CHICAGO — McDonald’s Corp. has promoted Tiffanie Boyd to executive vice president and global chief people officer. Boyd succeeds Heidi Capozzi, who has decided to leave the company at the end of the month for a new opportunity.
In her new role, Boyd will oversee the company’s human resources operations around the world, including talent management, talent acquisition, total rewards, learning and development, DEI, culture, and organization effectiveness.
“Tiffanie is an exceptional HR leader, who understands that great people are the bedrock of the McDonald’s business,” said Chris Kempczinski, chairman and chief executive officer of McDonald’s. “Since she joined the company a few years ago, Tiffanie has quickly established herself as a collaborative, values-driven leader who has championed several transformational programs like our People Brand Standards and talent development initiatives that have turned our US business into a role model within the system.”
Over the course of her career, Boyd has led teams in the United States, Canada and around the world to elevate talent management and employee development. She joined McDonald’s in 2021, leading the development of the McOpCo Total Reward strategy, changes in McDonald’s talent strategy and improvements to its career planning and development philosophy. Previously, Boyd spent over two decades working with General Mills, Inc. in various capacities, overseeing talent initiatives, organization design, culture change and employee engagement. She began her career as a consultant at Hewitt Associates.
Currently, Boyd serves on the board of Thrive Scholars, which helps students of color from low-income backgrounds attend top colleges and pursue meaningful careers.
Boyd graduated from the University of Michigan with a bachelor’s degree in finance and received a master’s of business administration degree in organization behavior from the University of Michigan Ross School of Business.
“At McDonald’s, we’ve built our people strategy on a simple idea: The employee experience fuels the customer experience,” Boyd said. “There is already great work underway, and we are seeing the impact of a focus on employee experience. I look forward to partnering with our teams across segments, markets and functions to power a culture of care so robust that our people and business thrive like never before.”
Clemens Food Group is unveiling Hatfield’s new sausage portfolio. Designed to meet the rigorous criteria of Gold Standard Animal Care, these thoughtfully raised sausage products aim to revolutionize breakfast offerings for operators. The Gold Standard Animal Care initiative ensures higher welfare standards for farm animals, promoting more humane treatment and addressing growing consumer demand for responsibly sourced food.
Sausage-stuffed waffle. Courtesy of Clemens Food Group
Hatfield’s new sausage portfolio introduces a range of products tailored to current culinary trends, including shakshuka, breakfast poutines and breakfast sandwiches. Among these offerings are a variety of Hatfield All Natural Skinless Sausage Links and Sausage Patties, which are both frozen and fully cooked, including a new savory and aromatic sage flavor. These products ensure consistent quality and ease of preparation, making them ideal for busy foodservice operations across various settings such as family dining, midscale restaurants, fast-casual chains, health care cafes, colleges, universities and lodging facilities.
“We are having much success with the Clemens precooked sausage patty, especially in our larger high-volume units,” said Sam Lazarro, executive chef from the Carilion Group. “The patty has great biscuit/plate coverage with almost no shrinkage when reheated, and the sage flavor profile has been very pleasing to our customers. The quick cook time is a plus for our back-of-the-house staff, which has led to consistent-looking and tasting products for our kitchens, which is the biggest plus for me as an executive chef striving for uniform kitchen results in 10 locations.”
Sage sausage patties and links. Courtesy of Clemens Food Group
“As a chef, I’m always looking for products that not only meet the highest standards of quality but also inspire creativity in the kitchen. Hatfield’s new sage-infused sausages do just that. They’re versatile, flavorful, and crafted with care, making them a perfect addition to any menu. Our commitment to ethical sourcing and sustainability ensures that we’re providing the best possible product to our customers,” said Jen Moyer Murphy, corporate executive chef at Clemens Food Group.
Maple Leaf Foods Inc. announced that Deepak Bhandari has been named as chief financial officer for the company’s pork business, which is set to be spun off as an independent, publicly traded company in 2025.
Bhandari rejoins the Maple Leaf Foods organization where he had a career spanning 13 years in progressively senior roles within the company’s finance organization. Most recently, Bhandari served as the interim chief financial officer of High Liner Foods and is currently its senior vice president of strategy and corporate development. He will step into the role of CFO, Pork Complex at Maple Leaf Foods in September 2024.
“We are delighted to welcome Deepak to our team,” says Dennis Organ, President of Maple Leaf Foods’ Pork Complex and incoming CEO of the new Pork Company. “Having previously been part of the Maple Leaf Foods organization, Deepak has a thorough understanding of our pork operations and the landscape of the business. We look forward to his financial leadership and expertise as we complete the work to execute the spin transaction and embark on the next step in our journey as an independent company.”
