Question: ILLUSTRATION: FROM SUPERSTORMS TO FACTORY FIRES MANAGING UNPREDICTABLE SUPPLY-CHAIN DISRUPTIONS BY DAVID SIMCHI-LEVI, WILLIAM SCHMIDT, AND YEHUA WEI TRADITIONAL METHODS FOR managing supply chain risk rely on knowing the likelihood of occurrence and the magnitude of impact for every potential event that could materi- ally disrupt a firm’s operations. ForSee the answerSee the answerSee the answer done loadingPlease summarize below.
Show transcribed image textANSWER From Superstorms to Factory Fires: Managing Unpredictable Supply-Chain Disruptions By David Simchi-Levi, William Schmidt, and Yehua Wei . SUMMARY Traditional methods of managing supply chain risk require estimations of how likely a disruption…View the full answerTranscribed image text: ILLUSTRATION: FROM SUPERSTORMS TO FACTORY FIRES MANAGING UNPREDICTABLE SUPPLY-CHAIN DISRUPTIONS BY DAVID SIMCHI-LEVI, WILLIAM SCHMIDT, AND YEHUA WEI TRADITIONAL METHODS FOR managing supply chain risk rely on knowing the likelihood of occurrence and the magnitude of impact for every potential event that could materi- ally disrupt a firm’s operations. For common supply-chain disruptions- poor supplier performance, forecast errors, transportation breakdowns, and so on-those methods work very well, using historical data to quantify the level of risk. To address this challenge, we developed a model-a mathematical description of the supply chain that can be computerized-that focuses on the impact of potential failures at points along the sup- ply chain (such as the shuttering of a supplier’s fac- tory or a flood at a distribution center), rather than the cause of the disruption. This type of analysis ob- viates the need to determine the probability that any specific risk will occur-a valid approach since the mitigation strategies for a disruption are equally ef- fective regardless of what caused it. Using the model, companies can quantify what the financial and op- erational impact would be if a critical supplier’s fa- cility were out of commission for, say, two weeks- whatever the reason. The computerized model can be updated easily and quickly, which is crucial since supply chains are in a continual state of flux. But it’s a different story for low- probability, high-impact events- megadisasters like Hurricane Katrina in 2005, viral epidemics like the 2003 SARS outbreak, or major outages due to unforeseen events such as factory fires and political upheavals. Because historical data on these rare events are limited or nonexis- tent, their risk is hard to quantify using traditional models. As a result, many companies do not ade- quately prepare for them. That can have calamitous consequences when catastrophes do strike and can force even operationally savvy companies to scram- ble after the fact-think of Toyota following the 2011 Fukushima earthquake and tsunami. In developing and applying our model at Ford Motor Company and other firms, we were surprised to find little correlation between how much a firm spends annually on procurement at a particular site and the impact that the site’s disruption would have on company performance. Indeed, as the Ford case study described later in this article shows, the great- est exposures often lie in unlikely places. In practice, that means that leaders using tra- ditional risk-management techniques and simple LA B heuristics (dollar amount spent at a site, for instance) often end up focusing exclusively on the so-called strategic suppliers for whom expenditures are very high and whose parts are deemed crucial to product differentiation, and overlooking the risks associated with low-cost, commodity suppliers. As a result, managers take the wrong actions, waste resources, and leave the organization exposed to hidden risk. In this article, we describe our model and how compa- nies can use it to identify, manage, and reduce their exposure to supply chain risks. Time to Recovery and the Risk Exposure Index A central feature of our model is time to recovery (TTR): the time it would take for a particular node (such as a supplier facility, a distribution center, or a transportation hub) to be restored to full functional- ity after a disruption. TTR values are determined by examining historical experience and surveying the firm’s buyers or suppliers (see the sidebar “Assessing Impact? Use a Simple Questionnaire”). These values can be unique for every node or can differ across a subset of the nodes. Our model integrates TTR data with informa- tion on multiple tiers of supplier relationships, bill- of-material information, operational and financial Impact of Supplier Disruptions on Ford’s Profits The sites whose disruption would cause the greatest damage are those from which Ford’s annual purchases are relatively small. Ford had not previously identified many of them as high-exposure suppliers. (Data have been dis- guised to protect sensitive competitive information.) $90,000 80,000 70,000 Low-cost, commodity suppliers are often overlooked by risk managers. 