Supply Chain Risk Mitigation

Transcription

1 TLI Asia Pacific White Papers Series Supply Chain Risk Mitigation Volume 13-Nov-RISK

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3 Supply Chain Risk Mitigation

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5 CONTENTS EXECUTIVE SUMMARY... 3 I. Risk mitigation Problem Statement Development of supply chain risk mitigation policies Evaluating the effectiveness of risk mitigation policies... 6 II. A new (r, Q) policy under supply chain disruptions Problem statement As-is Our Approach Novelty Benefits III. Supply Chain Risk Management with Demand Uncertainty: Bilevel Multi-criteria Game Models Problem statement As-is Model Description Novelty Benefits Management Insights

6 IV. Conclusion REFERENCES

7 EXECUTIVE SUMMARY Risk mitigation is a series of actions that are undertaken to limit any negative consequences of a particular event. After investigating and assessing the possible risks, responsibility would usually dictate that managers take a proactive role in coordinated activities to manage and control supply chain risks. In part I, risk mitigation approaches are identified which involve the development of mitigation policies and the evaluation. Then two studies are followed in parts II and III to demonstrate how the supply chain risks from uncertain supply and demand can be mitigated, respectively. 3

8 I. Risk mitigation 1. Problem Statement Risk mitigation activities are recommended to be conducted after identifying and assessing risks within risk management framework (ISO 31000:2009 Risk Management - Principles and guidelines). However, risk mitigation strategies should be designed and applied even before the disruption happens. Especially in a supply chain scenario, risks can be propagated and amplified in a supply chain network. It could be too late if we just act after we detect a risk since high losses normally incurred in the early stage of a disruption if no predictive plan is in place. So we need to apply risk mitigation policies not only after but also before a disruption. One important aspect to mitigate supply chain risk proactively is to build flexibility in the supply chain (Tang & Tomlin, 2008). While it is clear that flexibility enhances supply chain resiliency, it remains unclear how much flexibility is needed to mitigate supply chain risks. For example, maintaining extra capacity and inventory at some critical nodes of a supply chain is a mitigation strategy. The issues are how much and where we should allocate the redundancies. Therefore, we need to investigate risk mitigation strategies (policies) on how a supply chain organization can obtain significant strategic value by implementing a mitigation program. 2. Development of supply chain risk mitigation policies While there are many tactics for mitigating risks, it is important to know that the goal is not always about eliminating the risk, but to reduce the risk to a level that is acceptable to the firm and the focus of the risk mitigation strategy should be on creating controls that monitor and handle the identified risk. Most available techniques and strategies for supply chain risk mitigation generally fall under the following three broad categories: Risk alleviation by implementing controls to prevent an event Risk limitation by implementing strategies and controls to limit the likelihood of effects of risks Risk relief by reducing the loss of the risk. Most of the researchers suggest strategies to deal with supply chain disturbances focussing on the area of risk limitation and relief and the strategies contributing to the reduction in loss, probability, speed, frequency, and exposure to risk events. However, traditional risk management approaches should be enhanced to increase levels of resilience in the organization through the management of multiple variables. 4

9 Tang (2006) presented some robust strategies that possess two properties. First, these strategies will enable a supply chain to manage the inherent fluctuations efficiently regardless of the occurrence of major disruptions. Second, these strategies will make a supply chain more resilient in the face of major disruptions. A sample of different risks and possible mitigation strategies for handling supply chain risk (SCR) in a retail supply chain is presented by Oke & Gopalakrishnan and is shown in Figure 1 (Oke & Gopalakrishnan, 2009). Figure 1: Mitigation strategies employed in a retail supply chain (Oke & Gopalakrishnan, 2009) It is also important to note that a firm is never isolated and risk management becomes more effective when it includes the firm s strategic partners. This is seen in paper by Lavastre et al. (2011) where they present a ranked list of risk mitigation methods from a survey on French companies. Of the top five supply chain risk mitigation strategies that are viewed as effective and efficient, three involved working together with partners. Rank Methods to effectively and efficiently minimize risk 1 Communication and information exchange (forecasting, operational) 2 Accompanying providers/suppliers in improving their performance 3 Forecast accuracy 4 Long term continuity in relations with partners 5 Safety stocks (Vendor owned inventory (VOI) or in-house) 6 Establishment of emergency scenarios 7 Introduction of strict and formal procedures that are consistently respected 8 Activity planning using Advanced Planning System 9 External partner-owned safety stocks 10 Dual sourcing or manufacturing 11 Responsiveness due to Supply Chain Event Management 12 Introduction of sanctions and penalties for misconduct, faults, or mistakes 13 Centralization of operations (stocks, production and/or distribution) 14 Centralization of decision making 5

