Join Dan Bowen from Q Laboratories and David Hatch from Neogen as they discuss how workflow automation and lab data integration deliver ground-breaking benefits. Learn how a new paradigm is being established — where results data presents itself to your team, as opposed to your team having to dig through multiple files and records to find it.
On three recent occasions, I have talked with food safety leadership at mid- to large-sized processors about their food safety testing and sanitation programs. While these organizations each face some unique challenges, there was a common theme that was clear among them all: The creation of meaningful and timely reporting that is communicated effectively is typically just too cumbersome and manual today.
Other industries have digitally transformed the management of testing, diagnostic, and sanitation workflows. Take, for example, the healthcare industry, where the electronic health record (EHR) has become the standard means of system-wide communication of patient health and risk information. All testing and diagnostic data related to a patient is added to their EHR, making it far simpler for medical professionals to access and use in their assessment and determination of appropriate treatment programs and medications. In addition, the EHR makes it easier for health providers and payor organizations to access aggregate data to assess outcomes, risks, and other measures relevant to the organizations and the industry.
The banking industry, which years ago established online electronic banking as the standard means of transacting business, provides another digital transformation example. Each personal or business account holder can be seen as a source of transactions (analogous to a series of diagnostic results), where there is an expected outcome. The old way of banking, via manual, paper-based systems, had many limitations, including the risk of human error.
Resistance to change abounded in each of these examples, but the benefits of accelerating access to information, eliminating human error, and streamlining the ability to collect, assemble, and deliver impactful analytics, far outweighed the hesitancy to move forward with new digitally supported methods.
In the food and beverage manufacturing and processing industry, resistance has remained very strong within Food Safety & Quality functions. This resistance has largely been the result of food safety regulation being viewed as a “compliance” necessity, and it therefore does not receive the same attention as a business innovation that yields a business growth outcome.
New thinking on this conundrum is starting to generate a different perspective on the matter, however. Early adopters of digital food safety management platforms have found several business benefits beyond streamlined compliance outcomes. At the 2023 International Association for Food Protection (IAFP), a panel of food safety leaders from three different organizations discussed their experiences in adopting digital software for managing their testing programs. Excerpts from this event can be found in this video.
The main discussion centered on how each organization established a business justification for adoption of digital technology to manage their testing programs. Here are three effective justifications that I have heard from leadership, which also featured in the discussion at IAFP:
If you are challenged with building a business justification for adoption of digital technology in your organization, perhaps the thinking in this article will provide a starting point.
This past year, I was invited to participate in a risk assessment workshop led by a third-party consultant at a food safety event. During my 30+ year career, I have been through many different types of risk assessments across several industry segments. I have been a participant seeking to define and address risk at my own organization, as well as a consultant helping my clients perform their own risk assessments. Each time I experienced a risk assessment exercise, I learned something new, and this time was no different. The key learning for me in this case is encapsulated in the title of this blog: Food Safety Risk Assessments are “Data Hungry.”
As we went through the workshop exercise, we explored the elements of risk. Specifically, risk is defined as a combination of three factors: Is something POSSIBLE, how PROBABLE is it to occur, and what is the potential SEVERITY if it were to occur?
The room then proceeded to use these elements and measurement techniques to assess risk across 10 different scenarios. These included descriptions of foodborne illness, food safety testing outcomes, discovery of allergens, labelling mishaps, chemical contamination, food fraud, supply chain disruptions, and other risks.
The risk assessment included a worksheet laid out as a table, where each scenario could be prioritized and scored according to the risk measurement elements (Figure 1).
![Example Risk Scoring Table]](https://foodsafetytech.com/wp-content/uploads/2024/04/Figure1.jpg)
The room was divided into three teams, and each was asked to prioritize the various scenarios in order of highest to lowest risk. Each group completed this task, and here is where things got interesting — each team had different results!
As shown in the example table, a lower priority may yield a risk score above that of something that was originally considered a higher priority. Each team’s tables looked significantly different from the others. To be clear, these were not strangers performing the exercise with no knowledge of each other’s priorities. In fact, the three teams comprised the global food safety leadership of one company — yet each team seemed to have very different ideas on risk prioritization. This unexpected result caused some lively discussion; meanwhile, the consultant leading the exercise was the only one in the room who was not surprised at all by the results. Here’s why:
There was one more factor to consider — one that was on the minds of each team, but not openly expressed as a factor for prioritizing risk: The TYPE of risk.
