MEASURING DISRUPTION INDICATORS IN FOODSERVICE SUPPLY CHAIN
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MEASURING DISRUPTION INDICATORS IN FOODSERVICE SUPPLY CHAIN by Teng Yi Li, BCom, University of British Columbia 2013 and Amy Schwendenman BSc. in Supply Chain and Operations Management, Miami University (OH) 2015 SUBMITTED TO THE PROGRAM IN SUPPLY CHAIN MANAGEMENT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE IN SUPPLY CHAIN MANAGEMENT AT THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2021 © 2021 Li and Schwendenman. All rights reserved. The authors hereby grant to MIT permission to reproduce and to distribute publicly paper and electronic copies of this capstone document in whole or in part in any medium now known or hereafter created. Signature of Author: _____________________________________________________________________________ Department of Supply Chain Management May 14, 2021 Signature of Author: _____________________________________________________________________________ Department of Supply Chain Management May 14, 2021 Certified by: _____________________________________________________________________________ Dr. Christopher Mejía Argueta Research Scientist, Center for Transportation and Logistics Director, Food and Retail Operations Lab Capstone Advisor Accepted by: _____________________________________________________________________________ Prof. Yossi Sheffi Director, Center for Transportation and Logistics Elisha Gray II Professor of Engineering Systems Professor, Civil and Environmental Engineering Page | 1
Measuring Disruption Indicators in Foodservice Supply Chain Teng Yi Li and Amy Schwendenman Submitted to the Program in Supply Chain Management on May 14, 2021 in Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Supply Chain Management ABSTRACT The food-service industry in the United States is worth approximately $300 Billion annually and supports 1 million jobs across the country. The sponsoring company is a major distributor in the United States for different categories of restaurant chains, ranging from counter-only-service to full-service. The key products in their supply chain include meat such as poultry and beef, which are vulnerable to both supply and demand shocks, and could have significant impact to their operations. While they have some visibility downstream to understand causes of demand shocks, there exists an information gap upstream to understand supply shocks. This project aims to connect various external data sources to internal data to 1. identify what supply shocks looks like; 2. find lead indicators of supply shocks in the external data; and 3. quantify their impact on the sponsoring company in order to improve operations planning and contingency planning. The models we built predict instances of expedited shipments and delayed shipments as they relate to macro factors, such as severe weather, wholesale prices, and national slaughter rates. Capstone Advisor: Dr. Christopher Mejía Argueta Title: Research Scientist, Center for Transportation and Logistics. Page | 2
TABLE OF CONTENT 1. INTRODUCTION ...................................................................................................................... 6 1.1 Motivation ............................................................................................................................. 6 1.2 Problem Statement ................................................................................................................ 8 2. LITERATURE REVIEW ......................................................................................................... 12 2.1 Overview ............................................................................................................................. 12 2.2 Length and Depth of Meat Supply Chains .......................................................................... 12 2.3 Demand Factors................................................................................................................... 14 2.3.1 Promotions .................................................................................................................... 14 2.3.2 Shift from Foodservice to Retail Consumption ............................................................ 15 2.4 Supply Factors ..................................................................................................................... 15 2.4.1 Location and Its Effects ................................................................................................ 16 2.4.2 Food Recalls ................................................................................................................. 17 2.4.3 Pricing ........................................................................................................................... 18 2.5 Defining Supply Chain Disruption...................................................................................... 19 2.5.1 Quantifying risk in food Supply Chains ....................................................................... 20 2.6 Quantifying impact of supply chain risk and disruption ..................................................... 21 2.7 Conclusion........................................................................................................................... 22 3. METHODOLOGY ................................................................................................................... 23 3.1 Internal Data ........................................................................................................................ 24 3.2 External Data ....................................................................................................................... 24 3.2.1 United States Department of Agriculture (USDA) Data .............................................. 24 3.2.2 National Oceanic and Atmospheric Administration (NOAA) Data ............................. 25 Page | 3
3.2.3 COVID-19 crisis ........................................................................................................... 25 3.2.4 Other External Data Considerations ............................................................................. 26 3.3 Data Cleaning ...................................................................................................................... 26 3.3.1 Internal Data Cleaning .................................................................................................. 27 3.3.2 External Data Cleaning ................................................................................................. 27 3.3.3 Internal/External Alignment ......................................................................................... 27 3.4 Analysis Framework ........................................................................................................... 31 3.4.1 Exploratory Analysis .................................................................................................... 