In July, Maple Leaf Foods announced a plan to create value by separating into two public companies. By spinning off its pork business, the name of which will be announced in the coming months, Maple Leaf Foods says its making “a world-leading organization which produces sustainable meat the right way and can fully take advantage of its own unique business model to unlock its own significant growth potential.”
AI technology is becoming an underlying framework for many automation systems today, and one of the latest applications for employing AI is in vision inspection systems. In fact, the key component of a vision inspection system is the camera, and suppliers like Cognex, Keyence and SICK AG are now routinely building AI into their cameras.
One system integrator reports using Cognex and Keyence cameras frequently. Actemium USA (formerly known as Outbound Technologies Inc.) and a CSIA (Control System Integrators Association) certified member, offers a broad range of advanced services and solutions, leveraging AI to enhance quality decision making in food and beverage applications. According to TJ March, senior project engineer, AI-based inspection technologies that his firm has deployed include detecting foreign objects, identifying defects and anomalies, ensuring precise label identification and placement and sorting products efficiently.
March has employed cameras with assisted AI, including the following models: Cognex In-Sight 2800 for product sorting; In-Sight D900 for OCR and label imperfections; In-Sight 3800 for label identification, complex OCR for inkjet printing color ID and label positioning; VisionPro Vidi for defect detection from various angles; and Keyence’s IV3 for product sorting.
“Currently, we are working with a multinational food processing company to develop a high-speed inspection system that detects surface and geometry defects on packaging,” says Sean Dugan, mechanical engineer at Huffman Engineering, Inc., a CSIA member.
“Our past projects included OCR systems to inspect package and carton labeling, as well as systems to inspect package integrity in the pharmaceutical industry,” adds Dugan.
Fortress Technology builds X-ray, metal detection, checkweighing and vision systems. Often these systems are used standalone, but can be combined together with a common data reporting system, says Matthew Gidman, product manager. “It can be more complex to have all these individual systems in one area of a production line—mainly due to physical space and cost constraints. However, AI now makes it feasible to collect data and monitor processes across all four systems.”
Today’s generation of vision inspection sensors, such as the SICK Inspector83X, have AI technology built in, allowing non-expert users to “teach” the device using examples. Featuring flexible integration into industrial networks and PLCs, the Inspector83x-series comes preinstalled with SICK Nova, allowing users to conveniently extend the software’s functionality.Image Courtesy of SICK AG
Having all this data from these systems and using AI can provide valuable information on production lines. However, a little work is required before setting up any AI-based inspection systems, says Gidman. “Before an organization can begin augmenting data analysis of systems with AI, it must assess its data quality. Accessible, high-quality data is essential for AI effectiveness, as well as relevance of the data for the specific problems being solved.
“Given the volume of legacy data, merging this together without complications, intensive data management programs, costly setup and maintenance programs can be challenging. To remedy this, collected data and repositories must be set up with AI in mind from the outset. One of the most significant benefits of combination units is that the data center is integrated, rather than trying to tie multiple disparate database formats together.”
Vision systems inspect products and give robotics sight. Gray Solutions, a CSIA member, began with vision systems for grading vegetables and gradually expanded into inspecting for defects and color consistency, says Gregory Powers, vice president of digital transformation. “Given our extensive work with robotics, we have integrated vision systems with robots to perform quality inspections. Additionally, we use vision systems on high-speed lines to capture packaging defects, such as issues with cans and bottles, including proper labeling.”
The main promise of AI systems in machine vision applications is that they are easier to ‘train’ to do the job than it would be to define some combination of acceptance metrics that would be amenable to traditional vision algorithms.
Moving AI-Vision Applications from Industrial Parts to Food
“The main promise of AI systems in machine vision applications is that they are easier to ‘train’ to do the job than it would be to define some combination of acceptance metrics that would be amenable to traditional vision algorithms,” says Huffman’s Dugan. “Measuring a dimension on a machined part and comparing it to a given tolerance is straightforward, but assessing the texture and shape of a chocolate chip cookie, for example, so that it can be judged good/bad/OK is a challenge.”
Dugan notes that recent projects didn’t require AI algorithms because pass/fail criteria were relatively straightforward, but in the cases of surface inspection where the surfaces are complex or have randomized features, AI algorithms might offer an effective solution.
