate that the appropriate levels of diversification and flexibility are very sensitive to the resource costs and reliabilities, the firm's downside risk tolerance, the number of products, the product demand correlations and the spread in product contribution margins.reliability, flexibility, dual sourcing, loss aversion, risk

Process flexibility, whereby a production facility can produce multiple products, is a critical design consideration in multiproduct supply chains facing uncertain demand. The challenge is to determine a cost-effective flexibility configuration that is able to meet the demand with high likelihood. In this paper, we present a framework for analyzing the benefits from flexibility in multistage supply chains. We find two phenomena, stage-spanning bottlenecks and floating bottlenecks, neither of which are present in single-stage supply chains, which reduce the effectiveness of a flexibility configuration. We develop a flexibility measure g and show that increasing this measure results in greater protection from these supply-chain inefficiencies. We also identify flexibility guidelines that perform very well for multistage supply chains. These guidelines employ and adapt the single-stage chaining strategy of Jordan and Graves (1995) to multistage supply chains.Supply Chain, Flexibility, Capacity, Product Allocation

The contract manufacturing industry has grown rapidly in recent years as firms have increasingly outsourced production to reduce costs. This growth has created powerful contract manufacturers (CMs) in several industries. Achieving a competitive cost position is often a primary motive for outsourcing. Outsourcing influences both the original equipment manufacturer's (OEM) and the CM's production levels, and, therefore, through learning‐by‐doing renders future costs dependent on past outsourcing decisions. As such, outsourcing should not be viewed as a static decision that, once made, is not revisited. We address these considerations by analyzing a two‐period game between an OEM and a powerful CM wherein both firms can reduce their production costs through learning‐by‐doing. We find that partial outsourcing, wherein the OEM simultaneously outsources and produces in‐house, can be an optimal strategy. Also, we find that the OEM's outsourcing strategy may be dynamic—i.e., change from period to period. In addition, we find both that the OEM may engage in production for leverage (i.e., produce internally when at a cost disadvantage) and that the CM may engage in low balling. These and other findings in this paper demonstrate the importance of considering learning, the power of the CM, and future periods when making outsourcing decisions.

Disruptive events that halt production can have severe business consequences if not appropriately managed. Business interruption (BI) insurance offers firms a financial mechanism for managing their exposure to disruption risk. Firms can also avail of operational measures to manage the risk. In this paper we explore the relationship between BI insurance and operational measures. We model a manufacturing firm that can purchase BI insurance, invest in inventory and avail of emergency sourcing. Allowing the insurance premium to depend on the firm's insurance and operational decisions, we characterize the optimal insurance deductible and coverage limit as well as the optimal inventory level. We prove that insurance and operational measures are not always substitutes, and establish conditions under which they can be complements; that is, insurance leads to a higher inventory investment and/or a larger benefit from emergency sourcing. We also find that the value of insurance is higher for those firms less able to absorb the post-disruption consequences of financially significant disruptions. As disruptions become longer-but-rarer, the value of emergency sourcing increases, and the value of inventory and the value of insurance increase before eventually decreasing.