State of Michigan Agriculture Logistics and Supply Chain ... · Logistics and Supply Chain ......
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State of Michigan Agriculture Logistics and Supply Chain Assessment and Strategy
Recommendations
Michigan State University Department of Supply Chain Management
and Product Center Food-Ag-Bio
in association with
State of Michigan Department of Agriculture and Rural Development
Final Report
Submitted By:
Spartan Consulting Inc.
May 31, 2015
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Table of Contents
Executive Summary 3 Supply Chain Overview 5 Introduction & Purpose 9 Data Collection & Analysis 12 Design Specification Analysis: 17
Animal Processing 17 Feed Processing 21 Food Processing 24 Intermodal Transportation 26
Commodity Scenario Analysis: Soybeans 33 Corn 53 Wheat 66 Dry Beans 71
Summary of Findings and Next Steps 78 Proposed Implementation Planning and Next Steps 82 Appendix A: List of Figures 84 Appendix B: List of Tables 86 References 87
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Executive Summary
The initiative is funded through a Strategic Growth Initiative grant provided
by The Michigan Department of Agriculture and Rural Development (MDARD).
The Michigan Department of Agriculture & Rural Development Strategic Growth
Initiative began in the spring of 2014. The focus of this Strategic Growth Initiative
project is identifying potential logistics and supply chain improvements and
strategy recommendations for the State of Michigan Agriculture sector and how
these recommendations benefit value added processing and exports. The
commodities being studied are soybeans, corn, wheat and dry beans.
The engagement focuses on the assessment of the end to end agriculture
supply chain which begins with the purchase and application of seeds and
fertilizers, planting of the seeds, continues with the harvest and the transport of
products from the field to processing locations, continues with food/feed
processing, and ends with the sale of processed product for animal livestock or
consumer use. The supply chain for each agriculture commodity is unique and is
explained in greater depth in each commodity’s respective section of this report.
The information presented in this report is intended to demonstrate that the value
associated with implementing the logistics and supply chain strategy
recommendations will be substantial and the economic benefits will include
reduced cost or cost avoidance for all stakeholders, improved service for the
agriculture supply chain by reducing cycle times and delays for transport and
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processing, reduced supply chain risks for all stakeholders, and creation of new
jobs for the State of Michigan.
The primary recommendation includes further research into the building of
a new soybean processing plant. Primarily, research must be done to determine
the possibility of increasing Michigan’s animal processing industry in order to
justify additional soybean processing capacity.
Michigan State University’s assessment approach of analyzing the integrated
end-to-end supply chain provided excellent insights for Michigan agriculture economic
development benefits. Soybeans appear to be the most promising opportunity for
economic development given the livestock demand increase, new food/feed processor
opportunity and new animal processor opportunity. Soybeans will need an
implementation champion to assure the benefits of the integrated end-to-end supply
chain will become a reality. Implementation challenges may arise given the lack of
project participation by several stakeholders.
The purpose of this Spartan Consulting Inc. report is to provide thorough
documentation of the deliverables to date and provide the framework necessary for
implementing the project results. This report provides detailed documentation of the
proposed and validated methodology for industry assessment and provides supporting
documents necessary for data analysis developed by Michigan State University and
validated by stakeholders at five stakeholder workshops throughout the project
duration.
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Supply Chain Overview
The supply chain for agriculture commodities has many steps as shown in Figure
1. It begins with the purchase of raw materials including seeds as well as chemicals
and fertilizers that ensure proper development of the crop. Following the growing
season, the crop is harvested and moved to either an elevator for storage or directly to
a processor. Depending on the crop, several stages of processing might be required
including preparing the crop for human consumption or for livestock feed. If the crop
becomes animal feed, then the next step in the supply chain would be processing the
animals. At this point, the crop has reached the end of its supply chain and is ready to
be sold within the state or exported.
Figure 1: End to End Supply Chain for Commodities
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The supply chain for agriculture commodities is complex as shown in Figure 2.
The complexity exists because each commodity has a unique supply chain including
different requirements and input for each stage. Each commodity also requires different
processes to transform the crop into the final end product. The end product then will be
sent to a wide variety of end customers. During the team’s research, the complexity of
each commodity’s supply chain was recognized. The uniqueness of each supply chain
was also considered.
Throughout each commodity’s supply chain, there is a constant exchange of
information through various processes (Figure 3). The process starts as the raw
materials and ends with the product being delivered to the end consumer. Along the
supply chain, information is exchanged both internally and externally. Information
Figure 2: Complexity in the Supply Chain
(Hope, 2005)
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sharing allows for timely transactions and efficient use of materials. The supporting
functions that enable information sharing and relationship building are key to proper
execution throughout the supply chain.
The integrated supply chain incurs numerous expenses throughout the product
lifecycle as Figure 4 illustrates. It is the sum of all of these expenses that determine the
end product’s total cost. By further studying each cost component of the supply chain,
opportunities for cost savings can be identified. The goal in total cost analysis is to
breakout individual costs so that a stakeholder can identify how to save money
implementing recommendations that offer solutions that reduce or eliminate costs
incurred during the supply chain.
Figure 3: End to End Integrated Supply Chain Model
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Figure 4: End to End Integrated Supply Chain (The Total Cost Analysis)
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Introduction and Purpose
The project scope includes soybeans, corn, wheat and dry beans. Spartan
Consulting, Inc. has orchestrated data collection activities, reviewed data
collection and research methods, developed supply chain recommendations, and
vetted recommendations with the participating stakeholders shown below.
• Michigan Soybean Promotion Committee • Corn Marketing Program of Michigan • Michigan Wheat Program • Michigan Bean Commission • Michigan Farm Bureau • MSU Department of Supply Chain Management • MSU Product Center • MDARD • MEDC • MDOT
Spartan Consulting, Inc. was responsible for data collection, analysis and
modeling, developing supply chain recommendations, validating
recommendations with stakeholders, and proposing implementation guidelines.
Throughout the project, the Spartan Consulting team conducted a series of five
workshops to review findings with stakeholders as well as to consider feedback
from the stakeholder group. The final deliverable for Spartan Consulting is to
summarize project findings through this stakeholder report.
The first workshop was conducted on February 21, 2014 and primarily
focused on data collection. During this phase of the project the team conducted
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preliminary interviews with stakeholders to analyze the current state of the
agricultural supply chain in Michigan.
Data Collection Define data collection needs Collect information on the current state of operations Collect information on current supply chain and logistics infrastructure
assets Collect information on global best practice deliverables
The second workshop on April 17, 2014 summarized the current state of
agricultural commodity flows and created process flow maps for each commodity.
During Workshop 2, the team worked with the stakeholders to create templates
to send out to Michigan elevators to obtain additional data to support supply
chain recommendations.
Analysis & Modeling Define network modeling tool to use for analysis Prepare data in format to be used in the analysis and modeling step Input data into modeling tool and validate model Analyze alternative agriculture logistics and supply chain improvement
scenarios using network modeling tool Workshop 3 was conducted on November 20, 2014. Unfortunately, there
was limited participation from Michigan elevators. Due to this there was a limited
data set to analyze. Therefore, the main focus of Workshop 3 was on best
practices surrounding potential supply chain improvements, as well as insights
into potential supply chain recommendations.
Develop Supply Chain Recommendations Develop logistics and supply chain improvement recommendations Refine supply chain improvement recommendations Document recommendations for review by various stakeholders
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The fourth workshop was held on March 3, 2015. The primary focus of
Workshop 4 was to develop supply chain scenarios for each commodity group.
For example, the Spartan Consulting Inc. team presented a preliminary soybean
model developed based on the scenario of an additional soybean processor in
the state. During the workshop the stakeholders provided feedback that was
used to improve the recommendations for the final workshop.
Validate Recommendations with Stakeholders Meet with stakeholders to discuss logistics and supply chain
recommendations Discuss opportunities and obstacles to solution implementation Document agreed upon and recommended supply chain solutions Share solution with all stakeholders to assure buy-in of solution
The final workshop was conducted on May 1, 2015 and centered on
developing supply chain recommendations and implementation planning. For
example, the final soybean model was presented to stakeholders and the team
discussed how the soybean model could be used by external users to assess the
feasibility of a new soymeal processing facility.
Implementation Planning Document the proposed implementation plan and assign resources to
work on activities in the implementation plan Work with all stakeholders regarding next steps to implement project
The Purpose of This Document is Threefold:
1. Clarify proposed and validated methodology for supply chain improvement recommendations. This document should assist others attempting to replicate this research and evaluation model.
2. Present Spartan Consulting findings and next steps. 3. Itemize the data and other documents created by Spartan Consulting.
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Data Collection and Analysis
Data Collection for Elevators
Process:
Elevators play a crucial role in the agricultural supply chain because they
store grain after harvesting and before processing. The team wanted to further
understand the role elevators played in the supply chain by studying their
capacity and how they
were utilized. The team
first identified where the
elevators were located.
Figure 5 shows the
location and capacity of
all the grain elevators in
the Lower Peninsula
(MDARD, 2014). The
figure illustrates that most
of the grain handling in
Michigan occurs south of
the Saginaw Bay area.
Figure 5: Location and Capacity of Michigan Elevators (MDARD, 2014)
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In order to simplify the data collection process, the team contacted general
managers of 13 major elevator firms within Michigan via email. Most of the firms
contacted operate more than one elevator. Each email contained an excel
spreadsheet designed to collect information from the past 3 years regarding:
Elevator Inflows • Year, Month, Commodity, Origin of Commodity, and Volume
Elevator Outflows
• Year, Month, Commodity, Destination of Commodity, Next Step in the Supply Chain, Transport Mode, and Volume
The signature line of this email included Dr. Chris Peterson and Dr. David Closs.
Result:
Two larger elevators in the state provided useable data for analysis.