“Integrating vision systems with AI in the food industry is an effective solution for quality inspection,” says Aaron Burke, an engineer at Concept Systems, a CSIA member. The setup is similar to being in a machining environment. Providing a variety of images with clear pass or fail conditions and correct programming will result in a successful inspection system. Some examples currently utilizing AI are position checks, measurement data of key features, label applications, contamination checks and defect detection. Keyence and Cognex products have been utilized by Concept Systems in food quality applications, says Burke.
Vision systems strengthen quality control by capturing an image and processing it against set quality control parameters.Image courtesy of Fortress Technology
Training AI is Getting Easier
Due to the high variability in food products, deep learning models excel when trained with a comprehensive, representative sample set (i.e., a collection of data samples that accurately reflects the full range of variations and conditions present in the target application), says Actemium’s Marsh. “Given the complexity and variability of these applications, we recommend leveraging the expertise of a system integrator. A system integrator can effectively manage all the requirements and nuances involved in deploying these advanced AI systems, especially when upgrading or adding to existing, potentially outdated inspection lines.”
“With regard to training an AI vision system, either system integrators or end users can train AI vision systems, however, training these systems for consistence and accuracy can be tricky, says Caleb Feagans, a chemical engineer at Huffman Engineering. Understanding the necessary variations and defects required to train and program a robust vision inspection system can be challenging, which is where an experienced system integrator can be beneficial. (See the box, “Training Considerations for AI/Vision Systems.”)
Training Considerations for AI/Vision Systems
Training vision systems with built-in AI for food quality applications can present some challenges compared to a more structured environment, such as manufacturing machined parts. Here are some considerations for training AI vision systems:
Variability: Food surfaces exhibit a wide range of variations of color, textures and sizes. Machined parts often have consistent shapes and surfaces while food items do not and can vary significantly, even in the same batch.
Diverse training data: Training AI models for food quality inspections (or any AI training for that matter) requires a diverse dataset that captures variability in appearance and defects. This training set must cover different lighting conditions, angles and variations in order to ensure quality results.
Integration challenges: Upgrading an existing inspection system to include AI capabilities often requires integration with the current production line and existing infrastructure. This might involve adapting software, examining compatibility with existing hardware and possibly retraining the workforce.
— Caleb Feagans, Chemical Engineer, Huffman Engineering, Inc.
Gray Solutions has long utilized vision systems for various food quality applications, says Powers. Initially, setup requires input from a solution integrator, but with new AI tools, these systems are now more user friendly and can be trained by operators. Gray Solutions began by using vision systems to grade products and expanded their use to meat processing, ensuring meat products are free from contaminants and meet quality standards for texture and appearance. As camera technology improves and becomes more affordable, the use cases continue to grow, including ensuring date codes, bar codes and general packaging applications.
It’s important to remember that machine vision is typically part of a larger inspection system that can include x-ray systems, says Fortress Technology’s Gidman. The goal is to strengthen quality control by capturing an image and processing it against a set quality control parameters. In inspection technology, vision is commonly deployed for food pack label verification, and sometimes for food surface defects. Working in tandem and using a common AI system, these technologies can be used to strengthen food safety:
Vision can improve X-ray performance by providing contextual information about each pack, including the label placement and presence of legally required information, such as dates and allergens.
Vision cameras can be used for pack separation to ensure products are correctly spaced out as they are fed into inspection apertures or onto sensitive weighing conveyors.
Metal detectors can be combined with X-ray to ensure high performance across the full contaminant spectrum. This combination could also be applied for dual-density products, e.g., inspecting baked bread with a harder surface crust and softer, dough middle.
AI Cameras a Drop-In Replacement for Older Vision Inspection Cameras?
Like any other equipment purchase, the decision to upgrade or purchase new equipment depends on the age and design of the current system, says Gidman. “This decision should be based on a thorough risk and cost analysis conducted by the purchaser. This ensures that the chosen option aligns with the company’s operational needs and budget constraints.”
“AI camera systems are not necessarily drop-in replacements or simple add-ons,” says Actemium’s Marsh. Due to the hardware requirements of deep learning or AI, it is rare for an AI-based camera to be a direct drop-in replacement for an older camera. They use their own specialized software for configuring and programming the cameras, which often involves additional programming to ensure proper communication with PLC or SCADA/MES systems. This integration process requires a detailed understanding of both the AI systems and the existing industrial control architecture to ensure seamless operation and data flow.
“It is important to consider that the new cameras may need adjustments for distance and lighting, likely requiring a systems integrator,” says Gray’s Powers. “Additionally, we need to assess how the new AI can utilize the training data from the old system, as retraining the new system may be necessary. Some new AI software solutions are compatible with various cameras, allowing for seamless camera switching.”