60,000 50,000 40,000 30,000 20,000 10,000 0 10,000 60,000 $260,000 110,000 160,000 210.000 FINANCIAL IMPACT OF A DISRUPTION (LOST PROFIT) TOTAL ANNUAL SPEND AT SUPPLIER SITE measures, in-transit and on-site inventory levels, and demand forecasts for each product. Firms can represent their entire supply network at any level of detail-from individual parts to aggregations based on part category, supplier, geography, or product line. This allows managers to drill down into greater detail as needed and identify previously unrecog- nized dependencies. The model can account for disruptions of varying severity by running scenarios using TTRS of different durations. To conduct the analysis, the model removes one node at a time from the supply network for the dura- tion of the TTR. It then determines the supply chain response that would minimize the performance impact of the disruption at that node-for instance, drawing down inventory, shifting production, ex- pediting transportation, or reallocating resources. On the basis of the optimal response, it generates a financial or operational performance impact (PI) for the node. A company can choose different measures of PI: lost units of production, revenue, or profit mar- gin, for instance. The model analyzes all nodes in the network, assigning a PI to each. The node with the largest PI (in lost sales, for instance, or lost units of production) is assigned a risk exposure index (REI) score of 1.0. All other nodes’ REI scores are indexed relative to this value (a node whose disruption would cause the least impact receives a value close to zero). The indexed scores allow the firm to iden- tify at a glance the nodes that should get the most attention from risk managers. At its core, the model uses a common mathemat- ical technique-linear optimization to determine the best response to a node’s being disrupted for the duration of its TTR. The model accounts for exist- ing and alternative sources of supply, transportation, inventory of finished goods, work in progress and raw material, and production dependencies within the supply chain. Our approach provides a number of benefits. It: Identifies hidden exposures. The model helps managers identify which nodes in the network cre- ate the greatest risk exposure-often highlighting previously hidden or overlooked areas of high risk. It also allows the firm to compare the costs and ben- efits of various alternatives for mitigating impact. Avoids the need for predictions about rare events. The model determines the optimal re- sponse to any disruption that might occur within the supply network, regardless of the cause. Rather than trying to quantify the likelihood that a low- Idea in Brief THE PROBLEM Traditional tools for analyzing supply chain risks require assessments of whether something is likely to happen, and the magnitude of its impact. probability, high-risk event will strike, firms can focus on identifying the most important exposures and putting in place risk-management strategies to mitigate them. Reveals supply chain dependencies and bottlenecks. Companies can also use the analyses to make inventory and sourcing decisions that in crease the robustness of the network. This includes taking into account the likely scramble among rival companies to lock in alternative sources if a suppli- er’s disruption affects several firms. Such cross-firm effects of a crisis are often overlooked. Contracts with backup suppliers can be negotiated to give a company priority over others should a disruption with the primary supplier occur, which would de- crease time to recovery and financial impact. Promotes discussion and learning. In the course of analyzing the supply chain in this way, managers engage in discussions with suppliers and internal groups about acceptable levels of TTR for critical facilities and share insights about best- practice processes to reduce recovery time. As a re- sult, the impact of disruptions is minimized. Prescriptive Actions Our model provides organizations with a quantita- tive metric for segmenting suppliers by risk level. Us- ing data generated by the model, we can categorize suppliers along two dimensions: the total amount of money that the company spends at each supplier site in a given year, and the performance impact on the firm associated with a disruption of each sup- plier node. Let’s now take a look at the supplier seg- ments and consider the risk-management strategies appropriate for each. Obvious high risk. Most companies focus their risk-management activities on suppliers for whom total spend and performance impact are both high. Typically, these are the suppliers of expensive com- WHY THIS HAPPENS A large class of risks-such as tsunamis, pandemics, and strikes-can’t be assessed in this way. THE SOLUTION The authors have developed a model for determining the impact that a disruption of each node in its supply chain would have, regardless of its cause or likelihood. It uncovers risks that other models don’t, including dangers posed by suppliers of low-cost commodities and the lack of correlation between the impact of a site disruption and dollar amount that the firm spends at that