31 3.4.2 Scenario Analysis ......................................................................................................... 33 4. RESULTS ................................................................................................................................. 35 4.1 Correlation Analysis ............................................................................................................ 35 4.2 Poultry ................................................................................................................................. 36 4.2.1 Regression Analysis ..................................................................................................... 37 4.2.2 Scenario Analysis ......................................................................................................... 40 4.3 Beef ..................................................................................................................................... 42 4.3.1 Regression Analysis ..................................................................................................... 43 4.3.2 Scenario analysis .......................................................................................................... 46 4.4 Discussion of results............................................................................................................ 48 5. CONCLUSION ......................................................................................................................... 49 5.1 Limitations .......................................................................................................................... 50 5.2 Further Research ................................................................................................................. 51 References ..................................................................................................................................... 53 Page | 4
LIST OF FIGURES Figure 1 Meat Industry Process Flow ............................................................................................. 9 Figure 2 Tyson Foods: Cattle Production Timing, Growth Rates ................................................ 13 Figure 3 Tyson Foods: Poultry Production Timing, Growth Rates .............................................. 14 Figure 4 USDA: Market Beef Prices for Boneless Chuck in 2020, 2019, and 3-Year Average .. 18 Figure 5 Framework for the Methodology.................................................................................... 23 Figure 6 Variable Correlation Heat Map ...................................................................................... 36 LIST OF TABLES Table 1 External Data Dictionary ................................................................................................. 31 Table 2 Regression Model Outputs for Poultry ............................................................................ 37 Table 3 Regression Model 1 ......................................................................................................... 37 Table 4 Regression Model 2 ......................................................................................................... 39 Table 5 Analysis of the number of freight spend disruptions for Model 1 ................................... 40 Table 6 Cost Impact of freight spend disruptions for Model 1 ..................................................... 41 Table 7 Analysis of the volume (receipt weight) impacts for Model 2 ........................................ 41 Table 8 Impact of changes in Shipments based on Avg Receipt Weight per order for Model 2 . 42 Table 9 Regression Model Outputs for Beef ................................................................................ 43 Table 10 Regression Model 3 ....................................................................................................... 44 Table 11 Regression Model 4 ....................................................................................................... 45 Table 12 Analysis of the estimated number of freight spend disruptions for Model 3 ................ 46 Table 13 Cost Impact of freight spend disruptions for Model 3 ................................................... 47 Table 14 Analysis of the number of delayed shipments for Model 4 ........................................... 47 Page | 5
1. INTRODUCTION 1.1 Motivation In 2019, it was estimated that the size of the food-service industry in the United States was worth $293 Billion and supported 1 million jobs across the country (Refrigerated Frozen Food, 2018). Within the food-service industry, food distributors help coordinate, transport, and consolidate shipments for their customers. Our project company is a nationwide distributor for several major restaurant chains throughout the United States. The service offering at these restaurant chains ranges from counter service only, to full table service. Due to the nature of our project company clientele, a large component of our project company’s revenue comes from distributing different cuts of meat. Meat, or “center-of-the-plate” offerings, are critical for our company’s operations. Within the meat category, beef and poultry are two types of meat that are important for our project company due to their prevalence in the foodservice space. In the U.S., consumption of poultry has steadily increased since the 1940s, overtaking beef in supplied pounds per capita around 2010. In 2017, approximately 64 pounds of boneless chicken were available per person, whereas approximately 54 pounds of beef were supplied per person (Kantor and Blazejczyk, 2020) – for a combined total of over a hundred pounds of available meat per person (for a population of 325 million inhabitants). While meat consumption in the United States continues to grow as consumers are looking to add more protein to their diets, consumer preferences are changing the way meat is offered (Euromonitor, 2020). Page | 6
Pushed by the emergence of casual restaurants, one of the recent trends in the food industry is the pivot towards more sustainable menus (Mitroff, 2019). One impact of this trend is that restaurants are increasingly demanding fresh over frozen meat, which significantly increases the complexity of the supply chain. Changes in consumer preferences have not only created demand for more sustainable food chains, but have also led to a rise in non-meat protein alternatives in both retail and foodservice sectors. Several restaurant chains that now have product offerings sourced from either Impossible Burger or Beyond Meat (Mitroff, 2019). These shifts in consumer preferences change distribution requirements and are important for our project company to consider for their operations now and for the future. On the supply side, one major trend within the meat industry has been the rapid consolidation of companies operating in the United States within the past few decades. Today, the meat industry is extremely centralized: four firms process 80% of the beef in America, while five firms control 60% of processed poultry. Also, 85% of the supply of beef is processed in just 30 facilities across the U.S., which could indicate more severe impact to supply during times of disruption (NCBA, 2020). In the poultry industry, 25,000 family farms feed into only 180 slaughtering and processing plants, producing 42.5 billion pounds of poultry products each year (The National Chicken Council, 2020). In 2020, the COVID-19 pandemic highlighted this fragility as cramped working conditions in meat packing plants allowed the coronavirus to easily spread amongst workers, leading to major outbreaks. Many plants slowed or shut down their operations due to these concerns; however, an executive order was issued by President Donald Trump to open the meat packing plants back up to ensure meat supply continuity. This supply shock coincided with Page | 7