Major Challenges: • Lack of participation • Companies unwilling to share information • Data that was returned did not follow the given template • Inconsistencies between sources
Processor Data Collection
Process:
The team contacted 21 processors via email. Each email contained an excel
spreadsheet designed to collect information from the past 3 years regarding:
Elevator Inflow • Year, Month, Commodity, Origin of Commodity, and Volume
Elevator Outflow
• Year, Month, Commodity, Destination of Commodity, Next Step in the Supply Chain, Transport Mode, and Volume
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The signature line of this email included Dr. Chris Peterson and Dr. David Closs.
Result:
We received limited responses from the processors we contacted, and
almost no useable data.
Major Challenges: • Lack of participation • Companies unwilling to share information
Initial Interviews
The team conducted initial interviews with each of the participating
stakeholders to determine:
• Roles of stakeholders and their relationship to the commodities • Opportunities or problems the stakeholders saw within their supply chain • Contribution opportunities of each stakeholder to the Growth Initiative
Issue Prioritization
During Workshop 1, the team worked with the stakeholders to identify some
of the problems they saw in their respective supply chains. Prior to Workshop 2,
stakeholders were asked to rank their priorities for what they saw as the greatest
opportunity to focus the project on. These scores were then summarized to
create a prioritization matrix with the highest scores being the most important.
The top four issues, each with a score of 50 or greater were:
• Michigan Food Processing • Transport & Logistics • Exports and Value Chain • Michigan Animal Processing
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Once priorities were determined, the team began to research current state
situations and best practices for each of the issues. The stakeholder’s priorities
are outlined in Table 1.
Table 1: Stakeholder Issue Prioritization Matrix
Overall Soybeans Corn
Dry Bean
Wheat General Stakeholders
Inbound Fertilizer 36 1 2 5 8 4 2 1 2 7 2 2 2.86
MI Food Processing 74 7 6 8 5 7 8 8 6 5 7 7 6.86
MI Animal Processing 50 6 8 4 1 5 4 3 5 3 6 5 4.43
Border Congestion 26 2 5 1 3 1 1 2 1 2 4 4 2.14
Exports & Value Chain 51 3 7 3 2 6 5 6 7 4 5 3 5.14
Transport & Logistics 66 5 3 7 6 8 7 7 8 1 8 6 6.43
IT & Decision Aids 47 4 1 6 4 3 3 4 3 8 3 8 4.57
Regulatory Congestion 46 8 4 2 7 2 6 5 4 6 1 1 3.57
*Score each issue with a number of 1-8, where 1 is the lowest priority and 8 is the highest priority. No number may be provided twice.
Data Analysis
End to End Supply Chain Process Maps were created using a combination
of stakeholder interviews and industry research. Process maps were evaluated
and critiqued by stakeholders during Workshop 1 with refinements made prior to
Workshop 2.
Supply and Demand Heat Maps were created using the software Tableau.
Harvest volumes were provided by United States Department of Agriculture
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(USDA) from the National Agricultural Statistics Service (NASS). Elevator
capacities were provided by MDARD, and elevators volumes were collected from
MDARD.
All of the above mentioned data analysis tools such as The Process Maps,
Commodity Percentage Flow Through, and Supply and Demand Heat Maps, will
be displayed and discussed in each commodity’s respective section.
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Design Specification Analysis
Research regarding design specifications was conducted regarding the topics of
animal processing, feed processing, food processing, and intermodal transportation.
Best practices were collected by researching online, interviewing stakeholders, and
conducting primary research. The focus of the research was in two key areas, structure
and operations. Within these two areas, additional information was collected on a
number of subcategories:
Structure: • Optimal Global Architecture • Supplier Integration • Internal Integration • Customer Integration • Strategic Alignment • IT Integration • Insourcing/Outsourcing • Visibility
Operations:
• Talent Management • Inovation Management • Risk Management • Agility (Parameterization) • Planning Effectiveness • Supply Chain Segmentation • Performance Management • Change Management
Animal Processing Design Specifications
Four major design specifications specific to animal processing were identified,
these consisted of physical environment, sanitation and hygiene, safety and shelf life,
and odor management.
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Physical Environment:
Permanent equipment should be completely sealed to the floor, wall or ceiling.
Floor mounted equipment should be sealed or elevated six inches to allow for easy
cleaning (Saskatchewan Ministry of Health, 2011). Ventilation systems should be
designed, installed and maintained in order to prevent contaminants from collecting on
walls and ceilings and dripping on food and food contact surfaces (Saskatchewan
Ministry of Health, 2011). In areas where food is processed, packaged, stored or
received, and where utensils and equipment are kept or cleaned, the floors, walls and
ceilings should be constructed using specific material. This materials should be easily
cleaned, durable, impervious, light in color, smooth, non-toxic, and non-corrosive
(Saskatchewan Ministry of Health, 2011).
Sanitation/Hygiene:
There are several principles of meat hygiene that should be considered.
Microbial contamination should be reduced or eliminated by applying heat treatment at
the final processing state for extension of shelf life of products. It is important to
minimize microbial growth in raw materials, semi-manufactured goods, and final
products by storing them at a low temperature (Saskatchewan Ministry of Health,
2011). Companies can prevent microbial contamination of raw materials, semi-
manufactured goods, and final products during meat product manufacture through
absolute cleanliness of tools, working tables, machines, as well as hands and outfits of
personnel (Saskatchewan Ministry of Health, 2011).
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Safety and Shelf Life:
Safety and shelf life involves employing predictive models to predict how well
products are protected against the growth of pathogenic bacteria. The use of predictive
growth models reduces the requirements for laboratory tests and documentation of
food safety and shelf life (Christensen, 2010). Results are presented using growth
curves and relevant values in order to allow the quality manager to easily evaluate
safety of a specific recipe or establish shelf life at different storage conditions
(Christensen, 2010).
Odor Management:
The meat processing industry has the potential for generating large quantities of
solid wastes and waste water, which can lead to offensive odors. Some management
techniques are simple odor masking systems. The most efficient long term solution is a
mixture of wastewater treatment, solid waste encapsulation, and plant scrubbing and
direct injection to the stacks (OMI Industries, 2014).
Examples of Best Practices:
Craig Mostyn Group’s Linley Valley Facility:
Australia’s first laser guided pork cutting robot was installed by this organization.
This robot uses a highly accurate laser imaging system to take measurements of a
carcass to produce cuts that are based on an animal’s individual anatomical features
(Mu, 2011). High precision cutting reduces contamination and wastage of meat due to
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accidental cutting of the stomach from 4% to less than 1%. This also delivers a more
predictable cut of meat with consistent quality and increased yield of 1% per carcass
(Mu, 2011).
Karan Beef:
South Africa’s premier beef
product supplier which
accommodates 130,000 heads of
cattle. They utilize trimmings from
on-site deboning plant to pack a
range of value added products
including marinated, pickled, and
par-cooked products and
hamburger patties (Interpack,
2014). The packaging line is
composed of a box forming machine and four in line Triaflex Delta Robots (shown in
Figure 6), which by means of a smart vision system can pick flow-wrapped frozen
hamburgers, and load them into three flap lid boxes with different configurations and
sizes (Interpack, 2014). This reduces component costs and the number of interfaces by
using a single controller for the entire system based on a standard technology which
can be managed by a single engineer (Interpack, 2014).
Figure 6: Karan Beef Triaflex Delta Robot (Interpack, 2014)
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Feed Processing Design Specifications
In animal feeding operations, animals are kept and raised in confined
facilities that handle animals, feed, manure, urine, and operational equipment. In
feed processing facilities, feed is brought to the animals rather than animals
grazing outside in fields. The team started by collecting data for animal feeding
operations in Michigan to model the current situation. Major design
specifications identified include equipment, personnel training and safety.
Equipment:
Feed processing plant equipment technology is expensive and advances
rapidly. Under these circumstances, proper equipment selection is a very
important factor in plant design. Currently, many plants are using manual control
of operating conditions and mix measurement monitoring. In the last few years,
technologies have changed, and the ability for software to analyze large amount
of data quickly provides more advanced real time conditions (FAO and IFIF,
2010). The team recommends that plants should move from manual to computer
controls of operating conditions and mix measurement monitoring.
Personnel Training:
Employees engaged in manufacturing and operations should be trained in
all national regulations and industry standards relating to product quality and
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safety regulations (FAO and IFIF, 2010). These trainings sessions should be
documented to keep track of training level and gaps in training. Also,
effectiveness of trainings should be evaluated by several pre and post training
skill evaluation. Providing employees with job previews and making them aware
about their roles and responsibilities will be helpful to maintain feed safety (FAO
and IFIF, 2010). Government regulations are getting stricter in food processing
and better understanding of those regulations is critical to operate effectively in
today’s changing environment. Along with developing skillset in employees
processing units should also ensure that employees are qualified to maintain
equipment within regulations (FAO and IFIF, 2010). Food borne illnesses, along
with higher number of recalls, have resulted in increased demand for regulations,
guidelines and economic losses.
Food Traceability
Through research, the team identified opportunities to change food
traceability processes that would result in better food traceability, and in times of
recall, a quicker response with batch level data (Bailey, Robb, and Checketts,
2005). This will culminate into significant economic and reliability benefits in the
long term. In current scenario major stages involved in beef processing at a
packing plant are illustrated in Figure 7 (Bailey, Robb, and Checketts, 2005).
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This current process breaks traceability at several stages. Our recommendation
is to develop two-step process. The first step of this process is the
implementation of identification system from farm to slaughter. The second step
of the process would be to track meat after it leaves the packing plant. This two-
step approach creates a "break" in traceability at the processing plant.
Figure 7: Schematic of Wholesale (Packer) Sector Stages and Linkage (Bailey, Robb, and Checketts, 2005)
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Food Processing Design Specifications
Major design specifications for food processing involved internal food
traceability and a new microwave technology used for soybean processing.