10 15 Reduction of number of suppliers 16 Geographical proximity to partners 17 Introduction of rewards in absence of misconduct or faults 18 Personal, friendly relationships with partners 19 Appointment of risk manager who convenes an SCRM group 20 Cultural proximity with partners 21 Presence of focal firm which coordinates supply chain Table 1: Ranking of risk mitigation methods (Lavastre, Gunasekaran, & Spalanzani, 2011) Based on the literature study, it can be seen that while there are costs for implementing these strategies, they provide additional selling points for acquiring and retaining apprehensive customers. However, the strategies are mostly just proposed and discussed in the papers, and there are not much model testing and case studies to justify the statements made. 3. Evaluating the effectiveness of risk mitigation policies Evaluation of a mitigation strategy s effectiveness should go beyond economic losses and include, but not restricted to, speed of recovery, extent of recovery and reduction in losses. One of the simplest and easily understandable methods is to consider the cost-benefit ratio of the risk mitigation strategy. Costs for a risk mitigation strategy include the cost of accepting the risk, reduction in operational effectiveness, implementation, and preparation in terms of employee education and training. Other methods that could be employed include resource gaps in the wake of an event and comparisons between the actual and estimated losses. Ni, Chen, & Chen (2010) suggested approaches on evaluating the effectiveness of mitigation strategies through plotting the assessment results on the same graph followed by selecting an appropriate graphical edition based on the arithmetic operation of the evaluation test. The graphical extensions are useful as they maintain the ease of both understanding and performing assessment from the risk matrix approach. Figure 2: Evaluation of risk mitigation policies (Ni, Chen, & Chen, 2010) The above proposed matrix approach could be used together with Value at Risk (VaR) to evaluate mitigation strategies. The approach would be to compare the difference in the reduction of potential loss 6

11 after each implementation. A further step of including the cost of implementation between mitigation strategies would also be helpful in deciding the mitigation strategy. Case Strategy Impleme New New New VaR Original ROI ntation Probabili Impact (L*I) VaR Cost ty (L) (I) 1 Increase safety stock amount in warehouse by 2 extra weeks % Source for alternate suppliers in region % Purchase futures to hedge against raw material cost increases % Table 2: Sample comparison of mitigation strategies using VaR Supply chain with different structures and mitigation strategies will present various performance behaviors with the impacts from disruptions. So the modeling of supply chain disruptions is important for testing mitigation strategies. Combining network modeling and agent-based simulation, the effectiveness of the mitigation strategies and the related disruption scenarios can be evaluated extensively. II. A new (r, Q) policy under supply chain disruptions 1. Problem statement We found at a global MNC server manufacturer that it is struggling to fulfill its demand commitments when any disruption happens at the suppliers end for the want of raw material or parts. Whenever a disruption like earthquake or flood happens at supplier s site, production gets hampered and supplier fails to deliver the items in time. As a result, the manufacturer s production schedule gets affected and it fails to satisfy the customer demand in time. These risks can result in a loss of business in tune of billions of dollars. Events like economic downturn, political unrest etc. also have a negative impact on customer demand and the manufacturer can lose business or incur more inventory cost if it does not modify its inventory control policies to incorporate these risks. For example, when Triple Disasters (earthquake, tsunami and nuclear) hit Japan in 2011, Apple faced shortage as its sub-supplier, ELectrotechno (Mitsubishi Gas Chemical Sub), was unable to provide BT resin. Before the disaster, ELectrotechno used to hold 50% of market share for global BT resin supply and like Apple, other manufacturers were also severely affected. Similarly, the Bangkok flood in 2011 affected the production capabilities of several hard disk drive (HDD) manufacturers and led to global shortage of HDDs and subsequently, a steep price rise for the HDDs. Therefore, impact of these disruptions can be 7

