Risk Mitigation Strategies in Semi-Organic Rice Supply Chains: Lesson Learned from the Involved Actors

Rice is the main consumption food for Indonesians. The demand for food increased from 114.6 kg per capita in 2016 to 124.89 kg in 2017. However, rice farmers and supply chain actors in rice agribusiness have experienced high challenges, such as production, transportation, price, product quality, and the environment. This research aimed to understand actors involved in the supply chain, their perception of occurring risks, and evaluation and risk mitigation in the supply chain. This was a quantitative descriptive study done purposively in Watugede Village, Singosari Sub-District, Malang Regency. Non-probability sampling was taken to gather primary data. The respondent of this research was 16 involved actors, from on-farm actors to consumers. The data were analyzed using the Fuzzy analytical hierarchy process (FAHP) to provide descriptive risk mitigation strategies. The results show that six involved actors are suppliers, farmers, grinders, traders, and buyers. Each actor faces different risks, and thus, the recommended mitigation strategies are adjusted to their risks. Sharing information, optimizing the level of supply availability, measuring supply chain performance, and building more coordination with the government are the best strategies to mitigate risks

Dynamic Pricing and Inventory Management with Dual Suppliers of Different Lead Times and Disruption Risks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109985/1/poms12221-sup-0001-OnlineSupplement.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109985/2/poms12221.pd

Optimal Learning Algorithms for Stochastic Inventory Systems with Random Capacities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156225/2/poms13178_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156225/1/poms13178.pd

Integrating Dynamic Pricing and Replenishment Decisions Under Supply Capacity Uncertainty

This paper examines an integrated decision-making process regarding pricing for uncertain demand and sourcing from uncertain supply, which are often studied separately in the literature. Our analysis of the integrated system suggests that the base stock list price policy fails to achieve optimality even under deterministic demand. Instead, the optimal policy is characterized by two critical values: a reorder point and a target safety stock. Under this policy, a positive order is issued if and only if the inventory level is below the reorder point. When this happens, the optimal order and price are coordinated to achieve a constant target safety stock, which aims at hedging the demand uncertainty. We further investigate the profit improvement obtained from deploying dynamic pricing, as opposed to static pricing. Our results indicate that either supply limit or supply uncertainty may induce a significant benefit from dynamic pricing, and the compound effect of supply limit and uncertainty can be much more pronounced than the individual effects. Whether or not the supply capacity is limited has a major implication on the value of dynamic pricing. Under unlimited supply, dynamic pricing is more valuable when procurement cost is high or when demand is more sensitive to price. With limited supply, however, the capacity restriction tends to be relaxed, reducing the value of dynamic pricing.inventory, stochastic, policies, pricing

Supply Chain Contracting in the Presence of Supply Uncertainty and Store Brand Competition

In today\u27s complex business environment, manufacturers are striving to maintain a competitive advantage over their supply chain partners. Manufacturers\u27 profitability is tightly linked to their strategic interactions with other entities in the supply chain. While numerous studies have been conducted to investigate such interactions in supply chains, certain issues remain unresolved. We apply a game-theoretic framework to analyze two distinct supply chain structures in the presence of supply uncertainty and store brand competition in two essays, respectively. In the first chapter, we study a decentralized assembly supply chain under supply uncertainty. In a decentralized assembly supply chain, one assembler assembles a set of $n$ components, each produced by a different supplier, into a final product to meet an uncertain market demand. Each supplier faces an uncertain production capacity such that only the lesser of the planned production quantity and the realized capacity can be delivered to the assembler. We assume that the suppliers\u27 random capacities and the random demand can follow an arbitrary continuous multivariate distribution. We formulate the problem as a two-stage Stackelberg game. The assembler and the suppliers adopt a so-called Vendor-Managed-Consigned-Inventory (VMCI) contract. We analytically characterize the equilibrium of this game, based on which we obtain several managerial insights. Surprisingly, we show that when a supplier\u27s production cost increases or when his component salvage value decreases, it hurts all other members and the entire supply chain, but it might sometimes benefit this particular supplier. Similarly, when the suppliers do not have supply uncertainty, it benefits the assembler but it does not necessarily benefit the suppliers. Furthermore, we demonstrate that when the suppliers\u27 capacities become more positively correlated, the assembler is always better off, but the suppliers might be better or worse off. Later in the chapter, we also solve the game under the conventional wholesale-price contract. We find that the assembler always prefers the VMCI contract, and the suppliers always prefer the wholesale price contract. In addition, we illustrate that the VMCI contract is more efficient than the wholesale price contract for this decentralized assembly supply chain. In the second chapter, we consider a two-tier decentralized supply chain with a national brand supplier and a retailer. The national brand supplier (she) distributes her products to consumers through the retailer. Meanwhile, the retailer (he) intends to develop and produce his own store brand through a manufacturing source that is different from the national brand supplier. The retailer holds the store brand production unit cost as private information, for which the national brand supplier only has a subjective assessment. Given a supply contract offered by the national brand supplier, the retailer simultaneously decides whether to accept the contract and whether to produce the store brand. The national brand supplier aims to design an optimal menu of contracts to maximize her expected profit as well as extract the retailer\u27s private cost information. We formulate the problem as a two-stage screening game to analyze the strategic interaction between the two players. Despite the inherent computational complexity, we are able to derive the optimal menu of contracts for the national brand supplier, of which the format depends on the national brand supplier\u27s unit production cost. Furthermore, we investigate how the model parameters affect the value of information for each member in the supply chain. We show that the retailer\u27s private cost information becomes less valuable to both the national brand supplier and the retailer when the national brand unit production cost increases. We also illustrate that when the gap between the two possible cost values increases, the private cost information becomes more valuable to the national brand supplier, however the value of information to the retailer himself can either increase or decrease. Finally, we demonstrate that when the perceived quality of the national brand increases, the value of information to the retailer first decreases then increases, but the impact on the value of information to the national brand supplier can be either positive or negative