nationwide restaurant closures and mandated lockdowns at different levels, rapidly shifting consumer demand from foodservice consumption to retail outlets. The severity of the COVID-19 disruption has prompted an industry-wide evaluation of business practice and risk mitigation strategies. While our project company was invested in better understanding their upstream supply chain, the pandemic highlighted this importance. Because the disruptions to the industry from COVID-19 are unprecedented, our research attempts to review this and other major disruptions across the meat supply chain for the food service industry. Part of our project company’s value proposition is being highly attuned to demand changes, with a desire to add this value on the supply side. Our project company is looking to increase their knowledge about supply constraints by supplementing internal knowledge with outside data sources. 1.2 Problem Statement Our primary research goal is to understand the significant factors that indicate the early stages of a large supply disruption for a nationwide distributor of perishable products to the foodservice industry. Currently for the company, there exists a lack of upstream visibility and information from their suppliers, opening the company up to additional risk and higher costs when a supply chain disruption occurs. Figure 1 shows the supply chain flow for our project company. As a distributor, our project company is in the middle of the supply chain and is impacted by both supply and demand changes. The businesses that use our project company to facilitate the movement of product to their restaurants are downstream from our company. The customers that purchase food from Page | 8
these restaurants are further downstream, but they will not be considered as part of the scope of this study. Demand fluctuations at the customer level can lead to larger variations in the upstream supply chain, known as the bullwhip effect (Supply Chain Academy, 2018). Demand signal processing (i.e., the speed at which changes in demand are communicated up and down the supply chain), non-zero lead times, and price fluctuations are three factors relevant in the food distribution model that cause the bullwhip effect in a demand-driven supply chain (Cao et al., 2017). The company we are working with is confident with the information they have from their downstream operations, but is looking to increase information about their upstream supply chain. Figure 1 Meat Industry Process Flow The suppliers that our company or our company’s partners place orders with are Tier 1 upstream suppliers. Within the meat industry, these suppliers are primarily meat packers and processors. Further upstream are the Tier 2 suppliers, and beyond that are those in Tier 3. Within the meat industry, these upstream suppliers are cattle or poultry farmers. Because our project Page | 9
company lacks upstream information, they are more likely to face the negative effects of the bullwhip effect, a gap our research attempts to fill. Additionally, the drastic and rapid changes brought on by the pandemic magnified the importance of understanding how upstream supply chain disruptions impact operations for our project company. The project scope has been limited to the beef and poultry supply chains and broken out into two separate demand categories: the poultry products, which are mostly served at restaurant category 1; and beef products, which are served at both restaurant category 1 and 2. This is primarily due to availability of internal data from the sponsoring company. Other protein groups, such as pork and fish, are considerably smaller in volume and therefore, they do not have the same richness of data. The scope limitation allows our research to focus on large-scale supply chain disruptions that impact supply in these restaurant categories. Through literature review and other research, the goal is to find external data sources relevant to our project scope. The external data will act to supplement company’s information to build our exploratory approach. Our statistical analysis will use the data found externally and provided internally from our project company to identify leading indicators of supply chain disruption. Our work will tie this to a set of scenarios which will help our project company understand the impact that relevant disruptions carry for the different products and channels. The scenarios help understand how transportation costs and times change due to the relative disruption impacts, helping to elevate our project company’s value proposition for their clients because their competitive advantage is the ability to provide clients with real-time transparency into supply chain operations. Our research provides the sponsor company with more insights about how external data from the upstream supply chain may help detecting outliers and potential disruptions in the Page | 10
operations of the project company. Furthermore, we hope our project illustrates how a first-order methodology based on exploratory analysis can help companies building knowledge about relevant factors that cause disruptions in their supply chains. Ultimately, this will allow the sponsor company and similar distributors to find strategies to prevent undesired effects and compute their effects in the financial and level of service. Page | 11
2. LITERATURE REVIEW 2.1 Overview To better understand leading indicators of supply risk within the meat and foodservice industry, we reviewed the relevant literature that exists about factors on both demand and supply sides. While our project focuses on the leading indicators of supply shocks, it is relevant to understand the demand variations as demonstrated in the bullwhip effect. Additionally, we review literature on supply chain disruptions and their effects for the supply chain. We also reviewed how to properly quantify impacts of disruptions using scenarios in our context, understanding how disruptions are translated into quantifiable impacts to businesses downstream. 2.2 Length and Depth of Meat Supply Chains The length of a supply chain is important in evaluating supply chain disruption. In this respect, length refers to the amount of time between the highest tier supplier and the lowest tier consumer; depth refers to the number of upstream and downstream tiers (DeAngelis, 2021). For a beef steak, this would be the amount of time between the birth of a cow (beginning time for “supply”) and its eventual consumption. For the meat industry, certain factors like growth rates are stable and cannot be influenced or shortened. Other factors like lead time, transit time, number of touching points (e.g., number of intermediaries) for the product, and storage type can be influenced to shorten or lengthen the supply chain. Of the three primary proteins (i.e., beef, pork, and chicken), changes in beef production take the longest to flow through the supply chain given the amount of time it takes for cattle to Page | 12