Internal Food Traceability:
In today’s ever changing world, global supply chains are more complex than ever
and food items often pass through several touch points. Today more than ever, food
products regularly cross national boundaries. When any problem arises with the food
quality and food items must be recalled, a robust food traceability program becomes
critical to minimize the damage, economic loss and diminished brand value (Opara and
Mazaud, 2001). During the team’s research, the team identified a strong need to trace
food back to its origin during a food related recall or outbreak. Traceability to and from
storage elevators is important, but at the same time internal traceability is equally
important (Opara and Mazaud, 2001). Our research suggest to focus on two important
points:
• Capture all information related to all incoming, internal and outgoing commodity lot activities to track time and efficiency.
• Analyze information to define new handling procedures to optimize logistics costs and to minimize food safety risk.
Food Traceability Optimization:
Currently, elevators are using the elevator optimization model, which is limited to
identifying food items on the lot level. Based on research, the team recommends using
a lot aggregation optimization model for minimizing traceability effort. This method uses
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a mathematical multi-objective mixed integer programming model (MIP) with a goal to
reduce costs and increase traceability in food supply chain (Thakur, Wang, and
Hurburgh Jr., 2009). This method uses grain lots from different bins for blending while
using the minimum number of storage bins. This model provides an effective method
for minimizing the traceability effort by minimizing the food safety risk. Moreover this
model also allows in using the maximum volume of grain present in a given bin
(Thakur, Wang, and Hurburgh Jr., 2009). The use of fewer bins for blending shipments
is also easier logistically and can lead to additional savings in terms of grain handling
cost and time.
Benefits of multi-objective mixed integer model will be to: • Minimize level of lot aggregation • Minimize number of storage bins used for blend grain for a shipment • Minimize discounts from blending
Examples of Best Practices:
Progressive Processing LLC:
A subsidiary of Hormel Foods Corp, based in Austin, TX. Hormel Foods is
a multinational manufacturer and marketer of consumer-branded food and meat
products. The Progressive Processing site is a state-of-the-art production facility
in Dubuque, Iowa spanning over 348,000 square feet. It is the first new
production facility that the company has built in more than 25 years and when
complete it will cost $89M. It currently employs about 90 team members and can
expand up to 300 people (Hormel Foods Corporation, 2010).
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Progressive Processing was built according to the Leadership in Energy
and Environmental Design (LEED) Green Building Rating System, developed by
the U.S. Green Building Council for environmentally sustainable construction. It is
expected to be one of the first manufacturing plants and the only refrigerated
food processing facility to be LEED certified at any level (Hormel Foods
Corporation, 2010). The facility will use at least 25% less energy and water than
a plant built to meet current building codes and industry standards and was
constructed using materials with more than 36% recycled content. Hormel Foods
believes they will recoup the extra cost necessary to construct the
environmentally friendly facility during the first two years of operation (Hormel
Foods Corporation, 2010).
Intermodal Transportation Design Specifications
The goal for this analysis is to better understand the current transportation
and intermodal facilities being used by Michigan agriculture, study the gaps and
understand what improvements could be made.
The key improvement areas the team identified to narrow these gaps are:
• Achieve cost savings through efficient transportation modes • Increase the speed of commodity movement • Protect the quality of the commodity being moved • Enhance intermodal transportation.
Metrics measured regarding transportation include (Agora, 2010): • Increase of flow factor • Control of shunting services • Supply of road trucking services
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• Extension of terminal opening time • Bonus-malus incentives for the use of interim storage space • Separation of rail-side and roadside handling • Task management according to pre-notification • Punctual rail services
The team first looked to understand where the crops were grown. Then, they
sought to understand the transportation available at these locations. Accordingly, the
team looked at the cropland distribution overlaid with the freeway and rail lines. The
cropland road interface is shown in Figure 8. This figure shows the thumb region is not
connected by the interstate system which provides difficulty to those looking to move
crops from this region. Next, the team sought to identify how the crop location is
influenced by railroad. Figure 9 shows the railroad connections are well distributed.
However, research revealed that there is a need for maintenance and repair on many
railroads in the Upper Peninsula as well as the Thumb region. The maintenance on
these rail routes will be essential to move crops away from the major product areas
such as the Saginaw Bay area.
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National Railway
CSX Transportation
Norfolk Southern Railway
Consolidated Rail Corporation (Owned by CSX and NS) Other Railroads
Cropland Data Layer:
Figure 8: Cropland with Interstates and Highways (NASS, 2014)
Figure 9: Major Railways (MDOT, 2014)
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Transloading facilities transfer material by using several modes of
transportation. While in transit, shipments are often transferred from one mode
of transportation to another. Figure 10 provides all the transloading facilities in
Michigan where the mode of transportation can be changed.
Examples of Best Practices
The State of California
California has the following intermodal hubs which are considered state-of-
the-art facilities. Michigan can look at these major projects for building intermodal
facilities and making modernizing existing intermodal facilities.
Figure 10: Transload Locations (MDOT, 2014)
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• Intermodal Container Transit Facility (ICTF) modernization (Intermodal Transfer Facility, 2008)
• Southern California International Gateway (SCIG) construction (Port of Los Angeles, 2013)
• Alameda Corridor- near dock and on dock rail facilities (Alameda Corridor Transportaion Authority, 2014)
The State of Wisconsin
Wisconsin has a similar geographical location to Michigan as well as
similar harvest volumes, therefore it makes for a good comparison state.
Wisconsin has introduced a number of rural and light density rail funding
programs to improve connectivity. Two programs of interest are the Freight
Railroad Preservation Program and Freight Rail Infrastructure Improvement
Program, which aim to maintain continued rail service in abandoned rail tracks
and remote regions (Huntington, 2012). Also, the Wisconsin Department of
Transportation Multimodal Freight Network prioritizes highway corridors and
segments and gives WisDOT planning and programming staff more information
to prioritize freight projects (Huntington, 2012)..
BoxXpress:
BoxXpress is a licensed private rail operator in Germany specializing in
reliable and fast transportation of overseas containers between the German
seaports and inland destinations. Port of Hamburg has EUROKOMBI-Terminal,
which is an efficient rail station for combined cargo transport that helps with the
movement of goods.
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RETRACK Consortium:
The RETRACK Consortium is a long-haul cargo transport from road to rail,
creating an effective and scalable freight corridor. The main objective of the
RETRACK project is to develop, demonstrate and implement an innovative and
market-tested rail freight service along an East-West trans-European corridor
(Retrack, 2015). High level of intra-rail competition will stimulate improvement in
rail service quality, the competitive position of rail operators in international
freight market, and the volumes of freight carried by intermodal rail operations
(Retrack, 2015).
Diverging Diamond Interchange:
Implementation of Diverging Diamond Interchange (DDI) in new roadways
servicing Intermodal centers (ATS/American, 2014). There are many safety and
operational benefits from the implementation DDI. The safety benefits include
fewer conflict points (14 for DDI, 26 for conventional) and better sight distance at
turns. The operational benefits include better storage between the ramp
terminals, lanes with multiple assignments in all directions and better signal
network synchronization (ATS/American, 2014).
Key Improvement Opportunities:
Through its research the team identified key improvement opportunities that
would be benefit Michigan agriculture. The recommendations are as follows:
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• Leverage Michigan Department of Transportation (MDOT) financing programs for shipper and railroad loans to fund track improvements and trans load facilities.
• CSX’s Grain Express Load/Unload Program offers financial incentives for enrolled customers to load or unload a unit grain train within 15 hours. Michigan elevators can utilize this program to obtain substantial cost savings and also invest in efficient equipment for material handling (CSX Corporation, 2012).
• Maximize use of CSX TRANSFLO terminals in Detroit, Grand Rapids, Wixom, and Melvindale to obtain cost advantage (CSX Corporation, 2012).
• CN is offering Fleet Integration Program for grain shippers who are using privately owned, covered hopper cars (CN, 2014).
• EModal.com provides a way for intermodal customers to manage truck registries, appointments, dispatching, chassis rental billing and maintenance and repair (Advent Intermodal Solutions, 2012).
• Transitioning shipping from truck to railway has cost savings potential. As Table 2 shows below, each commodity could save millions of dollars by moving 10% of its transportation to rail.
Table 2: Railway Shipment Opportunities (Michigan Agri-Business Association, n.d.) Commodity Volume
Shipped Current Rail Shipment
Current Rail Quantity
10% increase in Rail Shipments
Cost Savings
Corn (bushels) 40M 50% 20M 24M $2M Soybeans (bushels) 40M 80% 32M 36M $2M
Wheat (bushels) 40M 40% 16M 20M $2M
Dry Beans (hundredweight) 3M 50% 1.5M 1.8M $3M Total Potential Savings $9M
A 10% increase in rail shipments leads to $9M in cost savings (based on savings of $0.50 per bushel)
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Soybean Scenario Analysis Current State
Soybeans move through different stages and pass through several touch points.
The process starts with preparation and cultivation of the crop at the farm. Soybeans
then move from the farm either directly to a processing facility or to a storage elevator
and then to a processing facility. Approximately 80% of soybeans are sent to be
processed into soy meal, and 20% are processed into soy oil (Reinholt, 2014). The
end use of soybean products in the United States is human consumption, animal feed,
or export to other countries. Figure 11 below shows the end to end supply chain of
soybeans and details the life cycle of soybeans from crop preparation to consumption
(Reinholt, 2014).
Figure 11: End to End Supply Chain of Soybeans (Reinholt, 2014)
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Production
In 2012, Michigan farmers produced 83.2M bushels of soybeans throughout 43
counties. The process map, current flow, and production locations of Michigan’s
soybean supply chain can be seen below in Figure 12 (Reinholt, 2014).
Table 3 provides the Michigan production volume in 2013 compared to the entire
U.S. this was provided by NASS. In 2013, 1.9M acres of soybeans were planted in
Michigan, or 2.48% of the total acres planted in the U.S. Per acre productivity for 2013
in Michigan was higher than the U.S. average, but prices were lower (NASS, 2014).