Training the camera is only one part of implementing a new AI-based machine vision system, says Huffman’s Dugan. For example, many lines run different variations of the same product or different products altogether. In these cases, it is necessary to load the cameras with different programs or configuration/job files for the different products. Today, this is usually done over Ethernet with either a PLC or PC-based workstation sending control sequences to the cameras to load different programs based on user input. Most older machine vision systems use proprietary software to perform this task, so just dropping in a new camera is not enough.
“Also, it is necessary to understand how the old cameras sent results back to the system, for example to a reject station downstream. Coordinating communication back and forth between the camera system and the rest of the line requires understanding how it all works together,” adds Dugan.
Getting the Lighting Right
When transitioning from a manual inspection process to an automated system, the existing lighting setup is often inadequate for the precision needed in machine vision systems with assisted AI, says Actemium’s Marsh. “Therefore, it is crucial for the system integrator to assess and specify the appropriate lighting environment and camera hardware for optimal performance of the new system.”
“Lighting provides contrast and enhancement of part features for cameras to easily find patterns, features, edges or defects,” says Concept’s Burke. For best results, the end user should incorporate controlled external lighting. Some examples include a light bar, spotlight or a lighting attachment fixed to a camera. Lighting also provides consistent and repeatable conditions for the engineer to effectively program a vision solution. But, reliance on ambient lighting can result in unpredicted results if the ambient lighting changes from when the camera was programmed.
While camera vendors will always be a good source of lighting information, it’s still up to the original inspection system integrator to scope out the full requirements and oversee the setup, says Gidman. “Misuse or misunderstanding of equipment can frequently cause operational and performance issues. If this causes machine performance tests to fail, this may even compromise food safety audits.”
Combined, x-ray, metal detection, checkweighing and vision all play a critical role in guaranteeing the quality and safety of food products.Image courtesy of Fortress Technology
What Software is Required to Drop-In new AI-based Cameras?
“The level of software involvement required to integrate new AI-based cameras depends significantly on the complexity of the inspection tasks,” says Savannah Toombs, staff development manager at Actemium. While simpler applications may require less sophisticated AI systems and minimal programming, there is always some degree of configuration needed. These systems are not purely plug-and-play.
Even for basic applications, configuring the AI-based cameras involves setting up the software to recognize specific inspection criteria, adjusting parameters and ensuring accurate data communication with existing systems, adds Toombs. More complex applications necessitate detailed programming, including developing custom algorithms, configuring advanced settings and integrating with PLCs or SCADA/MES systems. Additionally, PLC programming tags will need to be updated based on the specific camera model and manufacturer to ensure seamless operation.
“AI-based cameras will always involve some type of programming,” says Concept’s Burke. The hardware that is selected and the intended purpose of the vision system will dictate the complexity of the AI setup. Simple AI cameras can be set up with minimal vision experience. General engineering experience is always preferred for simple vision systems. Other AI cameras involve significant time and experience to effectively integrate the AI solution.
Issues in Upgrading Older Equipment
Data disparity is one of the biggest issues that processors face in upgrading to AI-powered inspection equipment, says Fortress Technology’s Gidman. The ability to tether multiple front-end machines to back-end reporting software in real time is a key step toward AI-driven systems. Fortunately for food processors, machine builders like Fortress have done their homework. “By creating their own software, Fortress has attempted to support processors and help them avoid unexpected issues,” says Gidman.
AI-based systems have the potential to solve challenging problems in machine vision applications, says Dugan. However, it is important not to get swept away by all of the excitement surrounding “AI.” Dugan suggests a couple of issues to overcome are loss of insight and vendor lock-in.
“An AI-based system might at first generate the results the engineers or managers are looking for, but dependence on them necessarily means that an understanding of how or why the camera is rejecting/accepting a part is lost,” says Dugan. “Applying conventional inspection systems requires knowing exactly what you are looking for. If we depend on AI to judge good cookies from bad cookies, will we actually know why a good cookie looks like a good cookie? If this insight is lost, how will this affect the rest of production? Will we miss opportunities for improving production because we no longer have a concrete, measurable definition of what a good cookie looks like?”
One of the most significant benefits of combination units is that the data center is integrated, rather than trying to tie multiple disparate database formats together.Image courtesy of Fortress Technology
The second issue, Dugan notes, “At some point the new AI-based machine vision system will become the old, obsolete, ‘AI’-based machine system. When the time comes to upgrade, will we be able to convert or translate the programs or configuration files to a new system from a different vendor? The ideas behind the conventional machine vision tools such as edge detection, OCR, histograms, etc. are common across vendors. It is possible to manually convert a program from one vendor’s cameras into a program for another vendor’s cameras because the discrete tools are understood. With an AI-based program, the question becomes: How is this possible?”