12 quite huge and effective techniques should be employed to handle these risks efficiently. An efficient inventory control policy ensures timely arrivals of items to the production facility and thus makes sure timely delivery of products to the customers. These, in turn, results in lower inventory at the manufacturer s site and also lesser backorders. The inventory control policy should be such that it can adjusts its parameters based on the changing supply chain environment so that losses can be averted. The existing inventory policy of the manufacturer does not take into account these risks and cannot adjusts its parameters based on the changing environment so that losses can be averted. The existing inventory policy places the order based on the inventory on hand at the time of order placement and demand forecasts during the lead time only. 2. As-is The manufacturer needs thousands of parts or components for its assembly process; some of them are very specialized and can only be supplied by a few specific suppliers. It was found that the majority of the items required are supplied by a single supplier and no alternative supplier is readily available. As a result, this supplier becomes very critical for the firm and proper inventory policies should be in place to timely arrival of parts and satisfy customer demand in time and reduce number of backlogs. The inventory policy is very susceptible to any supply delay risk or customer demand risk and results in more backorders or high inventory. It was also found that the manufacturer does not follow any traditional ordering policies like (r, Q) or (s, S) polices for ordering. The HQ generates demand forecast for each product at different locations for future periods. These forecasts include input from sales department for any swing in demand. Based on these demand forecasts and expected delivery time to the customers at different locations, the requirement for different items at next periods are estimated. Orders for items required at a future period are placed to the suppliers according to these estimates, in advance. When actual demand is realized at a period, the forecast for the next periods are adjusted and updated incorporating this new information. The updated demand forecasts are also sent to the suppliers and order quantities are adjusted accordingly. Items arrive at the warehouse (Hub) after a known lead time. These items are then assembled into the final product and is shipped to the customers at different locations. The final product reaches the customer after the transportation delay (depending on the location of the customer). The schematic diagram for the manufacturer s supply chain is shown in Figure 3. 8

13 Figure 3: Manufacturer s supply chain 3. Our Approach To overcome the drawbacks of the existing policy, we propose a modified (r, Q) policy when demand for the items is stochastic but time-varying; Q and r, respectively, represent ordering quantity and reorder point. It works as follows: r and Q values are computed at the beginning of each time period based on the forecasted demand and various cost parameters. The algorithm by Federgruen and Zheng (1992) is used to determine the values of r and Q, for that period and replenishment order is placed based on these values. When demand for that period is realized, the demand forecasts for the next planning periods are adjusted, by incorporating the realized demand. The r and Q values for the next period are recalculated based on the new demand forecasts and cost parameters for the new period (if different from the last period) and order is placed based on the adjusted r and Q values. We call this new proposed policy time-adjusted (r, Q) policy. Unlike traditional (r, Q) policies, r and Q values are not fixed for this policy, but varies from period to period. The new policy adjusts the parameter values at every time period, taking into consideration the adjusted demand forecast and any changes in the cost parameters. Mathematically, the policy is given by Qj( t) if IPj( t) rj( t) qj() t = 0 if IPj() t > rj() t where IPj(t) and qj(t) are the inventory position (inventory on hand + inventory on order backorders) and quantity ordered for item j at the beginning of time period t. 9

14 The policy is depicted in Figure 4. Q 3 Inventory Position r 2 r 3 r 1 Q 1 t 1 t 2 t 3 TR Figure 4: Time-adjusted (r, Q) ordering policy Time 4. Novelty The proposed time-adjusted (r, Q) policy removes the drawbacks of the existing policy by incorporating the elements of uncertainties into it. The policy is more dynamic in nature as it changes its parameters when customer demands changes or lead time of replenishment changes. Since the ordering quantity, Q and reorder point, r, determine the number of items the system will be carrying or number of backorders it will face and the number of orders to be placed, determination of Q and r is of utmost importance. The proposed policy also takes into account any changes in the cost parameters like holding cost or backorder cost and adjusts its parameters accordingly. Thus, the time-adjusted (r, Q) policy is more capable of adjusting itself when any disruptions occurs and can order more anticipating the disruption or order less anticipating less customer demand. The novelty of this proposed policy is its dynamism under uncertain environment. 5. Benefits We conducted some numerical experiments assuming demand to be normally distributed to show the benefits of implementing the time-adjusted (r, Q) policy over the existing policy. Random demands are generated and cost for both the policies are computed. Summary of finding for 100 samples are shown is Table 3. It can be seen that the average cost difference is % with a maximum of 29.35% and a minimum of 1.34%. The time-adjusted (r, Q) policy performs better than the existing policy for all the samples. 10