Figure 12: Soybean Process Map Current Base Case (Reinholt, 2014)
x
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Table 3: Michigan Production Volume Comparison With US (2013) (NASS, 2014) Amount Produced (bu/acre) 2012 Price
Michigan 44 bu/acre (83.16 M bushel) $12.50/bu
United States 43.30 bu/acre (3317.33 M bushel) $12.70/bu
Table 4 outlines the quantity of soybean flows through different steps of the supply
chain. Quantities are in terms of soybeans produced in Michigan.
Table 4: Soybean Flow Through Supply Chain (Reinholt, 2014)
Figure 13 illustrates where Michigan soybeans are produced and the
number of elevators by county. In Figure 13, on the left side image, darker colors
represent higher levels of soybean production (NASS, 2014). On the right image, the
numbers refer to the number of elevators in the county while the colors represent total
elevator capacity for the county, with darker colors representing higher capacities
36
(MDARD, 2014). There is a strong correlation between soybean production, the
number of elevators and elevator capacity at the county level.
Demand
The demand for soybeans in Michigan is approximately 21M bushels. Michigan is
home to one soybean processing plant, located in Zeeland, which has processing
capacity of 10M bushels annually (Reinholt, 2014). The remaining 11M bushels of
soybeans used in Michigan must, therefore, be transported out of state to be
processed. The processes soybeans are then transported back to Michigan, primarily
for animal consumption. Table 5 provides a detailed grain flow analysis of Michigan
Figure 13: Soybean Production & Elevator Location by County (MDARD, 2014; NASS, 2014)
37
soybean use. In addition, Figure 14 shows the breakdown of soybeans exported to
various locations. Export data was provided by only one major elevator, which handles
approximately 2.76M bushels, or 3.5% or Michigan’s production. The assumption
being made is that the export ratios remain consistent for the majority of Michigan’s
soybeans.
Figure 14: Soybean Outflow Locations (Confidential Elevator Data, 2014)
Table 5: Soybean Outflow Analysis (Confidential Elevator Data, 2014)
38
Figure 15 shows soybean demand throughout Michigan, based on zip code,
separated by whether it is likely being fulfilled by the current in-state processor or by
an out-of-state processor. The assumption being made in this figure is that the current
facility in Zeeland is fulfilling the demand for the locations nearest to it until reaching it’s
10M bushel capacity, and everything else is being fulfilled by out-of-state processors.
Export Markets
Michigan provides approximately 2.5% of the U.S. soybean export market.
Soybeans make up approximately 21% of all U.S. agriculture commodity exports (Ye,
2014). Figure 16 shows the breakdown of various commodities and their relative sizes.
Figure 15: Demand Fulfillment for In-State and Out of State Processors
39
Figure 17: US Soybean Exports (% Share by Volume) (Ye, 2014)
Figure 16: US Agriculture Commodity Exports (Ye, 2014)
40
Of all soybeans exported from the U.S., approximately 63% are sent to China.
Figure 17 shows the breakdown of international export locations from the U.S..
While it makes up 63% of the total market, China primarily buys soybean oil.
Figure 18 shows the top export locations of both soybean meal and soybean oil. Figure
19 shows Michigan’s major export locations for 2013 and 2014.
Figure 18: Soybean Product Export Locations (unit: millions of bushels) (USDA, 2015)
02,0004,0006,0008,000
2013 Volume 2014 Volume
Figure 19: Volume for Export Locations (unit: bushels) (USDA, 2015)
41
Analysis The team’s analysis involves the quantitative and qualitative benefits and risks that
would likely arise from building an additional soybean processing plant in
Michigan. The model built for this analysis considers factors such as the optimal
location for a potential processing plant, a range of capacity ratings for a new plant, as
well as variables such as the amount of increase in animal production and various cost
assumptions. Assumptions of the soybean model include:
Increase in animal consumption will be proportional among all counties Soymeal yield of 47.5lbs per bushel Crush Margins will vary between domestic and export commodity soymeal, as
well as domestic and export specialty soymeal Market share assumption is applied on the net demand for MI soymeal Cost data for new plant is from a Remco Study done in 2004 One time new facility cost uses a 10 year compound average growth rate
based on PPI Annual operating cost uses a 10 year compound average growth rate of 2%
per year Assets are depreciated using straight line method over 10 years with 10%
salvage value Transportation rates are equal everywhere ($.18 per ton-mile) For transportation costs, “export” is the distance to South Bend, IN Soybean basis will increase $.10-$.35 for new processing facility Job creation based on IMPLAN software
Stakeholders who contributed to development and testing of the soybean model
include:
Michigan Soybean Promotion Committee Michigan Farm Bureau Economic Development Alliance, St. Clair County Michigan Department of Transportation
42
Model Findings This report assesses the potential for an additional soybean processing plant in
the state. In order to justify additional soybean processing in the state, additional
animals need to be raised. In order to justify additional livestock numbers, additional
livestock processing capacity needs to be created. It appears that the dairy, eggs and
perhaps the turkey and broiler industries are willing to expand their production and
processing capacity (Michigan Soybean Association, 2015). A new soybean processing
plant in Michigan would increase processing capacity beyond the current demand.
Therefore, we also looked into how an increase in Michigan’s animal processing
industry would increase current demand.
Hog production is likely to increase but whether or not it will increase enough to
justify a processing plant is a critical consideration. The recent agreement with
Clemens Food Groups to open a 10,000 head per day facility in Coldwater dramatically
improves the likelihood that a soybean processing plant would be successful. Hogs are
the dominant species with respect to soybean meal consumption in Michigan, and an
increase in their numbers will drive soybean meal demand more than other animal
species (Michigan Soybean Association, 2015).
Current conditions are well suited to expanded livestock production. Meat prices
are high and feed prices are declining. Livestock production appears to be moving back
to the Midwest from the Southeast and the West. Water issues and transportation
costs appear to favor the Midwest over other parts of the country. Increased production
in Michigan reflects this. Egg production in Michigan has increased at a faster rate than
43
any other state. Substantial increases in soybean production have driven the interest in
increased soybean processing (Michigan Soybean Association, 2015).
Another market fundamental that supports the growth of the livestock industry is
the growth of the global middle class, particularly in China. As incomes increase in
developing countries the demand for animal protein will increase at a faster rate than
the demand for other types of food. The U.S. food supply is considered safer than the
food supply in many other countries, which also bodes well for future exports.
We found that in order to achieve economies of scale that provide the most cost
effective operations, the optimal capacity of a new processing plant is 3000 tons per
day (tpd). However, there are risks associated with a processing plant that large, which
include, but are not limited to the need to significantly increase the livestock production
in-state, the ability to market excess soymeal outside the state of Michigan, as well as a
heavy reliance on higher crush margins for specialty soymeal. Table 6 shows the
baseline livestock data for Michigan as of 2012, as well as the proposed increase in
Michigan livestock used in this scenario.
44
Table 6: Baseline and Assumed Increase in the Number of Animals
Baseline (2012) % Increase
Livestock
(# of heads)
Dairy 375,000 10%
Layers & Broilers 14,500,000 20%
Turkey 5,000,000 0%
Hogs 2,300,000 50%
The proposed increase would boost Michigan soymeal consumption from
500,000 tons annually to 682,000 tons. The total demand for soymeal in Michigan
would amount to 1894 tpd, or 1044 tpd net demand after accounting for demand
fulfilled by current in-state processing.
Table 7 shows the soybean model output and the amount of soymeal in tpd that
would need to be marketed outside of Michigan under the initial proposed increase in
Michigan livestock, as well as what would still need to be marketed outside of the state
if livestock further increased in the state of Michigan. As the table shows, there would
need to be 1,498 tpd of commodity soymeal sold outside of Michigan, or 539,280 tons
annually, which is more than Michigan’s entire soymeal consumption as of 2012.
45
Table 7: Results of the Soybean Model
Scenario 2B above also shows that if livestock numbers increased enough to
nearly triple the current in-state soymeal demand, a 3000 tpd facility would still need to
market 865 tpd or 311,400 annual tons of commodity soymeal outside of Michigan.
Table 8 shows the same scenario, but with a 1500 tpd facility.
With flat net demand of 1044 tpd; IRR = 12.9% and Payback period = 7 Yrs
Scenario 2 New processor supply (tpd)
Domestic Balance (tpd) Export Balance (tpd) Total Margin for Processor
Commodity Specialty Commodity Specialty
3,000 752 84 1,498 666 $24,060,000
With increased net demand of 1923 tpd; IRR = 13.8% and Payback period = 7 yrs
Scenario 2b New processor supply (tpd)
Domestic Balance (tpd) Export Balance (tpd) Total Margin for Processor
Commodity Specialty Commodity Specialty
3,000 1,385 154 865 596 $25,130,000
46
Table 8: Results of Soybean Model with 1500 tpd Facility
In Scenario 1, the 1500 tpd facility is a better fit for Michigan demand with the
initial increase in livestock numbers, as the facility would only need to market 373 tpd or
134,280 tons of commodity soymeal annually. If demand were to increase further, the
1500 tpd facility would not have to export any soymeal even if they only captured 80%
of the net demand in Michigan. The 1500 tpd facility is a better fit to fill in-state demand
for soymeal, but with an IRR of 6% and a payback period of 10 years it would not
realize the economies of scale that a 3000 tpd facility represents.
Another risk of a 3000 tpd facility under the proposed scenario would be the
reliance on improved crush margins for specialty soymeal. Table 9 highlights the
impact of a 20% increase, as well as a 20% decrease to in-state and export specialty
crush margins.