“If you are considering implementing a new AI solution for capturing images, it may be worthwhile to assess the cost of replacing the cameras,” says Powers. “You might be surprised at how much these new cameras have improved, similar to the advancements seen in smartphone cameras with each model upgrade.” But Powers notes that while some AI tools claim to be compatible with any camera system, this is not always the case.
For a discussion of more issue when upgrading to AI-based vision systems, see the sidebar, “AI-Vision System Upgrade Gotchas.”
One final thought, AI tools do require extra hardware. A good example: An AI based graphics sharpening tool I purchased two years ago requires 5 GB of disk space for its modeling engine where a plain vanilla sharpening plugin tool from years ago needs under 1 MB. There are similar needs for RAM and processing power: A high-end graphics card and/or CPU is needed to run the model versus a simple processor for the plugin. Is the AI sharpening tool worth it? A resounding “Yes!” Why? It sharpens the image while simultaneously decreasing noise and artifacts. Old tools just worked on sharpening alone.
AI-Vision System Upgrade Gotchas
When replacing older vision/camera equipment with new AI-based systems, several “gotchas” can arise, along with important considerations for a successful upgrade:
Speed and performance issues: AI programs often require more computational power and memory compared to traditional machine vision algorithms. This can result in slower performance and larger program sizes, which might necessitate upgrading to high-end, PC-based deep learning systems to maintain the desired speed and efficiency.
Integration complexity: Integrating AI-based systems with existing infrastructure can be challenging. These systems often require different software and communication protocols, which means reprogramming and reconfiguring existing PLCs or SCADA/MES systems.
Data handling and storage: AI systems generate and process large volumes of data, which can strain existing data handling and storage solutions. Ensuring adequate infrastructure for data management is crucial.
Training and fine-tuning: Unlike traditional systems, AI-based systems require extensive training with representative sample sets to achieve accuracy. This process can be tedious and time-consuming, involving continuous refinement and adaptation to new data and conditions.
Environmental and operational adjustments: AI systems may necessitate adjustments in the operational environment when tasked with inspecting a broader range of complexities. This could include optimizing lighting and configuring specific lenses to ensure effective performance. As a result, revisiting and adjusting the physical setup is often required to accommodate these needs.
Technical expertise: Deploying AI systems necessitates a higher level of technical expertise. Staff may need additional training to handle the new technology, or it may be necessary to bring in external specialists or system integrators for the deployment and maintenance.
Switching to AI-based vision systems offers substantial benefits, including the ability to perform tasks that traditional machine vision systems cannot handle reliably. However, the transition involves navigating these complexities and investing in the necessary resources to fully realize the advantages of AI technology.
— TJ Marsh, Senior Project Engineer II, Actemium USA
A crop of Minotaur barley sown on May 1 at Collingullie in southern NSW. Photo: Grassroots Agronomy
PRICES for feedgrain have fallen by up to $25 per tonne in the past week as global markets sink under the weight of the Northern Hemisphere new crop.
Coupled with ongoing concerns about a lack of general rain in much of South Australia, Victoria, and the far south of New South Wales, the low prices have doused grower interest in selling on-farm, warehoused, and new-crop grain.
In the north, the season is one of boundless promise, and trade sources say a few consumers are starting to look for Sep-Oct deliveries to ensure they are covered ahead of new crop hitting the market in volume.
Prompt
Aug 15
New crop
Aug 15
Barley Downs
$335
$340
$320
$340
ASW Downs
$338
$355
$320
$340
Sorghum Downs
$338
$335
$330
$330
Barley Melbourne
$320
$335
$310
$332
ASW Melbourne
$340
$352
$330
$355
Table 1: Indicative prices in Australian dollars per tonne.
Buying interest pops up in north
Prompt barley in the northern market showed the smallest price drop of any quoted winter grain this week, with tight stocks and some spot demand from the beef and dairy sectors supporting the market.
“Barley stocks are really tight, and some of the smaller guys are going to hand to mouth; dairy’s buying too,” one trader said.
Stockfeed millers and larger feedlots are also “kicking tyres” for September deliveries, as a soft close for the Central Queensland and Maranoa growing seasons will arrest the amount of new-crop grain hitting the market early.
“There are some spot buyers out there.”