mature enough for slaughter. Changes in cattle production take about 39 months to reach our project company’s Tier 1 suppliers. If a cattle farmer, which falls under the Tier 2 supplier for our sponsor company, decides to reduce the amount of “supply” due to higher feed prices, the effect of this change will likely be absorb upstream and through a basket of market indicators, such as live cow wholesale prices, new calves birth rate, average age at slaughter, and export figures. Therefore, it is not clear which, if any, external data sources will be useful to find lead indicators from Tier 2 suppliers. On the other hand, poultry grows more quickly, with changes in production impacting the supply chain as fast as nine months. However, with respect to supply chain length, this is still a significant amount of time. This poses similar challenges and limitation to beef, in that the effect of upstream disruption is dispersed across many market indicators. Therefore, for the scope of this project, potential lead indicators from upstream suppliers beyond Tier 1, such as feedstock prices, are not considered in our model. Figure 2 Tyson Foods: Cattle Production Timing, Growth Rates Page | 13
Figure 3 Tyson Foods: Poultry Production Timing, Growth Rates Note. Figures from Investor Fact Book, by Tyson Foods, 2020, retrieved on November 3, 2020, from https://s22.q4cdn.com/104708849/files/doc_factbook/2020/FactBookFY19_SinglePage- (Final).pdf 2.3 Demand Factors For distribution channels to ensure supply, operators must be able to react quickly to changes in demand. Some examples of external factors that impact demand for our project company include weather, payday schedule, holidays, and competitor strength. Two unique factors for the foodservice meat distribution we want to highlight are promotions and most recently with COVID-19, shifts to retail consumption and related lockdowns. These data would not be consumer-related but they will give an idea of purchasing trends and patterns changed due to the pandemic. 2.3.1 Promotions For the foodservice channel, product promotions are a big cause of large, hard to predict shifts in demand. In 2020, several restaurant chains launch meal promotions to boost demand; however, the success of promotions led to ingredient stock-outs around the country (McCarthy, Page | 14
2019). These promotions can be detrimental for our project company because demand is difficult to predict. However, the sponsor company does not have granular data per restaurant, as the data are owned by the restaurant chains, what made difficult for this research to understand the effect of promotions. Key details, such as time period of the promotions, duration and timing of stock- outs events, and area of impact, were not available for review for this project. In addition, those promotions only created a big variation in the availability of meat for a particular group of restaurant chains. This means that for the aggregated beef and poultry dataset, it is not easy to identify impacts that can be clearly attributed to promotions. 2.3.2 Shift from Foodservice to Retail Consumption COVID-19 and the levels of lockdowns implemented by different states and cities in March and April 2020 forced the immediate closure of restaurants around the country, shifting large amounts of meat consumption from the foodservice channel to retail channels (Welshans, 2020). Meat shortages at the retail level and excess supply within the foodservice channel could not be quickly resolved due to differences in how meat is transported, packaged, and sold in both channels. Due to this dynamic, one external data source that we identified for our project may be retail prices or retail consumption patterns. 2.4 Supply Factors In this section, we review the relevance of diverse supply factors that may affect meat supply chains. A supply factor is any factor that influences the amount of supply in a given market (Pettinger, 2019). Many of these factors are relevant for all supply chains, while some are Page | 15
specific to the meat industry. We specifically look at these variables and how they connect with supply disruptions for meat distributors of the foodservice industry. 2.4.1 Location and Its Effects The supply is affected by the distance between origin and destination nodes. This clearly depends on supplier’s location. First, the transportation lanes from supplier to distribution are potential important factors. Longer distances between two points mean there is a longer lead time from order to receipt, and therefore, influence supply chain decisions like speed of distribution strategies and safety stock from inventory policies. If there is a disruption, long lead times prolong recovery efforts. Another supply factor impacted by the location of suppliers is weather. For the purposes of this paper, we only look at weather that can be defined as severe because supply chains have mitigation plans for routine weather events (Simchi-Levi et al., 2014). While severe weather events are geographically situated, downstream effects can impact the entire supply chain. One such example is when very wet, cold winters occur, cattle growth is impacted which reduces weight gains, slowing the rate of supply (CME Group, 2020). Also, when storms hit certain regions, infrastructure may be damaged and create accidents, what may produce losses. With respect to our product categories, poultry is primarily raised in the Southeast United States: Georgia, Alabama, Arkansas, North Carolina, and Mississippi are the top producing states (The National Chicken Council, 2020). Geographically, this area is susceptible to disruption from major storms, hurricanes, and flooding. In 2014, 3.4 million chickens drowned during Hurricane Florence. Top states for cattle feedlots are in the Great Plains: Texas, Nebraska, Page | 16
