By: Ranjana Mary Ninan and Christopher Sean Wang Thesis Advisor: Dr. Bruce C. Arntzen

Summary: The objective of our thesis is to demonstrate a technique to 1) visualize the supply chain and, 2) quantify areas of risk pertaining to natural disasters. By obtaining internal data on suppliers, parts, links, and revenues from our sponsor company, we aim to highlight the areas in the supply chain that have the greatest vulnerabilities. Ultimately, our goal is to help executives effectively manage their supplier base and ensure business continuity.

Ranjana Ninan graduated from Mahatma Gandhi University, Kot-tayam with a B.Tech. in Computer Science and Engineering. Prior to MIT, she worked as a purchase engineer at Gulf Extrusions Co. and as a Software Engineer at Bosch. Upon graduation, she will join Apple Inc.

Chris Wang graduated from the University of California, Berkeley with a B.A. in Economics and a B.S. in Business Administration. Prior to MIT, he worked as an investment banking analyst at Lazard and as a marketing manager at Time Warner. Upon graduation, he will join Google.

KEY INSIGHTS

1. Visualization enables the user to instantly identify weak links in the supply chain and implement risk-mitigating solutions through extra inventory, double-sourcing, or relationship-building.

2. Embedded risks are difficult to measure; metrics such as VARI that account for historical and imminent risks make it easier to compare one node versus another.

3. Up-to-date catastrophe models must be integrated into the visualization tool to make it relevant for day-to-day supply chain planning.

Introduction

In March 2011, the fifth most powerful earthquake ever to have been recorded struck the northeastern coast of Japan. What followed was one of the greatest displays of sourcing and supply chain management in human history, as businesses scrambled to preserve their operations.

While multinational businesses often find ways to keep supplies flowing during natural disasters, the Tōhoku earthquake largely halted Japan's economy – the third largest in the world – and in turn halted much of world trade. The earthquake was one of the most salient stress tests on

global supply chains, and many companies failed because there was no backup plan.

Today, there is a clear and immediate need to preempt such disruptions. We have collaborated with a sponsor company and two service providers, Sourcemap and AIR Worldwide, to develop an interactive mapping tool that evaluates risk in the supply chain and delineates key suppliers and manufacturers to ensure business continuity. Our sponsor company focuses on providing specialized diagnostic, measurement, and other industrial tools to its clients. We will examine four products which are core to the Company’s operations.

Methodology Data Collection and Cleaning: First, we requested from our sponsor company the Bill of Materials (BOM) and supplier data for the company’s four main products. This in-formation resided in the company’s Manufacturing Resource Planning (MRP) systems and contained the component part number, its respective BOM level number, lead time, safety stock, primary supplier name, and supplier remit-to address. Management provided additional information that was not captured in the IT system, such as: the revenue associated with each tool, the recovery time, the forward inventory coverage, the suppliers’ where-made address, data regarding whether the component was double sourced, and the transportation type (i.e. the

Visualizing and Quantifying Global Supply Chain Risk

mode of transportation used to send the components to the customer).

Once we received the supplier where-made addresses for each tool, we sent the information to a catastrophe risk-modeling consultancy, AIR Worldwide, to obtain risk models for the locations. The report from AIR indicated the damage (in dollars) that a generic $1-million structure would incur for various types of natural disasters, such as floods, cyclones, earthquakes, wildfires, or storms for the area in question.

Visualization: Using our sponsor’s BOM data and mapping software from Sourcemap, it was then possible to plot on a world map the supply chain for a particular tool. Circular nodes were given different colors to indicate their role as a supplier, manufacturer, or distributor in the supply chain. It was possible to get an overall view of the number of nodes in a region as well as zoom-in and view the nodes spread across the same region. The nodes were connected by links which were created based on the logic of the Bill of Materials and the suppliers’ where-made addresses. The name of the part numbers associated with the product and the number of components sourced from each node could be obtained at a first glance or by clicking on the node. The direction of flow of components between nodes was displayed as well, as seen in Figure 2.

Heat Map: The objective of the heat map is to show the importance of one node relative to another node. Rather than coloring each node by its function in the supply chain, the heat map colors each node by its relative importance, measured either by 1) the Risk Exposure Index (“REI”) or, 2) the Value at Risk Index (“VARI”).

1) Risk Exposure Index (“REI”) Using the revenue associated with the deployment of a particular tool, the inventory level of the component, and the recovery time to obtain a component once a node is disabled by an unforeseen disaster, we calculated a metric called the REI. Each node has a unique REI, which reflects the revenue-enabled-per-year by that supplier, adjusted for any pipeline inventory:

Risk Exposure Index ($/year) = Revenue Enabled ($/year)* [Recovery Time (days) – Forward Inventory Coverage (days)]/365 days

2) Value at Risk Index (“VARI”) When the REI is further adjusted to account for the probability of a disruption from a weather event, we can determine another metric called the VARI:

Value at Risk Index ($/year) = REI ($/year) * risk probability (%)

We distinguished between two types of VARI: baseline and event-based. Baseline VARI adjusts REI for historical risk, while event-based VARI adjusts REI for imminent risk. Both VARIs are an adjusted REI, and the only difference between the two VARI is the risk probability by which each is multiplied:

Baseline VARI (historical risk): To determine a node’s baseline VARI, we found historical risk probabilities by analyzing catastrophe models from AIR Worldwide. In each catastrophe model, we used a one-percent threshold in property value loss to indicate a disruption (e.g. a disruption occurs if there is $1 or more of damage for a building valued at $100). One percent damage may seem small, but the damage to the workers’ homes and to the gas, water, electrical, and information systems would likely be higher and lead to the site becoming unavailable to the supply chain.