Qld had only sprinkles of rain in the past week, which is no cause for concern, and northern NSW had little, with Quirindi on 11mm the highest total registered.
Central and southern NSW had some registrations of 10-20mm at locations including: Temora 18mm; Trangie 12mm; Dubbo and West Wyalong 15mm, and Young 19mm.
Softer market invigorates southern demand
Clear Grain Exchange general manager Trent Smoker said demand is being seen as the market softens, with ASW1 wheat trading at $310/t Melbourne port equivalent this week, down from $327/t last week, and BAR1 at $300/t, down $15/t.
“Published bid prices and trade values have generally continued to soften this week, although buyer interest in trying to buy grain remains robust,” Mr Smoker said.
“Generally, trading volumes are modest as sellers hold firm on their price ideas, while buyers are actively trying to buy grain.
Mr Smoker said 40 buyers have actively placed bids on both warehouse and ex-farm grain this week on CGX and igrain, in some cases, have stepped up to meet sellers’ price ideas.
“As an example, BAR1 barley traded $320/t Adelaide port equivalent yesterday, $20 above the best published bid.”
Watsons Bulk Logistics managing director Joel Watson said the northern Mallee was still relatively dry, with small amounts of rain here and there propping up yield potential for now.
Showers are forecast for Vic in coming days, but Mr Watson the crop remains exposed because of its late establishment.
“In the Wimmera, crops are about four weeks behind down there, and they’ll be relying on a kind finish,” Mr Watson said.
As grain markets fall, mixed farmers are running the ruler over their options for what could be low-yielding cereals selling into low-priced markets versus the relatively stronger lamb market.
Mr Watson said the lack of biomass in many crops could be what sees them push through to harvest.
“For a lot of areas, the crop doesn’t have the density to cross it over into hay.”
Concerns about the season, and falling nearby and new-crop prices, have made grower offers hard to find.
“Growers have gone to ground.”
“They don’t like the prices, and new crop has a rather large question mark hanging over it.”
Mr Watson said average rainfall for the closing months of the growing season could see Vic growers get an average crop, but dry conditions will clip yield potential.
In SA, Pinion Advisory commodity risk manager Chris Heinjus said much of the state’s crop was in a situation as precarious as Vic’s.
“We’re on a knife edge,” Mr Heinjus said.
“We’ve had a little bit of rain…and that’s bought us another week, but we’re running three or four weeks behind where we should be.
“Our production risk is still quite real, and there are some areas that probably won’t make it.”
While some crops in SA are traveling reasonably well, Mr Heinjus said overall the crop will be nothing to write home about.
“There’ll be a crop, but I doubt very much it’ll be even an average one.”
Some growers in south-eastern Australia have received handy rain in the past week, but those that missed the falls are crossing their fingers for what has been forecast.
In Vic, higher registrations in the week to 9am today include: Dimboola 17mm; Goroke 43mm; Murrayville 8mm; Nhill 14mm; Rupanyup and St Arnaud 27mm, and Woomelang 9mm.
In SA, some gauges got nothing in the past week, but others got handy falls, including: Clare 31mm; Cummins 15mm; Coulta 20mm; Keith 25mm; Maitland 27mm; Roseworthy Ag 16mm, and Snowtown North 22mm.
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Robert Young, chief executive officer of Miami Beef, brings passion and excitement to his position, giving him the drive to make moves for the company to better its position in the meat marketplace.
In February, Young and the meat company acquired Syracuse, NY-based Hofmann Sausage Co., a longtime staple of the hot dog scene in the Northeast.
“I think it’s the best hot dog out there,” Young said of Hofmann “I mean, they have a formula they haven’t changed for 100 years. We met with their team. You know they had all the right things in place. They have a great product. They have a great brand.”
Another appealing attribute that Miami Beef identified was the culture and leadership team at Hofmann. Young said maintaining that culture will be a key to a successful ownership.
“They’ve got guys that have been there for 20 years, and they love the product, and the brand is strong up there,” Young said. “It goes all up and down the East Coast where we ship.”
Having a brand with an established heritage in New York was a major factor in Miami Beef acquiring the processor.
“We wouldn’t change the formula of what they do, because that’s what their customers love and they have for generations,” Young said. “What we want to do is add products to what they’re doing.”
Young and the Miami Beef team want to find the right people, location and products. The South Florida meat company was already producing burger patties and other grill products. It now wants to add the hot dog lines from Hofmann to its increasingly diverse offerings.