With flat net demand of 1044 tpd; IRR = 6% and Payback period = 10 Yrs
Scenario 1 New processor supply (tpd)
Domestic Balance (tpd) Export Balance (tpd) Total Margin for Processor
Commodity Specialty Commodity Specialty
1,500 752 84 373 291 $10,890,000
With increased net demand of 1923 tpd; IRR = 6% and Payback period = 10 yrs
Scenario 1b New processor supply (tpd)
Domestic Balance (tpd) Export Balance (tpd) Total Margin for Processor
Commodity Specialty Commodity Specialty
1,500 1,125 154 0 221 $11,760,000
47
Table 9: Impact of Changing Specialty Margins
It is clear to see that the specialty margins have a large impact on the profitability
of a new plant, as a 20% decrease in specialty margins results in an internal rate of
return (IRR) drop of 2.84 percentage points. Likewise, a 20% increase in specialty
crush margins would result in an IRR increase of 2.69 percentage points. Overall a
3,000 tpd would present the highest returns for a new soybean processor in Michigan
due to the economies of scale advantages, but there are significant risks associated
with a facility of that size that would need to be addressed.
The study analyzed current consumption of soybean meal and looked at three
scenarios involved with increased animal production. Current consumption would
make a soybean processing plant problematic; however, a 30% increase in livestock
numbers would make a small scale but still commercial sized soybean processing plant
feasible. Increased hog production would have a particularly large impact on soybean
meal demand. Figure 20 shows results of a sensitivity analysis for a 3,000 tpd facility.
48
Assumptions in the analysis include:
Facility will fulfill 80% of Michigan's net demand Demand ratio of 90% commodity, 10% specialty Production ration is 75% commodity, 25% specialty
Figure 20: Model Livestock Demand and Export Sensitivity
In order to increase the probability of success, a soybean processing plant
should be located near the center of the Lower Peninsula or in the Saginaw Bay area.
This is near major areas of soybean production and the region is becoming increasingly
important in livestock production, especially dairy. This location would also be far
enough away from existing soybean processing plants to minimize competition from
those plants.
49
Figure 21 shows the
potential demand that
could be fulfilled by adding
an additional processor in
Michigan. This map
assumes that the new
processor is located in
Ithaca, and that demand is
fulfilled based on sending
products to locations
nearest the processor until
their capacity is met.
While the focus on
much of this report is on
the demand for soybean
meal, soybean processing also generates soybean oil. A firm that has experience in
both processing and marketing soybean oil as well as meal is more likely to be
successful than a new entrant into this industry. Soybean processing is a low margin
industry and the ability to control costs is extremely important, which is another reason
why an experienced firm is more likely to be successful.
Despite the positive trends in livestock production, there are several barriers to
increased livestock production and livestock processing, which is a necessary
Figure 21: Future State Potential Demand Fulfillment
50
precondition for increased soybean processing. It is important to determine which
communities are interested in increased animal processing. Community support is
important to overcome whatever opposition to a processing plant may exist. Also,
developing new labor saving technologies would improve the acceptance of animal
processing. The perceived problems of animal processing are greatest for pork
processing and to a lesser extent turkey and broiler processing. It is less of an issue
for egg and dairy processing. Employment opportunities for agri-food firms are not well
advertised, which is a barrier to the growth of the entire agri-food system. Despite the
state’s relatively high unemployment rate, there does not appear to be a strong interest
among workers in agri-food jobs. Finding qualified workers that are interested is a
particular barrier to the dairy industry.
A factor that hurts Michigan’s competitiveness is the poor state of its
infrastructure. Roads are in poor shape. Additional funding, most likely in some type of
tax or registration fees, will be necessary to improve the state of the roads. Improved
access to Canada through the construction of a second bridge in the Detroit/Windsor
region would improve access to the Canadian market. The state has good rail service
connected to the larger cities but short line service on rural routes heading north and
south is generally not considered as good. This is not likely to change as long as
demand on the short lines is high in the fall and early winter and tapers off during the
rest of the year. Improved internet access would improve the economic performance of
rural areas. The state also needs a natural gas policy. Michigan has a great deal of
natural gas and large storage facilities for storing natural gas, but pipelines to rural
51
areas are lacking. The lack of access to natural gas increases the cost of handling
grain and maintaining grain quality.
Regulations are a consistent point of contention between members of the agri-
food system and the general public. The reality is that regulation will continue to be an
issue. Consumers, retailers and other groups are becoming more demanding with
respect to how food is produced throughout the supply chain. Farmers and processors
will need to be more responsive to these demands. Environmental sustainability will be
of increasing importance for farmers and processors as retailers and other firms
institute their sustainability policies. Animal welfare will be another concern that will
need to be addressed in order to meet the demands of retailers and consumers.
Increasingly, these issues will be determined by economic agents other than
government agencies. Research and extension will need to play a role in aiding the
affected industries in developing policies to address these regulatory issues.
Soybean processing creates two primary products, soybean meal which is used
for animal feed and soybean oil which is primarily used as a cooking oil, and is also
used an input into other food products. To be successful a soybean processor will
need to effectively market both those products. An experienced firm will be more
successful than a new entrant. Soybean processing is a narrow margin industry that
exhibits economies of scale; as a result the ability to control costs will be very
important.
There is one soybean processing plant located in Zeeland. This location is
excellent to meet the needs of the growing poultry industry and there are some large
52
hog and dairy farms in the area as well. However, there is no room for growth at that
location and a new facility in the region may create too much competition. A facility
located in Central Michigan or the Saginaw Bay region would have the potential to
obtain soybeans without excessive competition from the facility in Zeeland or the large
soybean crushing plants located in Ohio and Indiana.
Determining the impact of a soybean plant on soybean prices is difficult to
determine. A conservative estimate is that the price would rise 5 to 10 cents a bushel
in the area that is serviced by the plant. While there is some variability in price from
year to year it should be noted that soybean prices in Ohio and Indiana tend to be
around 30 cents a bushel higher than the price in Michigan. In addition to these states
having closer access to major markets, they are also home to several large soybean
processing plants. The most likely scenario is that soybean prices will increase about
20 to 30 cents in Michigan if there were another soybean processing plant in the state.
Increased soybean processing in Michigan would increase the profitability of soybean
farming and has the potential to reduce the feed cost of some livestock producers in the
state.
53
Corn Scenario Analysis Current State
Corn moves through different stages and passes through several touch points
along its supply chain. The process starts with preparation and cultivation of the crop at
the farm. Corn then is moved from farms to elevators and then to processors. From
processors, processed corn is directed to either ethanol production, human
consumption, used as livestock feed or sold in the export market (Zook, 2014). Figure
22 provides end to end supply chain map of corn. It explains life cycle of corn from crop
preparation to consumption in detail.
Figure 22: Corn End to End Supply Chain (Zook, 2014)
Figure 23 shows the current state process map for corn in Michigan. As the map
highlights, there are four main uses for Michigan corn; exporting corn directly, food-
54
processing use, feed processing, and ethanol production. The team’s analysis of the
corn supply chain in Michigan was centered on these four uses.
Figure 23: Corn Process Map (Zook, 2014)
The amount of Michigan corn that flows through the various stages of the supply
chain and final markets is shown in Table 10. Currently, 115M bushels or 34% of
Michigan’s crop is used in-state to produce ethanol. In total, 145M bushels or 43% of
the crop is exported out of Michigan, and 80M bushels or 23% of the crop is consumed
in-state for feed use (Zook, 2014).
55
Table 10: Corn Flow Through Supply Chain for 2013 (Zook, 2014)
Production
Figure 24 highlights where corn is grown in Michigan, as well as where Michigan
grain elevators are located throughout the state. As the figure indicates, the elevators
are concentrated in the counties that produce the most corn in Michigan.
56
Figure 24: Corn Production and Elevator Capacity by County (NASS, 2014); (MDARD, 2014)
Table 11 provides comparative data of Michigan corn acreage with U.S. corn
acreage. In 2013, Michigan planted 2.6 Million acres of corn, 2.73% of total acreage
devoted to corn in the U.S. This figure compares growth efficiency (bushels per acre)
as well as prices per bushel for Michigan and the rest of the U.S.
Table 11: Michigan Corn Production Volume Comparison with U.S. (2013) (NASS, 2014)
Production Efficiency 2013 Price
Michigan 155 bu/acre $4.05/bu
United States 158.8 bu/acre $6.15/bu
57
Locations of in-state corn processors and ethanol plants are highlighted in Figure
25, along with elevator capacities by county. The darker the color in the production map
the greater the level of corn production. The number of elevators and capacity is
strongly correlated with the level of corn production. As the figure indicates, the corn
processors in Michigan are not concentrated around the highest producing counties in
the state.
Figure 25: Locations of Corn Processors and Grain Elevator Capacity (MDARD, 2014)
Demand
Data analysis was hindered by the lack of participation from in-state elevators,
but the data that was collected helped to highlight where Michigan corn is shipped to
58
out of state. As Figure 26 indicates, the majority of MI corn is sent to the southeast
United States. It is assumed that the data that we could not collect would represent a
similar outflow map
Figure 26: Corn Outflow Locations
The corn being sent to the Southeast United States is used for primarily for hog
and poultry feed, as well as some ethanol production (Confidential Elevator Data,
2014).
(Confidential Elevator Data, 2014)
59
Again, due to limited participation the data represents a small portion of Michigan
production, but it is assumed that the rest of the data would reflect similar end users of
Michigan corn.
Historical usage statistics are represented in Table 12. As the historical data
shows the corn usage in Michigan stays within a small range in terms of percentage of
Michigan production. It should be noted that the 2014-15 figures are estimates.
Table 12: Michigan Corn Usage Statistics (numbers in millions of bushels) (The ProExporter Network, 2014)
Figure 27 shows the largest export markets of U.S. and Michigan corn. As
pictured Japan, Mexico, and China are the leading buyers of U.S. corn.