15 Average cost difference Maximum difference Minimum difference 1.34 Time- adjusted (r, Q) policy is better (%) 100 Existing policy is better (%) 0 Table 3: Comparison for two policies (normal demand) We also investigate how the benefit is sensitive to the changes in holding cost and ordering cost. Figure 5 graphically shows the behavior of cost improvement for various holding cost and ordering cost. Generally, the benefit increases with increasing ordering cost, but decreases with increasing holding cost % Improvement Holding cost rate Figure 5: Sensitivity analysis for ordering cost and holding cost We then study the performance of the policies when some disruptions happen. It can be seen from Figure 6 that time-adjusted (r, Q) policy always provides lower cost than the existing policy for all values of Lead time, though the difference starts getting lesser with increasing lead time. The time-adjusted (r, Q) policy places the replenishment order based on the lead time and due to its dynamic nature, it modifies the order quantity and reorder point and thus incurs lesser cost. When lead time starts getting higher, the proposed policy keeps more inventory than the existing policy (but places less orders) and inventory carrying cost increases, and thus, cost difference decreases. 11

16 AVERAGE LEAD TIME % OF TIME Average % (r,q) is better % Existing is better Figure 6: Impact of lead time uncertainty on cost difference When mean demand increases, time-adjusted (r, Q), cost difference increases steadily as evident in Figure 7; from % for mean =10 to 45.02% for mean = 50. The existing policy starts keeping more inventory when mean demand gets higher (sometimes more than twice compared to time-adjusted (r, Q) policy) and incurs more cost; whereas time-adjusted (r, Q) policy places more orders and keep less inventory to tackle this high demand. Average Improvement (%) Mean demand % of time Average % (r,q) policy is better % Existing policy is better Figure 7: Impact of demand variability on cost 12

17 III. Supply Chain Risk Management with Demand Uncertainty: Bilevel Multicriteria Game Models 1. Problem statement Demand uncertainty means that it is difficult to accurately predict customer demand in the future. This poses a significant challenge because it makes inventory hard to control and manage. In the multiple tiers supply chain, the demand uncertainty can even cause more serious results and leads to Bullwhip effect. Further, each decision-maker faces the competition from others. In the multiple tiers supply chain, leaders behave as Cournot firms with respect to other leaders, but as Stackelberg firms with respect to followers. So, for leaders, how to make decision about pricing and supplies with demand uncertainty is becoming very difficult. Followers need to make orders with both price given by leaders and demand uncertainty. We utilize a bilevel multi-criteria game model to address these problems. With this model, retailers can make their orders by considering the competition from other retailers, the uncertain demand and the price given by manufacturers. While manufacturers can make their decisions about theprice and supply by considering both the competition with other manufacturers and the orders. 2. As-is The multiple tiers supply chain has been extensively studied by utilizing game theory under stochastic demands. For example, Lau and Lau (2005) consider a two-echelon supply chain with one manufacturer and one retailer by game theory. They assume that a manufacturer wholesales a product to a retailer, who in turn retails it to the consumer under the stochastic demand. Corbett and Karmarkar (2001) first consider simultaneous quantity competition at multiple tiers in the supply chain with deterministic demands, then Adida and DeMiguel (2011) generalize this idea by presenting bilevel game models to study a two-echelon supply chain competition where multiple manufacturers who compete in quantities to supply a set of products to multiple risk-averse retailers who compete in quantities to satisfy the uncertain consumer demand. Recently, some researchers study supply chain competition with demand uncertainty by utilizing robust game theory. Harks and Miller (2011) study the resource allocation games by utilizing the worst-case cost sharing methods in terms of the ratio of the minimum guaranteed surplus of a Nash equilibrium and the maximal surplus. They show that an upper bound on the efficiency can be found. Jiang, Netessine and Savin (2011) study the robust Newsvendor competition under asymmetric information. They show the existence and derive closed-form expressions for the robust optimization Nash equilibrium solution for a game with an arbitrary number of players. Wadecki, Babich and Wu (2012) study the optimal subsidy decisions of manufacturers in four supply chain structures and show that competing manufacturers face an important tradeoff. Our models extend the above supply chain competition models to simultaneous competition among manufacturers and retailers, product and 13