Event-based VARI (imminent risk): Predictability is an important factor for determining event-based VARI, since the ultimate goal of calculating event-based VARI is to highlight hotspots that management could plan around. The methodology for finding risk probabilities for event-based VARI hinges on our alerts system in association with a company called Beroe.

Alerts: The primary function of Alerts is to provide risk probability data for calculating event-based VARI.

Example: Converting a Hurricane Warning into a Risk Probability:

In this case, we used a two-step approach to convert a hurricane warning into a quantifiable risk probability. First, we determined the probability of a hurricane hitting the area in question. Second, we assessed the probability of a disruption given the type of the building. We then combined probabilities from the first and second steps to find the risk probability of a disruption in the event of a hurricane:

Event-based VARI ($/year) = probability of a hit (%) * probability of a disruption (%) * REI ($/year)

1) Probability of a Hit: The hurricane alert includes data on the severity and radius of the disaster, as well as the percentage chance of a hit.

2) Probability of a Disruption: In the second step, we determine the probability of a disruption. A decision tree recommends specific tables

outlining risk probabilities, depending on the height and construction of the building.

Damage State

Hurricane Intensity (Wind Speed – MPH)

75 100 125 150 200

None 0.903 0.567 0.44 0.191 0.103

Light 0.080 0.183 0.141 0.156 0.062

Moderate 0.011 0.178 0.178 0.164 0.152

Heavy 0.003 0.048 0.097 0.275 0.242

Severe 0.001 0.012 0.12 0.104 0.237

Collapse 0.001 0.012 0.024 0.109 0.203

Figure 1 – Damage Probability Matrix for 1-3 Concrete or Masonry Commercial or Industrial Structures

Figure 1 indicates the probability of incurring losses at a given damage state for a given wind speed combination. We determined that any damage state above Moderate would be considered a disruption. Following this definition of a disruption, a 1-3 story concrete structure hit by 100mph winds would see a twenty-five-percent chance of a disruption.

Pr(Disruption) = Pr(Moderate) + Pr(Heavy) + Pr(Severe) + Pr(Collapse)

Pr(Disruption) = 0.178 + 0.048 + 0.012 + 0.012

Pr(Disruption) = 0.25 = risk probability

Results

The aggregation of internal supplier data, in addition to the various REI and VARI indices, culminates in an interactive map of our sponsor’s supply chain on Sourcemap as seen in Figure 2.

Figure 2 – Sourcemap Output

Sourcemap offers a high-level view of the sponsor’s supply chain, which can be further dissected in more detail using the map’s dynamic zoom-in capability. The number on each circular node represents the number of facilities in that general vicinity.

Management can filter by a number of categories associated with the nodes. The filter function can screen by any data attribute, including Primary Supplier ID/ Name, Component Part Number, etc. These attributes can be customized however the user sees fit.

Sourcemap also allows users to see a tree-structure of the BOM. This may be useful for quickly delineating between Tier I and Tier II suppliers, as well as focusing on which suppliers belong to which products.

In addition to visualizing the supply chain, Sourcemap enables the user to compare nodes based on their relative importance. In Figure 3, each node is color-coded by their respective REI. High REIs are highlighted in red, while low REIs are highlighted in green.

Figure 3 – Heat Map

The baseline and event-based Value at Risk Indices (“VARI”) are an adjusted REI that accounts for historical and imminent risk probabilities of a disruption respectively. Similar to the REI map in Figure 3, each node is color-coded by their respective VARI.

Conclusion

Our tool allows procurement professionals to instantly gauge the vulnerabilities in the supply chain of a product by displaying a spectrum of risk profiles based on its REI and VARI. This ranking of risk profiles by color can now aid the procurement professional to make better risk mitigation decisions. For instance, if a supplier is depicted as a red node, the purchasing department can decide how they would want to build redundancy in terms of investments in

extra inventory, dual-sourcing, or deeper relationships with the existing supplier.

One major advantage in the way we have visualized risk is that each node shows the revenue at stake in the event of a disruption. Incentives for purchasing departments in organizations are often tied to savings associated with spend. Therefore, the Purchasing department often suffers from tunnel vision by focusing merely on the costs associated with procuring a product rather than the revenue that is enabled by the product. This means that while the Purchasing department considers components with higher spend as a strategic risk, they sometimes fail to consider the ‘hidden risk’ associated with low-spend components which will cause a major financial impact if a disruption occurs.

Another advantage is that by considering the probabilities of a natural catastrophe, such as a hurricane or an earthquake, in the calculation of the VARI, the insight gained from this index can help the decision maker in the supplier selection and development process. Based on this data, decision makers can decide whether they want to deepen their existing relationship with a single supplier for a component, so as to get involved in the supplier’s business continuity plans and recommend that a second facility be opened in another region by promising economies of scale. In order to spread the risk, the company can also decide to source the component from another supplier in a different geographical region or a supplier that has facilities in multiple regions. During the vetting process, suppliers could be asked to confirm whether their facilities are constructed according to building codes that make it resistant to natural disasters.

A third advantage is that by embedding the catastrophe models in the visualization, the Purchasing department will be attentive to weather alerts occurring in those parts of the world where the risky nodes are located. This can help them make quick decisions such as locking in additional capacity with a supplier that also serves a competitor, thereby ensuring supply.

We expect there to be significant financial benefits to using the Sourcemap visualization tool, since it ensures the continuity of revenue-earning opportunities.