Investment decision-making in clean energy under uncertainties: A real options approach

International commitments on emission reduction and the deterioration of fossil energy resources have caused more research attention to clean energy production. Getting the optimal investment portfolio in infrastructure for energy supply and consumption is a minimum requirement to enable the transition towards a sustainable energy system. Due to their environmental benefits, advanced biofuel and clean power generation are expected to play an important role in the future in transportation sector and electricity sector, respectively. In this dissertation, a real options approach is adopted for valuating clean technology investment portfolios under uncertainty, exploring managerial insights, and examining policy implications. The dissertation consists three parts discussing problems on clean energy investment. Biofuel production investment, motivated by consumption volume mandates in revised Renewable Fuel Standard, is a long-term irreversible investment facing revenue uncertainty given volatile fuel market. Iowa, rich in agricultural residues like corn stover, is a major player in the fulfillment of the cellulosic biofuels mandate. In this first part, we aim to answer the question: Is now a good time for Iowa to start investing in cellulosic biofuels? Using a fast pyrolysis facility as an example, we present a real options approach for valuating the investment of a new technology for producing cellulosic biofuels subject to construction lead time and uncertain fuel price. We conduct a case study, in which the profitability of the project, optimal investment timing, and the impact of project lead time are investigated. The second part extended the previous work by incorporating supply risk and dual sourcing. While corn stover is an abundant source of feedstock for biofuels production in Iowa, there is a potential supply risk due to the following reasons: (1) lack of market; (2) low percentage of farm participation; and (3) yield uncertainty due to the changing weather conditions. The decision maker would consider investing in a land to grow his own feedstock, in addition to the investment of biofuel facility. Land option with the growing of dedicated energy crops has a value-adding effect when operating with the fast pyrolysis facility. And with dual sourcing, the impact from supply uncertainty could be mitigated. A real options approach is used to analyze the optimal investment timing and benefits of the dual sourcing. Risk-aversion has an unexpected effect on investment decision-making, which may cause the investment decision of the value-adding option can be very sensitive to the primary underlying uncertainty, and the immediate action towards land investment can no longer be described with a single fuel price threshold. Policy is deemed as one of the top decisive external factor that impacts the interest of a power producer. All energy projects are prone to policy risk, yet such eventualities are difficult to predict and therefore expensive to insure. In the third part of the study, we extend the uncertainty to the scope of government policy, in addition to considering the critical uncertainty of commodity prices. In this work, we want to examine the timing that an owner of a traditional coal-fired generator adopts in a clean technology when facing two realistic policy uncertainty cases: risk of repealing an existing policy, and risk of a policy change. The investment of a natural gas generator is considered in order to meet the load obligation while maximizing its expected long-run profit with regulated emission-related costs considered. The price uncertainties in electricity, natural gas, and carbon emission, together with policy uncertainty jointly affect profitability and decision-making of the clean technology adoption. A real options approach is applied to investigate the optimal investment decision. The producers are risk avoiding when facing uncertain future policy environment; and this reflects in delaying investment plan and creating a future investment plan that is stubborn to current carbon price. To a risk-neutral price-taking power producer, emission trading is a more effective instrument compared to carbon tax, and shifting from carbon tax to emission permits could more effectively inducing immediate investment in clean technology