site. ponents, such as car seats and instrument panels, that strongly affect customers’ purchase decisions and experience. The cost of these “strategic com- ponents,” as they’re frequently called, often make up a large portion of the total manufacturing cost. Indeed, for many companies, they represent 20% of the suppliers but account for about 80% of a firm’s total procurement expenditures. Because strategic components typically come from a single supplier, appropriate risk-mitigation strategies include stra- tegic partnering with the suppliers to analyze and reduce their risk exposure, providing incentives to some suppliers to have multiple manufacturing sites in different regions, tracking suppliers’ perfor- mance, and developing and implementing business continuity plans. Low risk. Suppliers with low total spend and low financial impact do not require intense risk-manage- ment investment. In our experience, most compa- nies effectively manage the minimal risks from dis- ruptions of these supplier sites by investing in excess inventory or negotiating long-term contracts with a penalty clause for nonperformance. Hidden risk. Many companies, however, are subject to considerable exposure from “hidden risk” suppliers. Here, total spend is low but the financial impact of a disruption is high. Even the savviest managers are prone to equating total spend with performance impact: They rightly identify strategic components as carrying high levels of supply chain risk, but fail to consider that low-spend suppliers, of- ten of commodity goods, may represent outsize risks. Traditional risk-assessment exercises overlook these components because they are perceived as adding little value to the firm’s products. But the reality is that markets commodity goods are typically dominated by only a few manufacturers, leaving purchasers susceptible to disruptions. For example, in the automotive industry, a carmaker’s total spend System flexibility allowed Pepsi Bottling Group to rapidly respond to a supply disruption caused by a fire at a chemical plant near one of its suppliers. FROM SUPERSTORMS TO FACTORY FIRES A High-Tech Manufacturer’s Risk Exposure Index Our model allows companies in any industry to effectively identify areas of hidden risk in the supply chain. Imagine a high-tech manufacturer that has suppliers and assembly plants all over the world. For each node in the supply chain, managers estimate the time to recovery if a disruption occurred at that node (how long it would take for the node to be restored to full operation) and then calculate the performance impact (lost sales during TTR, for instance). By indexing the performance impact values, managers can see at a glance which nodes represent the highest risks and direct their mitigation strategies accordingly. TIME TO RECOVERY 2 WEEKS PERFORMANCE IMPACT $300M Risk managers can use time to recovery values of different durations to account for disruptions of varying severity. RISK EXPOSURE INDEX Managers can calculate performance impact using a variety of measures: lost units of production, revenue, or profit margin, for instance. .12 Risk exposure index scores show the level of risk at each node in the supply chain-which is vastly more effective than using simple heuristics such as dollar amount spent at a site. 2 WEEKS $100M 2 WEEKS $1.5B PRINTED CIRCUIT BOARD .04 ● CHIPSET U.S. PORTS STORES RAW MATERIALS DISTRIBUTORS 1 WEEK $100M FINISHED GOODS U.S. SUPPLIERS 1.04 on suppliers of O-rings or valves is typically quite low, but if the supply is disrupted, the carmaker will have to shut down the production line. Thus, it is critical to ensure that an adequate supply is available. That can often be accomplished using the strategies that apply to the other segments: investing in excess inventory, requiring suppliers to operate multiple production sites, or implementing dual-sourcing strategies. Alternatively, companies can use flexibility to deal with hidden supply risks. For example, system flexibility (the ability to quickly change the produc- tion mix of plants) allowed Pepsi Bottling Group to rapidly respond to a supply disruption caused by a fire at a chemical plant near one of its suppliers. 2 WEEKS $2.5B 1.0 CONTRACT MFGS MFG PLANTS DISTRIBUTORS For each “node category” shown here, managers can drill down to any level of detail- from individual suppliers to geography to product line. Similarly, product-design flexibility (in this case, the use of standardized components) enabled Nokia to recover quickly from a disruption of its supply of ra- dio frequency chips caused by a fire at a supplier’s factory. Finally, process flexibility (achieved in this case by adjusting workforce skills and processes) al- lowed Toyota to quickly restore the supply of brake- fluid-proportioning valves (P-valves) after a major disruption. Case Study: Ford Motor Company We used our methodology to analyze Ford’s ex- posure to supply chain disruptions. Working to- gether with Keith W. Combs, Steve J. Faraci, Oleg Y. STORES Gusikhin, and Don X. Zhang, managers in Ford’s pur- chasing and R