Kansas, and Oklahoma (NCBA, 2020), states where tornadoes and severe winter weather are more likely to occur. The clustering of these industries also poses an additional risk with respect to regional weather disruptions. Severe weather events do not only impact production, but also create delays for various modes of transportation and other logistic operations. 2.4.2 Food Recalls Of all the potential disruptors in a supply chain, product recalls due to disease or sanitary/phytosanitary breaches pose a unique risk to the meat industry. The U.S. Public Interest Research Group (US PIRG) studied the Food and Drug Administration (FDA) recall data from the past decade. These data showed that between 2013 and 2018, recall risks increased by 10% overall, and hazardous “Class 1” protein recall risk increase by 83% (Parker, 2016; Karthikeyan and Garber, 2019). Some of the biggest sources of foodborne diseases from Class 1 recalls – which USDA defines as having a reasonable probability that use of the offending product could lead to serious illness or death – include salmonella, E. coli, and listeria. While most of the impact of food recalls is through the retail distribution channel, there have also been notable food contamination outbreaks in the foodservice industry. Chipotle, for example, has been fined $25 million for various disease outbreaks between 2015 and 2018. Over 1,100 people became sick as a result of multiple outbreaks involving salmonella, E. coli, norovirus, and more at various locations across the United States (Food Safety News, 2020). A disruption from product recalls does not only affect the firm issuing the recall notice and its customers, but also the wider industry in which the recall is issued. When serious questions arise regarding the product safety of a particular food group, customer demand will decline until the Page | 17
issue is resolved or until sufficient time has passed (Lawson et al., 2019). Naturally, recalls become an external data source to identify areas, products and seasons that are more susceptible to supply shocks. 2.4.3 Pricing From a general supply and demand economic model, pricing is a known factor that influences the amount of quantity supplied as well as the amount of quantity demanded. Both beef and poultry markets react to these changes, impacting pricing and vice versa throughout the supply chain. Farmers may hold off on selling their livestock when wholesale prices are low, and they may increase supply of livestock when prices are high. However, a similar behavior might be observed in subsequent stakeholders (e.g., processors, packers). Price fluctuation in products that are derived from livestock as well as price fluctuations in substitute products like pork, can also impact overall supply of livestock (CME Group, 2020). Figure 4 USDA: Market Beef Prices for Boneless Chuck in 2020, 2019, and 3-Year Average From Beef—It’s What’s For Dinner—Wholesale Price Update (2020) Page | 18
Figure 4 is an example of price variation for Boneless Chuck (a popular cut of beef) during 2020 and the COVID-19 lockdowns. Due to this severe disruption in both supply and demand, the volatility of price was high, indicating rapid changes in the wholesale market. Price elasticity is the rate at which changes in price impact the quantity demanded. Within our two product categories, beef is more price-elastic. This means that when the price of beef changes, the quantity demanded changes quickly with it; price changes affect poultry to a lesser extent. It is important to note that foodservice demand may not be as elastic given that price changes are not immediately passed on to consumers. These examples help highlight that price fluctuations are more likely to be indicators of supply disruption than actual sources of disruption. 2.5 Defining Supply Chain Disruption Supply chain disruptions are “unexpected, significant negative deviations from process plans caused by one or more temporal events” (Brenner, 2015). A supply chain disruption indicates a breakdown in the underlying process due to one or multiple outside supply or demand factors. Because of this, supply chain disruptions are unique to the specific market and industry a company operates in. One way to evaluate the impact of a potential disruption is to associate the disruption with the relative effects such an event will have for the company costs and their level of service. This requires an understanding of the probability and the frequency of an event to occur, as well as the magnitude of the impact from that event’s occurrence, based on a set of significant factors by modeling several scenarios. Page | 19
2.5.1 Quantifying risk in food Supply Chains We attempted to find relevant research papers that have looked into supply chain disruption in the food supply chain, to study the methodology and, findings, as well as to identify the gaps in the existing academic and practical spheres. While there are many studies that have tried to quantify the risk and resilience of the food supply chain, few exist that are specific to the meat industry market. Brenner (2015) performed an empirical study of causes of supply chain disruptions in the food cold chain by surveying companies about their operating procedure and then looked for statistical significance in the responses. In her research, she developed a classification framework to differentiate between different types of disruptions, then evaluated the performance of different companies using a scoring model on different performance indicators. While the context of her studies is different from our research goals, we took away some general ideas about how to classify instances of disruption in data. There were several other studies that we found relevant to the topic of food supply chain disruption. Prakash et al. (2017) examined the risk mitigation strategies of dairy farmers in India by assigning a risk value to each strategy, as well as understanding which strategy worked the best and what types of risks are present. In his study, he identified 17 unique risk factors classified into four types of risk – environmental risk, demand risk, supply risk, and process risk. Using interpretive structural modeling, the study found that environmental risks, such as natural disasters, are the most independent risk factors and have the greatest influence on other parts of the supply chain. This affected our decision to place high emphasis on examining severe weather data as a potential source of disruption lead indicator. Page | 20
MacKenzie and Apte, (2017) modelled disruption in the fresh produce supply chain to find out how to mitigate the risk to sales by looking at factors such as optimal safety stock and average time to discover contamination in the supply chain. His study helps explain how downstream customers are protected from disruption at upstream suppliers, as different links in the supply chain can act as a buffer to dampen the impact. His study allows to consider that time would be substantially affected in case of a disruption. 2.6 Quantifying impact of supply chain risk and disruption One of the methods to measure supply chain risk is Value at Risk (VaR). While VaR was a tool developed by JP Morgan for the banking industry to manage risk of losses from trades, it can be adapted to the supply chain context. There are three components of VaR: the amount of potential loss due to disruption, the likelihood that a disruptive event will happen, and the timeframe for the event to take place (Lim et al., 2013). This analysis allows for comparison of the potential disruption impact across different scenarios and timeframes. In one example of a study relating to supply chain disruption lead indicators, Lu and Xia (2014) evaluated the risk to supply chains from earthquakes in the United States and showed how VaR can be used to quantify the impact. Another method of measuring supply chain risk is Time to Recover (TTR) and Time to Survive (TTS). TTR measures the amount of time for a supply chain node to return to full capacity after a disruption, and TTS measures the maximum period for a supply chain node to continue its operations during an ongoing disruption. If a supply chain’s TTR is longer than its TTS, then the supply chain will be unable to continue operations during a disruption without Page | 21