2012-13
Percent of Supply 2013-14
Percent of Supply 2014-15
Percent of Supply
Production 318 349 342
Supply 343 369 371
Feed / Residual use 72 21% 103 28% 91 25%
Feed use 62 18% 74 20% 69 19%
Dairy 29 8% 35 9% 33 9%
Beef cattle 5 1% 6 2% 6 2%
Hogs 16 5% 18 5% 17 5%
Poultry 10 3% 11 3% 11 3%
Other 3 1% 3 1% 3 1%
Dry milling-Ethanol/
Processing in state 93 27% 101 27% 101 27%
Total use in state 165 48% 203 55% 192 52%
Net Exports 178 52% 166 45% 179 48%
60
However, as Figure 28 shows, Canada is the leading buyer of Michigan corn
followed by Mexico and Jamaica. Canada is a natural market for Michigan corn due to
Figure 28: Top Exporters of Michigan Corn by Percentage of Crop (USDA, 2015)
Figure 27: Top Exporters of US Corn by Percentage of Crop (USDA, 2015) (
Japan37%
Mexico25%
China13%
Venezuela6%
Taiwan3%
S. Korea2%
Canada2%
Saudi Arabia2%
Cuba2%
Jamaica1% Others
7%
00.050.1
0.150.2
0.250.3
0.350.4
0.450.5
0.550.6
0.650.7
0.750.8
0.850.9
0.951
Canada Mexico Jamaica Honduras Trinidad &Tobago
Sweden Barbados
20132014
61
the close proximity. However, the spike in 2014 Canadian consumption was a product
of a poor crop year, and should not be considered typical in terms of export potential.
Figure 29 highlights what MI corn is used for, as well as which livestock groups
are consuming the most Michigan corn.
Figure 29: Michigan Corn Utilization
Analysis
Figure 30 is a description of the proposed scenario analysis that was the
foundation for developing the corn model. This scenario is a result of discussions with
industry leaders and project stakeholders.
(Michigan Corn Growers Association, 2013)
62
Figure 30: Corn Supply Chain Scenario Proposal
The model was used to analyze the impact of additional livestock in Michigan,
increased in-state ethanol production, as well as shipping corn to Canadian processors
directly from the farm. The rationale behind the model was to assess how increasing
livestock and ethanol production in Michigan would impact corn consumption, as well
as the total supply chain savings that could result from large Michigan farmers shipping
corn directly to Canadian feed processors. There are some constraints associated with
the model. First, there needs to be a strategy in place to increase the livestock numbers
in Michigan in order to increase consumption. Next, there must be a market for the
increased ethanol production. Finally, Michigan farmers must have the trucking
capability to ship corn to Canadian processors directly.
63
Assumptions that were used to develop the corn consumption model include:
Livestock consumption was computed on a per head basis from the 2012 baseline data
Any livestock increases in the model would impact corn consumption in Michigan instantly
A new ethanol plant is intended to compute only the impact on Michigan corn consumption and basis improvements, and does not take into account the feasibility of a new plant.
The model does not take into account whether Canadian processors would be willing or able to accept shipments directly from Michigan farmers
The baseline margin savings calculated if farmers were to ship to Canadian processors directly is computed as $0.25 per bushel, but the number can be adjusted to reflect different ranges of total system savings between Michigan farmers and Canadian processors
The basis impact of a new ethanol producer in Michigan is computed as $0.0193 per additional million gallons of ethanol produced
Ending stocks in the model are calculated as an average percent of total supply, or 6.8% annually.
Figure 31 summarizes the key findings from the model. Increasing the total
number of livestock in Michigan (10% increase in dairy, 20% increase layers and
broilers, 0% change turkeys, and 50% increase for hogs) would boost Michigan corn
consumption for feed from 67M bushels to 77M.
64
Figure 31: Corn Model Findings
Table 13 shows the total system savings that could be achieved if Michigan
farmers were to ship directly to Canadian corn processors. The savings were calculated
by assuming exports to Canada would remain constant around 10M bushels.
Production was calculated on a per county basis with 38% of corn leaving each county
by rail. Next, out of the remaining production in each county, it was assumed that only
35% of the production would be from farmers large enough to ship directly to Canadian
processors. Finally, the nearest 10M bushels were calculated from each county to
London, Ontario, using a standard rate of $0.18 per ton mile in order to calculate the
transportation cost. The overall system savings split between Michigan farmers and
Canadian processors is represented by the total bushels shipped multiplied by the
margin savings from bypassing elevators.
65
Table 13: Impact of Shipping 10 Million Bushels of Corn Directly to Canada
In conclusion, additional livestock in Michigan, ethanol production in Michigan, and
increased farm direct shipments to Canadian processors could result in significant
supply chain improvements. The proposed livestock increase in Michigan would result
in an additional 10M bushels of corn consumed in-state, which would reduce
transportation costs, as well as increase local basis for farmers. A new ethanol
processor using 20M bushels of corn would have a $21.6M impact on Michigan’s
economy as a result of the increased basis. Finally, Michigan farmers shipping corn
directly to Canada would result in a $2.5M savings split between MI farmers and
Canadian processors.
66
Wheat Scenario Analysis Current State
Michigan is home to approximately 6,300 wheat producers throughout 40
counties, and they produced 45M bushels of wheat in 2013 (NASS, 2014). The wheat
production process starts with preparation and cultivation of the crop at the farm. Wheat
then moves from the farm to storage facilities either on the farm itself or to one of 112
storage elevators (MDARD, 2014). From storage facilities, wheat moves to milling
processors. The end use of wheat products in the United States is human consumption
or exportation to other countries. Figure 32 below shows the end to end supply chain of
wheat and details the life cycle of wheat from crop preparation to consumption.
Figure 32: Wheat End to End Supply Chain (Pollock-Newsom, 2014)
67
Figure 33 shows wheat production and processors location in the state. It shows
that Huron, Sanilac and Lenawee counties are the largest wheat producing counties.
Four of the six large wheat processors are located in the southern part of the state.
The complete wheat process map is shown in Figure 34.
= processor
Figure 33: Wheat Production and Processor by County (NASS, 2014); (MDARD, 2014)
68
Figure 34: Wheat Process Map (Pollock-Newsom, 2014)
Demand
Michigan’s 112 wheat storage elevators have the capacity to store approximately
30M bushels to be processed by the six wheat milling facilities, which are primarily
located in the southern portion of the state. The milling companies process
approximately 40M bushels annually, with the remainder being exported for processing.
The processed wheat is then sent either to bakeries or cereal companies to be used in
consumer goods (70%) or exported to be used out of state (30%) (Pollock-Newsom,
2014). This supply chain flow is shown in more detail in Table 14.
69
Table 14: Wheat Flow Through Supply Chain (Pollock-Newsom, 2014)
Analysis Our analysis follows the wheat from harvest through its end use, which is either
human consumption or animal feed. The purpose of our analysis was to identify
opportunities for cost savings and increased efficiencies throughout Michigan’s wheat
supply chain. A general trend across the U.S. of reducing the amount of acreage
devoted to wheat creates an opportunity for Michigan wheat farmers to fill this demand.
Michigan wheat can be used to supplant wheat currently coming from other states. A
20% increase in wheat production compared to the 2013 figures would increase wheat
revenue by $60.3M, and would have total economic activity of $105M while providing
70
an additional 785 jobs in grain production and 1,131 jobs overall. The primary
implications of our wheat analysis include:
A reduction in wheat acreage in the U.S. creates an opportunity for increased
wheat production and processing in Michigan Increased production of red wheat varieties could increase demand for Michigan
wheat and refined milling process increase market for red wheat Potential for increased milling capacity in Michigan
Key assumptions in our research include:
There is an increased interest in locally produced and sourced food The market for white wheat remains relatively strong Most bakeries are located relatively close to population centers Cereal companies and others have a vested interest in maintaining the white
wheat industry Growers remain interested in producing wheat
Primary constraints and obstacles include:
Market research must be done to determine the varieties of wheat that processors prefer
White wheat disease (deoxynivalenol) issues need to be addressed Increase in resource allocations for research and development of the wheat
supply chain
Moving forward, determining the optimal balance between red/white wheat and
spring/winter will be helpful to formulate a long term strategy. In addition, changes in
prices of other commodities such as soybeans and corn could encourage farmers to
devote more or less acreage to wheat.
71
Dry Beans Scenario Analysis Current State
Michigan farmers produce approximately 3.4M hundredweight (cwt) of dry beans
annually (NASS, 2014). Unlike the other crops, dry bean production is concentrated in
a few counties in the Central and East Central part of the state. Huron and Tuscola
counties are the leading producers. Figure 35 shows Michigan’s dry bean production
and processors by county.
Figure 36 shows the process map of the dry bean supply chain, beginning with
the production and going through to retail locations.
Figure 35: Michigan Dry Bean Production and Processing by County (NASS, 2014), (MDARD, 2014) and (Cramer, 2014)
72
Figure 36: Dry Beans Process Map (Cramer, 2014)
Due to the nature of the industry and confidentiality concerns, dry bean
stakeholders were unwilling to provide us with significant data. The team was able to
put together a supply chain flow through map (Table 15), but were unable to fill in all of
the details based on the information provided. The stakeholders did share that
domestically, dry beans are primarily transported by truck or railroad. Internationally,
beans staying in North America are often transported via railroad, or via container ships
overseas.
73
Table 15: Dry Beans Flow through Supply Chain
Analysis
The team’s analysis looks into increasing the supply of dry beans produced to
increase the quantity exported, and the additional revenue and job opportunities that
would be created. In addition to increasing the supply of Michigan dry beans, we
looked into potential opportunities to alter the mix of beans grown throughout Michigan.
Recent years have seen changes in taste preferences, partially due to an increase in
the population of Latin Americans throughout the U.S. shown in Figure 37. In 2009,
there were approximately 48.4M Latin Americans in the U.S., which made up 15.75% of
the U.S. population. By 2013, there were nearly 54M, making up 17.1% of the
population (United States Census Bureau, 2014). The countries with the most
representation are Mexico, Puerto Rico and Cuba (United States Census Bureau,
2014).
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Figure 37: The Growth of the Hispanic Population 2009-2013 (United States Census Bureau, 2014)
One major commonality among those populations is high demand for chickpeas,
a bean with a $115M U.S. market that Michigan farmers do not participate in. Table 16
shows popular beans in a variety of countries as well as varieties grown in Michigan.