Essays on Global Sourcing under Uncertainty

In this dissertation, we study the sourcing policies of global corporations and determine the key drivers of the procurement decisions under different types of uncertainties. The first essay explores the impact of exchange-rate and demand uncertainty on sourcing decisions of a multinational firm which engages in global sourcing through capacity reservation contracts. The focus of this essay is cost, which is known to be the main driver of global sourcing practices. We investigate the impact of cost uncertainty caused by exchange-rate fluctuations on procurement decisions, and identify the conditions that result in single and dual sourcing policies. Our analysis indicates that although cost is an order qualifier when exchange rate is considered deterministic, lower expected sourcing cost is neither necessary nor sufficient to source from a supplier under exchange-rate uncertainty. The second essay examines sourcing and pricing decisions of an agricultural processor encountering yield uncertainty of the agricultural input required for its offered specialty product and the price uncertainty of the competing commercial product. We show that uncertainty gives rise to a conservative sourcing policy which would never emerge in a deterministic setting. While both studies highlight the significant impact of uncertainty on the business decisions and performance, they demonstrate that the effect of uncertainty may take opposite directions contingent upon the business environment and the type of uncertainty. The operational environment studied in the first essay, provides an opportunity for the firm to benefit from exchange-rate fluctuations, whereas the variation in supply and the market price of the competing product are shown to diminish the firm’s expected profit in the agricultural setting studied in the second essay. Demonstrating the opposing behavior under different forms of uncertainty, this study recommends managers to think deeply about the impact of uncertainty on their businesses. It also provides various forms of prescriptions to mitigate risk and operate effectively under each uncertainty

Data Driven Optimization: Theory and Applications in Supply Chain Systems

Supply chain optimization plays a critical role in many business enterprises. In a data driven environment, rather than pre-specifying the underlying demand distribution and then optimizing the system’s objective, it is much more robust to have a nonparametric approach directly leveraging the past observed data. In the supply chain context, we propose and design online learning algorithms that make adaptive decisions based on historical sales (a.k.a. censored demand). We measure the performance of an online learning algorithm by cumulative regret or simply regret, which is defined as the cost difference between the proposed algorithm and the clairvoyant optimal one. In the supply chain context, to design efficient learning algorithms, we typically face two major challenges. First, we need to identify a suitable recurrent state that decouples system dynamics into cycles with good properties: (1) smoothness and rich feedback information necessary to apply the zeroth order optimization method effectively; (2) convexity and gradient information essential for the first order methods. Second, we require the learning algorithms to be adaptive to the physical constraints, e.g., positive inventory carry-over, warehouse capacity constraint, ordering/production capacity constraint, and these constraints limit the policy search space in a dynamic fashion. To design efficient and provably-good data driven supply chain algorithms, we zoom into the detailed structure of each system, and carefully trade off between exploration and exploitation.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150030/1/haoyuan_1.pd

Managing Emerging Market Operations

Emerging markets have been a critical part of global business, with high share of global GDP and rapid economy growth. My dissertation research focuses on studying risks and opportunities in emerging market operations. One critical characteristic of emerging markets is that agriculture remains an essential sector. The world looks to emerging countries to meet the increasing food demand. However, the output remains significantly below the potential due to limited financial, technology and policy support. Scientific agriculture such as effective planting and mechanization could potentially help farmers achieve higher yields. In the first chapter of my dissertation, we study the optimal seeding policy under rainfall uncertainty. Utilizing field weather data from Southern Africa, we investigate the advantage of the optimal planting schedule and the impact of climate conditions on this advantage in a real-size large-scale problem. Another critical characteristic of emerging markets is the low labor cost. This makes emerging markets attractive bases for global manufacturing and service operations. However, the globalization of supply chains complicates the logistics and procurement operations. In the second chapter, we focus on the warehouse outsourcing strategy in global supply chains. We establish the optimal warehousing strategy and demonstrate that excluding the logistics dynamics from contracting and making warehousing decisions unilaterally afterwards can lead to a suboptimal warehousing strategy for the retailer. Furthermore, a variety of threats such as supplier failure and transportation disruption could delay or even disrupt the operations, offsetting the low-cost benefit of emerging economies. In the third chapter, we study the optimal sourcing strategy under disruption in global supply chains. We establish the optimal sourcing strategy and provide insights on the roles of the nearshore supplier in response to supply chain disruption. Overall, my dissertation concentrates on the application of scientific methods to planting and farm machinery procurement to improve agricultural productivity in Africa and leveraging low-cost benefits in emerging markets.Doctor of Philosoph