18 retailer differentiation, and retailer risk aversion. These methods mainly consider that there is one objective to be optimized for each player (manufacturer, retailer or consumer) in the supply chain. However in the real-world supply chain, decision-making processes always have several social concerns and thus often have more than one conflicting objectives to be optimized simultaneously. So it is interesting to study supply chain competition by multi-criteria game theory. 3. Model Description We consider to model multi-criteria competition in a supply chain which contains M manufacturers who compete in quantities to supply products to N risk-averse retailers who compete in quantities to satisfy the uncertain consumer demand (see Figure 8). In this model each manufacturer has two objectives (i.e., maximize profit and minimize cost for the supply products) by choosing supply quantities for each of the products under the anticipation of the order quantities of the retailers and also the anticipation of the wholesale prices resulting from the market clearing conditions. While each retailer has also two objectives (i.e., maximize expected utility from retail sales and minimize the risk expressed as the standard deviation of profit) by deciding wholesale market order quantities for each of the P products. Figure 8: The structure of the supply chain Then a multi-criteria bilevel game (MBG) model for describing supply chain competition can be defined as follows: the th manufacturer faces the following decision problems, 14

19 where is a Pareto Nash equilibrium to the retailers' multi-criteria game, where is a stochastic vector inverse demand function of retailer j which depends on the stochastic variable, is the standard deviation of. Then we define that a mixed strategy is a weighted manufacturer-retailer equilibrium of the above MBG with the given weight if is a Pareto Nash equilibrium to the retailers' multi-criteria game and is a weighted Nash equilibrium to the manufacturers' multi-criteria game. 4. Novelty A multi-criteria bilevel game model is proposed for analysing the competition in a supply chain which contains multiple suppliers who compete in quantities to supply a set of products to multiple risk-averse retailers who compete in quantities to satisfy the uncertain consumer demand. This model contains the information about uncertain demands and the competition not only between manufacturer and retailer, but also among manufacturers and retailers. Each player in this model has two objectives to optimize. 5. Benefits This model can be used to coordinate the supply chain, helping manufacturers make decisions about pricing and supplies. To solve this model, a robust weighted approach can be proposed and the closedform solution to the robust weighted manufacturer-retailer equilibrium can be derived. 6. Management Insights 1. The efficiency of the decentralized supply chain may drop rapidly when manufacturers are asymmetric (i.e. they have different costs) or retailers are asymmetric (i.e. they have different risk aversion). 2. The asymmetry of product assortments at different retailers leads to a decrease in the degree of competition among retailers. 15

20 3. With different choice of weights, there are different weighted Nash equilibria. However a robust weighted approach can lead to a unique robust weighted Nash equilibira. 4. The efficiency of the decentralized supply chain may drop sharply with the asymmetry of the manufacturers because whereas in the centralized supply chain only the cheapest manufacturers produce, in the decentralized chain all manufacturers may produce. IV. Conclusion In this article, we first describe the approach for supply chain risk mitigation involves 1) development of mitigation policies and evaluating the effectiveness of policies. We also highlight that there are two important aspects in the supply chain risk mitigation: building in robustness and resiliency. In the second part, a novel time-adjusted inventory policy is proposed to solve the risk caused by supply uncertainty. The new policy performs better than the existing policy for all the samples. In the third part, a bilevel multi-criteria game model is used to address demand uncertainty considering multiple performance criterions. 16

21 REFERENCES Federgruen, A. &Y.-S. Zheng (1992). An Efficient Algorithm for Computing an Optimal (r, Q) Policy in Continuous Review Stochastic Inventory Systems.Operations Research 40(4): Lavastre, O., Gunasekaran, A., & Spalanzani, A. (2011). Supply chain risk management in French companies. Decision Support Systems. Ni, H., Chen, A., & Chen, N. (2010). Some extensions on risk matrix approach. Safety Science 48, Oke, A., & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail supply chain. International Journal of Production Economics, Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics 103, Tang, C. S., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics 116,

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