48.36
50.74
51.94
52.96
53.99
15.75%
16.40%
16.67%
16.87% 17.08%
15.0%
15.5%
16.0%
16.5%
17.0%
17.5%
47
49
51
53
55
2009 2010 2011 2012 2013
Millio
ns
Hispanic Population Hispanic as % US
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Key Implications of this scenario analysis consider the following:
An increase in Hispanic populations in the U.S. is driving a change in varieties of dry beans in demand
An increased supply of dry beans and expanded exports will generate increased revenue and job opportunities
Will potentially create a need for additional processing capacity and shipping capacity, particularly in the Saginaw Bay & Thumb region
May not drive the creation of new assets, but could lead to increased utilization of current assets
Key assumptions of scenario include:
Hispanic populations throughout the U.S. will continue to increase Consumption of beans is changing based on changing population demographics Lower corn and soybean prices will increase the acreage devoted to dry beans Normalization of relations between Cuba & the U.S. will increase the demand for
dry beans in Cuba
Table 16: Popular Dry Bean Varieties in Various Countries (The Bean Institute, 2015)
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Apart from changes due to Hispanic populations, consumption trends of legume
crops worldwide show increasing demand going to Africa, Asia and the Americas (see
Figure 38) (Akibode and Maredia, 2011).
Figure 38: World Pulse Consumption Projections (Akibode and Maredia, 2011)
Based on the assumption that Hispanic populations in the US will continue to rise
and that these changing demographics will continue to alter the consumption mix, it
makes sense for Michigan farmers to consider changing the varieties that they are
currently growing. In addition, if we see lower prices of other commodities such as corn
and soybean, farmers can begin devoting more acreage to dry beans. A 20% increase
in dry bean output compared to 2013 will increase revenue by approximately $28M. It
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would also provide additional employment opportunities in bagging and processing
facilities.
In order to accomplish this, Michigan farmers must overcome increasing
competition from Minnesota and the Dakotas, as well as Canadian farmers. The team
also considered that there might be increasing demand for dry beans coming from
Cuba as U.S. relations are normalized with the country. The White House has recently
eased export restrictions on agriculture goods, and allowed US banks to open accounts
with Cuban financial institutions in order to facilitate business payments and allow bank
card transactions with Cuba. Cubans traditionally prefer black bean and chickpea
varieties. Proactively ensuring that those varieties are available can help Michigan
farmers to prepare for demand increases from those changes.
Summary of scenario value proposition:
A 20% increase in output compared to 2013 will increase revenue from dry bean sales by approximately $28M
A change in bean varieties grown would allow MI farmers to enter more popular markets (example: switch from cranberry beans to chick peas)
Total economic impact of $48.9 million in Michigan 525 additional employment opportunities will be created especially in bagging
and processing facilities.
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Summary of Findings and Next Steps
The following summarizes the findings and recommendations for each commodity.
Soybeans
Building a new soybean processing facility in Michigan will help to increase the
state’s economic activity, provide at least 750 additional job opportunities (direct,
indirect, and induced combined), and decrease dependence on imported soybean
products. Our analysis found that in order to achieve the greatest economy of scale
advantages, a plant with capacity of 3,000 tons per day (tpd) would be optimal. A
3,000 tpd facility would provide an internal rate of return (IRR) of 13.5%, and we
anticipate that it would increase soybean prices in Michigan by approximately $0.20-
0.30. We compared this to a facility with a capacity of 1,500 tpd, which would provide
an IRR of only 6%. The primary issues with this plan are that the facility would rely
significantly on selling soymeal outside of Michigan; hence the need for an efficient
export supply chain. In addition to the need for exporting the excess soymeal, livestock
numbers must significantly increase in order to justify a new processing facility in
Michigan. Further research must be completed to determine the correct mix of
commodity soymeal versus specialty soymeal, as specialty margins can have a
significant impact on total margin, but the market for specialty products is unclear.
By using the model created by Spartan Consulting, it was found that a significant
increase in animal processing in the state of Michigan would increase soymeal to 650K
tons, up from current capacity levels which is at 500K tons. This would allow for an
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additional 410K in in state processing capacity, which also leads to decreased logistical
costs.
Corn
Interviews with industry stakeholders led to the development of a model used to
analyze the impact of additional livestock in Michigan, increased in-state ethanol
production, as well as shipping corn to Canadian processors directly from the farm.
The rationale behind the model was to assess how increasing livestock and ethanol
production in Michigan would impact corn consumption, as well as the total supply
chain savings that could result from large Michigan farmers shipping corn directly to
Canadian feed processors. There are some constraints associated with the model.
First, there needs to be a strategy in place to increase the livestock numbers in
Michigan in order to increase consumption. Next, there must be a market for the
increased ethanol production. Finally, Michigan farmers must have the trucking
capability to ship corn to Canadian processors directly. The model shows that
increasing the total number of livestock in Michigan (10% increase in dairy, 20%
increase layers and broilers, 0% change turkeys, and 50% increase for hogs) would
boost Michigan corn consumption for feed from 67 million bushels to 77 million. The
proposed livestock increase in Michigan would result in an additional 10M bushels of
corn consumed in-state, which would reduce transportation costs, as well as increase
local basis for farmers. A new ethanol processor using 20M bushels of corn would have
a $21.6M impact on Michigan’s economy as a result of the increased basis. Finally,
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Michigan farmers shipping corn directly to Canada would result in a $2.5M savings split
between MI farmers and Canadian processors.
Wheat
A general trend across the U.S. of reducing the amount of acreage devoted to
wheat creates an opportunity for Michigan wheat farmers to fill this demand. Michigan
wheat can be used to supplant wheat currently coming from other states. A 20%
increase in wheat production compared to the 2013 figures would increase wheat
revenue by $60.3M, and would have total economic activity of $105M while providing
an additional 785 jobs in grain production and 1,131 jobs overall.
Dry Beans
Our analysis looks into increasing the supply of dry beans produced to increase
the quantity exported, and the additional revenue and job opportunities that would be
created. In addition to increasing the supply of Michigan dry beans, we looked into
potential opportunities to alter the mix of beans grown throughout Michigan. Based on
the assumption that Hispanic populations in the U.S. will continue to rise and that
changing demographics will continue to alter the consumption mix, it makes sense for
Michigan farmers to consider changing the varieties of beans that they are currently
growing. In addition, if we see lower prices of other commodities, such as corn and
soybean, farmers can begin devoting more acreage to dry beans. A 20% increase in
dry bean output compared to 2013 will increase revenue by approximately $28M. It
81
would also provide additional employment opportunities in bagging and processing
facilities.
This will only be possible if Michigan is able to compete with farmers from
Minnesota, the Dakotas, and Canada. On the other hand additional opportunities, have
opened up with Cuba. As U.S. relations normalize, Michigan could explore exporting
dry beans to Cuba.
Project Conclusions
Michigan State University’s assessment approach of analyzing the integrated
end-to-end supply chain provided excellent insights for Michigan agriculture economic
development benefits. Soybeans appears to be the most promising opportunity for
economic development given the livestock demand increase, new food/feed processor
opportunity and new animal processor opportunity. Soybeans will need an
implementation champion to assure the benefits of the integrated end-to-end supply
chain will become a reality. Implementation challenges may arise given the lack of
project participation by several stakeholders
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Proposed Implementation Planning and Next Steps
Michigan States University’s assessment approach of analyzing the integrated
end-to-end supply chain provided excellent insights for Michigan agriculture economic
development benefits. Soybeans appears to be the most promising opportunity for
economic development given the livestock demand increase, new food/feed processor
opportunity and new animal processor opportunity.
Due to the fact that a new soybean processing plant in Michigan would
increase processing capacity beyond the current demand, we also looked into
how an increase in Michigan’s animal processing industry would increase current
demand. The model that was built based on input from project stakeholders was
developed to be used by livestock interests, grain handlers, and others with an
interest in Michigan’s agriculture industry to explore variables that can impact
costs and revenues throughout the soybean supply chain. We found that in order
to achieve economies of scale that provide the most cost effective operations,
the optimal capacity of a new processing plant is 3,000 bushels per day. In order
for this size plant to be feasible, livestock numbers in Michigan must be
increased significantly. At this point, a strategy must be developed to expand
livestock production in the state. Expanding livestock production may also justify
a new facility for animal processing, leading to greater potential for increased
economic activity in Michigan.
Implementation challenges may arise given the lack of project participation by
several stakeholders. Throughout this assessment data collection was challenging as
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many elevators and processors did not participate. A lack of trust and data sharing
between market participants works to the disadvantage of the entire system. Soybeans
will need an implementation champion to assure the benefits of the integrated end-to-
end supply chain will become a reality.