Other items Miami Beef has produced in the past include chicken and turkey patties, ground meats, steaks and pork chops. The addition of Hofmann has been something Miami Beef is promoting to its existing customers.
An example Young gave was packaging a Hofmann hot dog with Miami Beef’s established foodservice customers and retailers in the Florida area.
“I think that having an opportunity to offer our customers both makes a lot of sense,” Young said. “We can promote both in stores. We can give our customers an opportunity to buy hamburgers and hot dogs on the same truck and they can fill a pallet with both. There’s a lot of synergies there that make sense and we look to acquire more businesses, we’re going to look for synergies like that.”
This year’s acquisition wasn’t the first for Miami Beef. The company made a similar move in 2023 by acquiring Brooklyn Burger and Devault Foods.
Brooklyn Burger primarily delivered to retail customers throughout the United States with frozen burger products. Devault Foods supplied restaurants in the Northeast and Midwest with burgers, meatballs and steaks. Over the last year, Miami Beef leveraged Devault and Brooklyn’s reputation to expand its foodservice industry presence.
“We wanted to bring that great product and pack it here and take away those co-packer margins and just be the manufacturer,” Young said.
Michael Young is the founder of Miami Beef.
| Source: Miami Beef
Family History
Although Robert now works as CEO, his father, Michael, showed him the ropes after decades of hard work and successfully growing the business.
Michael started in the meat industry when he was 11 years old, riding the bus on Saturdays from Atlantic City to Philadelphia to work at Young’s Meats.
At age 21, Michael joined his father in Florida, where he worked with his cousin Jack at Henderson Portion Pack. Jack helped develop the company’s portion-control business and Portion Pack which was ultimately acquired by Borden Foods.
As he learned more about meat processing, Michael went to Arkansas and worked at Vogels Food Distributor. He then traveled across Texarkana, Ark., selling for Vogels before returning to Miami to sell for Henderson.
After gaining some experience, Michael decided to create his own meat business. His first investment was a pink Econoline truck. He bought and sold meat to country clubs, restaurants, Burger Castle, and Homestead Air Force Base. Michael also delivered meat from Florida City to North Fort Lauderdale.
Following the truck purchase was a 10-foot -by-12-foot small, rented room in Central Cold Storage, which was the smallest state-inspected plant in Florida. While perfecting the Jaccard hand-needle machine where he tenderized the ribeyes, Michael eventually needed a bigger room to expand his business.
So, Michael moved to an old poultry plant where he could cut steaks and grind hamburgers while maintaining the pink truck and supplying the family restaurant chain Lum’s, which had dozens of stores in the South Florida area.
As his meat industry reputation grew, Michael garnered more space at US Cold Storage, which included more hooks, tables, saws and equipment that came with the lease. Michael continued to cut steaks for customers like the Kapok Tree Inn and sold them to food distributors across the South.
Finally, in the 1970s, Michael built the current plant Miami Beef runs on today and has slowly made some improvements when needed.
Even at 79 years old, Michael comes in every day to work with his sons, Robert, Harrison and Daniel, who have continued to operate the family business.
Robert Young (left) and Harrison Young both play integral roles in helping Miami Beef achieve his new business goals.
| Source: Miami Beef
Merging standards
Both Miami Beef and Hofmann ship their products all over the East Coast.
By adding a well-known hot dog player to the Miami Beef portfolio, Young said it can offer customers a complete meat program because they provide more offerings.
As part of the acquisition, Young said Miami Beef plans to invest in additional equipment at the 16,000-square-foot Hofmann plant in Syracuse to ensure the legacy remains with Hofmann as they move forward in this new chapter while adding a second line.
“We’re investing in equipment,” Young said. “We’re investing in the people. We’re going to grow that business and we think it’s going to thrive. We’re going to try to introduce it nationwide over the next couple of years.”
In the summertime before acquiring Hofmann, Miami Beef’s production was more than 500,000 lbs per week at its 55,000-square-foot facility in South Florida.
Now, as it incorporates Hofmann under its umbrella, Young and Miami Beef believe it can grab existing Hofmann customers and introduce them to Miami Beef.
“We’re going to cross sell, add new items in the Hoffman SKUs like I said, no formula changes but increasing productivity by adding another line to increase capacity,” Young said.
Publix and Harris Teeter are two retail chains where Miami Beef and Hofmann are available on the East Coast, and that distribution is expected to continue for years to come.
Some Miami Beef products were on display at the Annual Meat Conference 2024.
| Source: Ryan McCarthy/Sosland Publishing Company
German machinery manufacturer SCHULE Mühlenbau announces the completion of a state-of-the-art plant in Latvia for processing peas and fava beans as commissioned by Golden Fields Alternative Protein, an international producer and supplier of agricultural products.