adequate backup plans. In his research, Simchi-Levi (2014) noted that this method was used by Ford Motor Company in 2013 to identify risk mitigation strategies. In addition, he described how this method could be used to model the impact of a major disruption event, such as a natural disaster. VaR, TTR and TTS allow us for finding ways to measure the impact of disruptions in terms of costs and time. Additionally, this guided our decision to identify outliers in the data and use scenarios to model the impacts in our study. 2.7 Conclusion Through our study of the relevant literature, we reviewed some demand and supply factors that may be unique in their importance to our product categories. We found that the location of suppliers, product recalls, and pricing are important variables we should consider in our modeling. Additionally, we investigated how to define supply chain disruptions in our context and the different strategies in quantifying supply disruption risk. While there is literature surrounding leading indicators of supply chain risk and multiple scenarios to model them, there is a lack of information about leading indicators of risk in the meat industry. Our research aims to fill this gap. Page | 22
3. METHODOLOGY In this section, we will describe the process we went through to create our model. First, we collected data from the sponsoring company and external sources, then cleaned and standardized our data. After our data were ready, we ran regression analysis using internal data as the dependent variable and external data as independent variables to find relevant leading indicators of supply chain disruption. Finally, we used the data provided by the project company with the regression outputs to create scenarios to show the financial and business impact that changes in the dependent variables can have on the business (see Figure 5). Figure 5 Framework for the Methodology Page | 23
3.1 Internal Data Our project company provided order data and average freight information from the beef and poultry products and two restaurant categories for the last three years (2018-2020). The order data contain order information, product attributes, and location details, which allowed us to review order trends and map out our project company’s supply chain. Certain variables regarding timing can be gathered: distance traveled, average lead time, and the shelf life of product. We can also designate a frozen or fresh shipment, which relates to the overall shelf life and sourcing strategy. From the order data, we are also able to review overall volume, and order frequency of different items. The order data is given on a daily level, by each item on an order. Other data we received were the relative cost for transportation of each order for its unique shipping lane. This costing information allows us to review variation in pricing in addition to volume changes to help determine if a supply disruption occurred. This costing information is provided by order, whereas the order information shows each line item per order. 3.2 External Data Our literature review and industry research allowed us to strategically search for data sources specific to the meat industry in the United States. Below are the sources that were contemplated and used in the proposed methodology. 3.2.1 United States Department of Agriculture (USDA) Data Due to federal regulations and guidelines within the industry, the United States Department of Agriculture (USDA) National Agricultural Statistics Service provides a robust Page | 24
dataset for both beef and poultry. Through the USDA, we found publicly available data for slaughter volumes, wholesale prices, meat imports and exports, and product recalls. From our literature review, we decided that all of these variables could be important indicators to review in our statistical model. 3.2.2 National Oceanic and Atmospheric Administration (NOAA) Data We know that severe weather can cause a disruption within the supply chain, so we also used data from the National Oceanic and Atmospheric Administration (NOAA) inventory of severe weather events within the United States. The data show the damage in dollar value of the region impacted by different types of severe weather and the day, month, and year this event happened. In addition, we hypothesized that supply chains already plan forhold of severe weather events, and that the driving factor is year-over-year changes. Therefore, we calculated the year-over-year changes in damages as a percentage (i.e., rate of change). Severe weather data have state location details for further segmentation. 3.2.3 COVID-19 crisis In 2020, COVID-19 pandemic was a source of supply and demand disruptions for the meat industry. We added this variable as a binary variable (i.e., 1 for COVID-19 months and 0 for non-COVID-19 months); however, once in the model, this variable became the only significant explanatory variable and removed the variability from other potential significant factors. Then, we transformed the metric into a categorical variable without any change. Afterwards, we evaluated another COVID-19 metric: processing plant shutdowns due to COVID-19 outbreaks and overall infection rates in communities where a large percentage of the Page | 25
population works in the meat packing industry. Ultimately, we did not add either of these factors to our model because we found that COVID-19 was a known disruption and would not be replicated for future disruptions. 3.2.4 Other External Data Considerations For beef and poultry, we used Global Trade data from Chicago Mercantile Exchange (CME) future trading information on total amount of pounds imported and exported. The data are organized by country, in which we pulled the United States by total pounds and explored both imports and exports. Data about futures are able to be traded within the livestock industry in order for players in the game to manage the inherent risk that is involved in raising livestock. This information is only available for the beef category. We ended up not adding this to our model for the sake of scope given that we do not consider raising cattle as part of the supply chain horizon for the sponsoring company. Additionally, we thought about using road condition index, which shows the mileage of acceptable road conditions in a state compared to the overall road mileage. The overall quality of roads does not change drastically, even due to blockages during severe weather events, so we decided against using this variable in the model. 3.3 Data Cleaning Both the internal and external data required standardization in order to create a merged dataset suitable for multiple linear regression and other analysis. Page | 26