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Appendix A: List of Figures
Figure 1: End to End Supply Chain for Commodities 5 Figure 2: Complexity in the Supply Chain 6 Figure 3: End to End Integrated Supply Chain Model 7 Figure 4: End to End Integrated Supply Chain (The Total Cost Analysis) 8 Figure 5: Location and Capacity of Michigan Elevators 12 Figure 6: Karan Beef Triaflex Delta Robot 20 Figure 7: Schematic of Wholesale (Packer) Sector Stages and Linkage 23 Figure 8: Cropland with Interstates and Highways 28 Figure 9: Major Railways 28 Figure 10: Transload Locations 29 Figure 11: End to End Supply Chain of Soybeans 33 Figure 12: Soybean Process Map Current Base Case 34 Figure 13: Soybean Production & Elevator Location by County 36 Figure 14: Soybean Outflow Locations 37 Figure 15: Demand Fulfillment for In-State and Out of State Processors 38 Figure 16: US Agriculture Commodity Exports 39 Figure 17: US Soybean Exports (% Share by Volume) 39 Figure 18: Soybean Product Export Locations 40 Figure 19: Volume for Export Locations 40 Figure 20: Model Livestock Demand and Export Sensitivity 48 Figure 21: Future State Potential Demand Fulfillment 49 Figure 22: Corn End to End Supply Chain 53 Figure 23: Corn Process Map 54 Figure 24: Corn Production and Elevator Capacity by County 56 Figure 25: Locations of Corn Processors and Grain Elevator Capacity 57 Figure 26: Corn Outflow Locations 58 Figure 27: Top Exporters of US Corn by Percentage of Crop 60 Figure 28: Top Exporters of Michigan Corn by Percentage of Crop 60 Figure 29: Michigan Corn Utilization 61 Figure 30: Corn Supply Chain Scenario Proposal 62 Figure 31: Corn Model Findings 64 Figure 32: Wheat End to End Supply Chain 66
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Figure 33: Wheat Production and Processor by County 67 Figure 34: Wheat Process Map 68 Figure 35: Michigan Dry Bean Production and Processing by County 71 Figure 36: Dry Beans Process Map 72 Figure 37: The Growth of the Hispanic Population 2009-2013 74 Figure 38: World Pulse consumption Projections 76
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Appendix B: List of Tables Table 1: Stakeholder Issue Prioritization Matrix 15 Table 2: Railway Shipment Opportunities 32 Table 3: Michigan Production Volume Comparison With US (2013) 35 Table 4: Soybean Flow Through Supply Chain. 35 Table 5: Soybean Outflow Analysis 37 Table 6: Baseline and Assumed Increase in the Number of Animals 44 Table 7: Results of the Soybean Model 45 Table 8: Results of Soybean Model with 1500 tpd Facility 46 Table 9: Impact of Changing Specialty Margins 47 Table 10: Corn Flow Through Supply Chain for 2013 55 Table 11: Michigan Corn Production Volume Comparison with US (2013) 56 Table 12: Michigan Corn Usage Statistics 59 Table 13: Impact of Shipping 10 million Bushels of Corn Directly to Canada 65 Table 14: Wheat Flow Through Supply Chain 69 Table 15: Dry Beans Flow through Supply chain 73 Table 16: Popular Dry Bean Varieties in Various Countries 75
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References
"A Diamond Interchange With a Twist." ATS/American, 2014. Web. 6 Feb. 2015. <http://www.divergingdiamond.com/benefits.html>.
"About Moderization Project." Intermodal Transfer Facility - Joint Powers Authority, 2008. Web. 30 Nov. 2015. http://www.ictf-jpa.org/modernization_project.php
"About RETRACK ." Retrack, 2015. Web. 12 Feb. 2015. <http://www.retrack.eu/site/en/about.php>.
"About Us." eModal. Advent Intermodal Solutions, 2012. Web. 30 Jan. 2015. <https://www.emodal.com/AboutUs.aspx>.
Akibode, Sitou, and Mywish Maredia. "Types of Dry Beans Commonly Consumed by Nationality." Department of Agricultural, Food and Resource Economics of Michigan State University, 27 Mar. 2011. Web. 19 Apr. 2015.
"Alameda Corridor Fact Sheet." Alameda Corridor Transportaion Authority, 2014. Web. 30 Nov. 2015. <http://www.acta.org/projects/projects_completed_alameda_factsheet.asp>.
"An Overview of Infrastructure Opportunities and Challanges for Michigan Agriculture, Agri-Business, and Rural Development." Michigan Agri-Business Association, n.d. Web. 30 Jan. 2015. <http://www.miagbiz.org/images/e0186601/MABA_Infrastructure_railroads.pdf>.
"Area/Partners of Destination and Commodities Export." United States Department of Agriculture, 30 Mar. 2015. Web. 30 Mar. 2015.
Bailey, DeeVon, James Robb, and Logan Checketts. "Perspectives on Traceability and BSE Testing in the U.S. Beef Industry." Choices 2005: 293-97. Web. 27 Apr. 2015. <http://www.choicesmagazine.org/2005-4/grabbag/2005-4-15.pdf>.
Christensen, Hardy. "Beef Shelf Life Prediction Model." Danish Meat Research Institute, June 2010. Web. 14 Oct. 2014.
Corn Uses. Michigan Corn Growers Association, 2013. Web. 25 Feb. 2014. <http://www.micorn.org/corn-uses>.
"Covered Hopper Fleet Integration Program ." CN, 2014. Web. 7 Feb. 2015. <https://www.cn.ca/-/media/Files/Your-Industry/Documents-Grain/covered-hopper-fleet-integration-program-2014-en.pdf>.
88
Cramer, Joe. Personal interview. 2014.
"CSX in Michigan." CSX Corporation, 2012. Web. 6 Feb. 2015. <http://www.csx.com/index.cfm/about-csx/company-overview/state-fact-sheets/michigan/>.
FAO and IFIF. Good practices for the feed industry – Implementing the Codex Alimentarius Code of Practice on Good Animal Feeding. FAO Animal Production and Health Manual. 2010. No. 9. Rome.
"Food Processing Facility Best Management Practices." Saskatchewan Ministry of
Health, June 2011. Web. 18 Oct. 2014. <https://www.saskatchewan.ca/government/government-structure/ministries/health>.
"Good Practices Manual for the Management of Intermodal Terminals." Intermodal
Terminals. Agora, Apr. 2010. Web. 18 Sept. 2014. <http://www.intermodal-terminals.eu/content/e3/e18/e128/index_eng.html>.
Hope, Bruce. "Identifying Research Needs for Risk Assessment of U.S. Food Supply
Security." Society of Toxicology. 13 Apr. 2005. "Hormel Foods Celebrates Grand Opening of State-of-the-Art Production Facility in
Dubuque, Iowa." Press Releases. Hormel Foods Corporation, 20 Mar. 2010. Web. 28 Oct. 2015. <https://www.hormelfoods.com/Newsroom/Press-Releases/2010/03/20100330>.
Huntington, Frank. "Wisconsin Freight Railroad Assistance Programs ." Wisconsin
Department of Transportation , 2012. Web. 30 Nov. 2015. <www.wisconsinplanners.org/attachments/Conference%20Presentations%202012/Huntington%20WisDOT%20rail%20programs.pdf>.
“Karan Beef and Cama: A State of the Art Meat Processing Plant." Interpack, 1 Jan.
2014. Web. 27 Oct. 2014. <http://www.interpack.com/cipp/md_interpack/lib/pub/tt,oid,22446/lang,2/ticket,g_u_e_s_t/~/KARAN_BEEF_AND_CAMA_A_STATE_OF_THE_ART_MEAT_PROCESSING_PLANT.html>.
Knudson, William. Personal interview. 2014.
Michigan Corn Usage Statistics. The ProExporter Network, 2014. Web. 18 Oct. 2014. <http://www.proexporter.com>.
89
"Michigan Grain Dealers List by County." Michigan Department of Agriculture and Rural Development, 10 Apr. 2014. Web. 10 Apr. 2014. <www.michigan.gov/mdard/>.
"Michigan's Railroad System." Michigan Department of Transportation. Michigan Center
for Shared Solutions Department of Technology, Management, and Budget, June 2014. Web. 12 Feb. 2014. <http://www.michigan.gov/documents/mdot/RailNorthernLowerPeninsulaEconDevlp_476969_7.pdf>.
Michigan Soybean Association. “The Livestock and Soybean Nexus Report Prepared for the Michigan Department of Agriculture and Rural Development.” May 2015.
Mu, Rita. "WA Pork Processing Facility to Use Nation’s First Laser Guided Cutting Robot." Food Maganize, 10 Jan. 2011. Web. 10 Feb. 2015. <http://www.foodmag.com.au/news/wa-pork-processing-facility-to-use-nation-s-first->.
National Agriculture Statistics Service. United States Department of Agriculture, n.d.
Web. 19 Feb. 2014. <http://www.nass.usda.gov/Statistics_by_State/Michigan/Publications/County_Estimates/index.asp>.
Opara, Linus U., and Francois Mazaud. "Food Traceability from Field to Plate." Outlook
on Agriculture 30.4 (2001): 239-47. Web. 14 Oct. 2015. Pollock-Newsom, Jody. Personal interview. 2014. Reinholt, Keith. Personal interview. 2014. Safely Eliminate Odors Created by Food Processing." Food Processing. OMI
Industries, n.d. Web. 27 Oct. 2014. <http://odormanagement.com/markets-served/food-processing/>.
"Southern California International Gateway Project Description ." Port of Los Angeles,
2013. Web. 18 Nov. 2014. <http://www.portoflosangeles.org/NOP/SCIG/NOP_SCIG_PROJECT_DESCRIPTION2.pdf>.
Thakur, Maitri, Lizhi Wang, and Charles R. Hurburgh Jr. "A Lot Aggregation
Optimization Model for Minimizing Food Traceability Effort." Iowa State University, June 2009. Web. 27 Apr. 2015. <http://lib.dr.iastate.edu/abe_eng_conf/361/>.
90
"The Grain Express Load/Unload Program." CSX Corporation, 2012. Web. 6 Feb. 2015. <http://www.csx.com/index.cfm/customers/commodities/agricultural-products/services/grainexpress-loadunload-program/#details>.
The Hispanic Population in the United States. United States Census Bureau, 2014. Web. 27 Apr. 2015. <http://www.census.gov/population/hispanic/data/>.
"The Role of Rail Infrastructure in the Economic Development of Michigan’s Northern
Lower Peninsula." Michigan Department of Transportaion, Sept. 2014. Web. 18 Jan. 2015. <http://www.michigan.gov/documents/mdot/RailNorthernLowerPeninsulaEconDevlp_476969_7.pdf>.
"Types of Dry Beans Commonly Consumed by Nationality." The Bean Institute, 2015.
Web. 19 Apr. 2015. <http://beaninstitute.com/types-of-dry-beans-commonly-consumed-by-nationality/>.
Ye, Su. "Why the Export Market is Important for US Soybeans." Minnesota Department
of Agriculture, 2014. Web. 6 Mar. 2015. <https://www.mda.state.mn.us/food/ business/~/media/Files/food/business/economics/exports-soybeans.ashx>.
Zook, Jim. Personal interview. 2014.