The new facility is to create 50 jobs in the Baltic region and have production capabilities of around 50,000 tonnes of pea and beans annually, as international demand for proteins from pulses continues to surge, as per our report from just yesterday. According to reports, Golden Fields is considering opening additional processing plants in the area.
Golden Fields Alternative Protein says that it co-operates closely with farmers for the production of plant-based proteins as raw materials for end products like meatless burgers, sausages and nuggets, stating, “We are at the forefront of this trend, creating innovative and sustainable protein and starch products from peas and fava beans.”
Thorsten Lucht, Area Sales Manager at SCHULE Mühlenbau, comments in a press release: “Extracting proteins from legumes requires many different processing steps and a great depth of expertise. In addition to cleaning and sorting, this includes dehulling, separating, fine grinding and separating into protein-rich and starch-rich fractions.”
Mahmoud Ahmed, CEO at Golden Fields Alternative Protein, adds, “Due to global population growth and the increasing demand for sustainably produced food, the need for high-quality proteins has been rising for years.
“With our new plant in Liepaja, which SCHULE Mühlenbau planned, built and commissioned just in time, we are ideally prepared for this future market in Northern Europe.”
TORONTO — Ontario will be permitting alcohol sales in convenience stores effective Sept. 5, 2024. With business owners looking to receive product by that date, Labatt is making it easier to stock shelves.
To help businesses get set up to sell alcohol in Ontario, Labatt has launched a resource hub on its website to assist business owners with getting the information they need, including step-by-step licensing guidance, industry tips for store setup, and a sign-up link to have a business development representative visit stores and business owners who don’t know where to start.
It’s important for business owners to start the licensing process as soon as possible to avoid delays. Alcohol sales in Canada was more than $33 billion in 2023, alcohol is the number-2 reason for trips to convenience stores in Quebec, eight per cent of those alcohol trips are for beer, and beer grows convenience store baskets by 47 per cent.
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The Canadian trucking industry cannot meet the domestic needs to keep critical supplies and daily goods flowing if a work stoppage happens at the country’s major railroads, according to the leader of the British Columbia Trucking Association.
“There is no possibility trucking can fill the gap of any labour disruption on railways,” Dave Earl, president and CEO of the BCTA, said in an email to Trucking Dive.
A labor deal impasse between the Teamsters Canada Rail Conference and carriers Canadian National Railway and Canadian Pacific Kansas City has led to a potential work stoppage that could start by Thursday. Both railroads also announced freight embargoes last week ahead of a possible shut down of their respective networks.
Earl recognizes the dire situation and impact to supply chains should Canada’s railroads stop running. More than 900,000 metric tons of goods move daily on Canada’s railways, according to the Railway Association of Canada.
Despite the soft freight market plaguing the U.S. trucking industry, Canada’s trucking industry is “already running near capacity,” Earl said, adding that “road transportation cannot come close to replacing the movement of goods that will be displaced from railways in the event of a dispute.”
BCTA’s motor carrier members operate between 13,000 and 14,000 trucks and employ over 26,000 people. Trucking depends on the railroads to haul bulk items including raw materials such as coal, grain and other minerals, Earl said.
“Our members move goods in smaller quantities to places railways don’t go,” Earl said.
U.S. trucking companies that operate in Canada are aware of a possible logistics crisis. A spokesperson for ArcBest said the carrier doesn’t expect Canadian rail disruption to impact its operation since most of its freight in Canada is transported over the road but stands prepared to handle any issues if problems arise.
While trucks will keep some domestic freight moving around Canada should the railroads shut down, it’s the transport of larger items that concern Earl.
He pointed to shipments of new vehicles arriving through the Annacis Auto Terminal at the Port of Vancouver, Canada’s largest, which handles 480,000 vehicles annually. Earl said if cars cannot move from the Annacis terminal on rail, eastern-based vehicle transport carriers will have nothing to deliver to dealerships.
The same scenario arises for shipping containers arriving in Vancouver, Earl said. If cargo typically transported to eastern and southern destinations in Canada isn’t moved on rail, distribution centers won’t be restocked, which means trucking companies have nothing to transport to stores for consumers to purchase.
“Far from an uptick in business, this will create significant disruption,” Earl said. Trucking may manage some of the displaced cargo, but he doesn’t see rail disruption creating a “boon for our sector.”
“Should a disruption occur, this will impact every element of the supply chain in Canada,” Earl said.