3.3.1 Internal Data Cleaning All data not pertaining to the exact products and restaurant categories being reviewed were removed. These data included orders that were from outside the scope of suppliers in our project. Volume outliers were also reviewed, but not removed, since the outliers could be indicative of a disruption and we did not want to lose that information within the model. 3.3.2 External Data Cleaning All external data that fell outside of the three years of order data provided by the project company were removed. We did not remove outliers from the data because extreme events are potential sources of major disruption. Additionally, we assessed what type of data to include from sources like pricing, where there are wholesale and specific cut information. For the sake of the model, we keep these variables in until further analysis in order to not bring our own bias about what could be significant into the process. 3.3.3 Internal/External Alignment One of the most important steps in our modeling was going through various iterations on how to align our internal and external data. First, it was necessary to define the key measurements, which include time and location specificity of each data set. Internal data is presented on a line-item basis. This means that there is a row of data for each item on an order, and the timing is presented by day with each order’s receipt date. Internally, three methods are used to organize the data to create a disruption metric: volume, variation in freight spend per Page | 27
order, and variation in lead time. The formulations are created on a per month basis, since the external data sources limit us to this timescale. Notation: Indices o: Order for products i: ship from location (origin) j: ship to location (destination) Variables rdo: received date for order o of products odo: order date for order o of products , : Individual observation of freight spends for product p from origin i to destination j : Average of freight spend for products from origin i to destination j : Standard deviation of freight spends for products from origin i to destination j , : Individual observation of lead time for product p from origin i to destination j : Average lead time for products from origin i to destination j : Standard deviation of lead time for products from origin i to destination j DI: Disruption Indicator DIfo: Disruption Indicator for freight spend DILT: Disruption Indicator for lead time N: Number of observations in the dataset Ni,j: Number of observations in the dataset from origin i to destination j p: Product, either beef or poultry Page | 28
Volume for each order is calculated per SKU quantity multiplied by the SKU gross weight for all
SKUS on an order. This is calculated for all orders. Monthly volume is the summation of all
order volumes within that month, and this is performed for all months. Volume is referenced as
receipt weight further in the analysis.
Formulation
Freight Spend Variation:
1
= ∑
=1 , ∀ , (1)
1 2
= √ ∑
=1 ( , − ) (2)
1 , , ≥ +
, , = { ∀ , , (3)
0 ℎ
(1) is the mean freight spend for all orders on unique transportation lane (i, j). This is
calculated for each year so that yearly changes in freight spend do not impact the analysis. The
standard deviation for the transportation lane is also calculated (2). The standard deviation is
used in (3) to determine what orders should be designated as a disruption. An order is flagged as
a freight spend disruption if it is greater than one standard deviation away from the mean for
each lane (i,j). This decision came after reviewing the quantity of orders that fall into this interval
for each product (beef and poultry). The number of disruptive orders for freight spend falls
between 10-20% of all orders for each category.
Page | 29Lead Time Variation:
= − (4)
1
= ∑
=1 , ∀ , ∀ (5)
1
= √ ∑ 2
=1 ( , − ) (6)
1 , , ≥ + , , ≤
+
, , = { ∀ , , (7)
0 ℎ
Lead time variation is calculated in similarly to freight spend variation. First, the lead
time per order is calculated in (4), which is the difference between the received date and the
order data. The mean (5) and standard deviation (6) are calculated for all orders on each unique
transportation lane (i,j). An order will count as a lead time disruption if it is either one standard
deviation over the mean or one standard deviation below the mean. Ultimately, it was unclear
with the company data whether a shorter transit time or a longer transit time as compared to the
average was a disruption, so we modeled both in (7). The total number of orders that count as a
disruption account for 10-20% of all orders for both categories.
For the external data, the data are organized differently from each source. The primary
data sources are grouped together with respect to location and time. Table 1 shows a sample of
our data dictionary. The location and time specificities are the most granular level of detail that
we were able to find for these data. For data like severe weather, which have more specific
location categories, we can designate different regional impacts since our project company has
suppliers in clustered locations.
Page | 30Table 1 External Data Dictionary Data Source Data Description Key measurements Location Specificity Time Specificity USDA Slaughter Rates Volume (in lbs) State level Monthly NOAA Severe Weather $ Property Damage State level Daily USDA Product Recalls Volume (in lbs) National Day Opened USDA Imports/Exports Volume (in lbs) National Monthly USDA Wholesale Pricing Price (per lbs) National Monthly For the analysis, we chose to review our data on a national level. The decision to do this came from the notion that there is little information known about upstream suppliers. For example, if we dive into the slaughter rates of a specific state, we are unable to know if any of that slaughtered meat is in our supply chain. This same issue exists for severe weather events. We needed to make a similar decision with regard to time specificity, where we needed to aggregate all data per month. 3.4 Analysis Framework Once the internal and external data were aligned and processed, the next step in building our model was to explore the data, run multiple linear regression, and create a scenario analysis to quantify the financial and time impacts due to disruptions. 3.4.1 Exploratory Analysis After we fully aligned our internal and external data on the same time scale for analysis, there were 36-line items for each category to cover each month from 2018-2020. The internal Page | 31
and external data were combined for initial exploratory analysis to identify patterns and trends.
