Measuring Disruption Indicators in Food Service Delivery Supply Chain

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-serv...

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Main Authors: Schwendenman, Amy, Li, Teng Yi
Format: Other
Language:en_US
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/1721.1/130960
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author Schwendenman, Amy
Li, Teng Yi
author_facet Schwendenman, Amy
Li, Teng Yi
author_sort Schwendenman, Amy
collection MIT
description 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.
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spelling mit-1721.1/1309602021-06-17T03:43:20Z Measuring Disruption Indicators in Food Service Delivery Supply Chain Schwendenman, Amy Li, Teng Yi Data Analytics Risk Management Supply Chain Strategy 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. 2021-06-16T16:56:05Z 2021-06-16T16:56:05Z 2021-06-16 Other https://hdl.handle.net/1721.1/130960 en_US CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ application/pdf
spellingShingle Data Analytics
Risk Management
Supply Chain Strategy
Schwendenman, Amy
Li, Teng Yi
Measuring Disruption Indicators in Food Service Delivery Supply Chain
title Measuring Disruption Indicators in Food Service Delivery Supply Chain
title_full Measuring Disruption Indicators in Food Service Delivery Supply Chain
title_fullStr Measuring Disruption Indicators in Food Service Delivery Supply Chain
title_full_unstemmed Measuring Disruption Indicators in Food Service Delivery Supply Chain
title_short Measuring Disruption Indicators in Food Service Delivery Supply Chain
title_sort measuring disruption indicators in food service delivery supply chain
topic Data Analytics
Risk Management
Supply Chain Strategy
url https://hdl.handle.net/1721.1/130960
work_keys_str_mv AT schwendenmanamy measuringdisruptionindicatorsinfoodservicedeliverysupplychain
AT litengyi measuringdisruptionindicatorsinfoodservicedeliverysupplychain