This portion of the modeling was done in Python using Pandas and Google Collab notebooks.
Before running our variables through regression, we standardize the data using standard
deviation to remove the biases from different data scales, and check for multicollinearity to
remove those variables that are highly correlated with each other.
Multiple linear regression is then performed on each of our three disruptive factors:
volume changes, freight charges, and lead time variation. A multiple linear regression formula
looks as follows:
y = β0 + β1x1 + β2x2 + ... + βpxp + ϵ
where,
y = dependent variable
xi = explanatory variables ∀ ∈ {1, … , }
β0 =y-intercept (constant term)
βi = slope coefficients for each explanatory variable ∀ ∈ {1, … , }
ϵ = the model’s error term (also known as the residuals)
The process includes modeling many iterations on the data to find the most statistically
significant variables for disruption. The regression analysis helps identify key risk factors, the
level of their impact, and the likelihood that the impact is statistically significant. It also helps us
understand what percent of the model is not yet explained. While there are many different
variables considered in the analysis, many of these were not found to have any significance and
Page | 32were removed from the model. For each regression model, we graph the absolute value of residuals to check for presence of heteroscedasticity to ensure that assumption for ordinary least squares regression is valid. In addition, we check the normality of the residuals to ensure that the variability of our model follows a normal distribution. Due to the nature of the project, regression is also performed with a lagging indicator. A lag is used to determine if supply shocks from one month take time to impact the company business; a hurricane may occur in June, but the supply shock from this disruption is not seen until July. 3.4.2 Scenario Analysis Once key risk factors were identified through regression modeling, some scenarios are created for each factor to analyze the potential overall impact on the supply chain. The minimum, mean, and maximum values for the independent variables in the regression output are used to create a worst-case and best-case scenario for each model. For the independent variables, the median and the mean did not show differences and we chose to use the mean to feed the model. The freight order disruption models calculate the number of expected shipments that fall outside of one standard deviation of the average freight spend for that lane. This information is then used to calculate the estimated cost impact of these variations. The estimated cost of a disruptive freight order is calculated by subtracting the average dollar amount of all non- disruptive orders from the average dollar amount of the disruptive freight orders. Similarly, the estimated number of days in monthly delay from disruptions can be calculated by multiplying the Page | 33
expected number of disruptive orders by the difference between average days to fulfill non- disruptive orders and disruptive order. This allows the company to quantify the cost and operational impact of disruptions in dollar amount and days. For the receipt weight regression models, the same process is followed for the scenario analysis; however, the regression estimates the variation in the total amount of volume ordered in a month. To quantity a potential impact for the company, the average weight of all orders is used to estimate the change number of orders due to the variation in receipt weight. In the next section, results and managerial insights from the proposed methodology are presented to help the sponsor company understand the impact of significant factors into their distribution operations under a few scenarios. Page | 34
4. RESULTS In this section, we present the results of two regression models for both poultry and beef that were found to have the most predictive power. The models chosen for poultry and beef have the highest R2 values for each of the disruption indicators. For poultry, the lead time disruption indicator is not included, and for beef the freight spend indicator is not included because these indicators were not found to be significant for their respective categories. For each of the regression models that do have a good predictive power, we then present the scenario analysis to help translate our findings into relevant impacts for the project company. 4.1 Correlation Analysis An important part of the analysis to prepare the data for the regression model was exploring the relationships between variables. Understanding the correlation between variables helps to point at any underlying trends within the data. Visually, this is presented as a heat map in Figure 6. Darker red colors indicate a strong positive correlation, meaning as one variable increases so does the other. Lighter colors indicate a strong negative correlation, meaning as one variable increases the other decreases, or the variables are inversely related. Page | 35
Figure 6 Variable Correlation Heat Map 4.2 Poultry The regression outputs are the two models that show the highest predictive power given the datasets. The lead time variation disruption indicator did not provide a robust output during the modeling process, so it is not included in the results. Additionally, many of the variables were found to be statistically insignificant – only two independent variables are found to have significance in both models. Page | 36
4.2.1 Regression Analysis Table 2 shows the summary statistics for the two regression models. Freight spend and receipt weight are the dependent variables where we find the most significance. The R2 values indicate how much variability in freight spend and receipt weight is explained by the independent variables, respective for each model. The total number of observations is equal to 36 - for one observation per month from the three years of company data. Each regression model has two independent variables which leads to 33 degrees of freedom on the residual. Only two independent variables are modeled because the other variables were found to be statistically insignificant or were removed earlier in the process due to multicollinearity. Table 2 Regression Model Outputs for Poultry Regression Output Regression 1 Regression 2 Dependent Variable Freight Spend Receipt Weight Adjusted R2 0.384 0.513 F-Statistic 11.91 19.46 # of Observations 36 36 Degree of freedom – Residuals 33 33 Degree of freedom - Model 2 2 Table 3 Regression Model 1 t- Variable Coefficient Std. Error Statistic P>|t| Intercept 9.3407 32.576 0.287 0.776 National Competitive Price -0.3621 0.121 -2.981 0.005 Net Export 0.0001 0.0000461 2.441 0.02 Page | 37
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