The consultant then asked the room to describe what type of risk they were thinking about from the following four categories:
The room concluded that the type of risk had a significant impact on how the risk was originally prioritized. Each team had set out their prioritization criteria based on a preconceived risk category, and it turned out that each team’s selected category was different. Depending on which of the four risk types or objectives was dominant, a different prioritization and risk scoring resulted.
This is where the “data hungry” concept factors in. The final analysis revealed that a risk scoring exercise conducted in this manner is capable of yielding only a “perceived risk” score. While perception is a good start, an actionable risk assessment should be based on actual outcomes and experiences. The availability of real-world data, collected over time, has a dramatic impact on validating perceptions.
For example, the availability of pathogen testing diagnostic data, along with the probability, frequency, and likeliness of occurrences, would allow a risk assessment score to be based on a historical trend, rather than a perceived level of frequency and probability. A risk assessment exercise would be informed by the data, and a score of 1–5 could be applied with far more confidence.
Data, in the words of one of the participants, “removes the guesswork and assumptions” within a risk assessment. I learned that data is the necessary element to transform risk perception into risk knowledge. While it is useful to perform a risk assessment based on perceived scoring and prioritization, it is essential that a risk assessment be validated with real data.
The Interagency Food Safety Analytics Collaboration (IFSAC)—a collaboration between the Centers for Disease Control and Prevention (CDC), the FDA and the USDA Food Safety and Inspection Service (FSIS)—has published its upcoming priorities for calendar years 2024 – 2028.
IFSAC was established in 2011 to improve coordination of federal food safety analytics efforts and address cross-cutting priorities for food safety data collection, analysis and use. The collaborative’s focus is foodborne illness source attribution, with an emphasis on four priority pathogens: Campylobacter, E. coli O157, Listeria monocytogenes, and Salmonella.
In addition to its continued work generating and publishing annual estimates of foods contributing to foodborne illness, the following four priorities will guide IFSAC’s work for the next five years:
Priority 1. Improve foodborne illness source attribution estimates for Campylobacter by exploring additional data sources and alternative methods to better estimate the sources of foodborne illnesses caused by Campylobacter and harmonize estimates across different approaches and data sources.
Priority 2. Develop foodborne illness source attribution estimates for non-O157 Shiga toxin-producing Escherichia coli (STEC). IFSAC will be adding STEC to its list of priority pathogens and provide source attribution estimates in its annual Foodborne Illness Source Attribution reports.
Priority 3. Refine foodborne illness source attribution estimates using data from non-foodborne sources of pathogens. Although the priority pathogens included in IFSAC’s analyses are spread predominantly through foodborne transmission, these pathogens also spread through contact with water, human, animal, and environmental sources. To generate more accurate estimates for foodborne illness source attribution, IFSAC analysts will explore available data for non-foodborne sources of the priority pathogens and consider methods to incorporate this information in communications.
Priority 4. Finalize existing analyses and disseminate findings to multiple audiences. IFSAC plans to review the status of all projects, determine which are close to completion, and identify which should be finalized and by when. During the final stages of each project, IFSAC will implement appropriate communication vehicles for each project, such as peer-reviewed publications, public reports, webinars, conference presentations or updates to the IFSAC website to disseminate findings to the appropriate audiences.
The food industry is facing new challenges in food safety due to the introduction of novel foods and extreme weather events. In recent years, flaws within the nation’s regulatory system have also come to light. On October 16-18, food safety and quality professionals will gather at the 2023 Food Safety Consortium in Parsippany, New Jersey, to share lessons learned, join discussions with regulatory bodies and gain knowledge on how to mitigate current and coming food safety challenges. Join your peers as we examine topics including:
Modernizing the U.S. Food Safety System
Following the infant formula crisis, the food industry, the public and the U.S. legislature called for changes to how we regulate food in the U.S. In this session, we look at key concerns and shortcomings with our current regulatory framework and how the system can be modernized to better address—and reduce—the most likely foodborne illness risks facing today’s consumers.
Panelists: Stephen Ostroff, M.D. former Acting FDA Commissioner, Bill Marler, Food Safety Attorney; Barbara Kowalcyk, Executive Director, Center for Foodborne Illness and Panelist of the Reagan-Udall Foundation for the FDA. Moderated by Inga Hansen, Managing Editor, Food Safety Tech.
View the full agenda.
The Rise of Previously Unforeseen Hazards
With the combined effects of the recent pandemic, globalization, climate change, digitalization, and decreased regulatory inspection oversight, it is inevitable that previously unforeseen food safety hazards have emerged from within the food sectors previously thought low risk. Arguably, the rise of previously unforeseen food hazards may be attributed to the following:
In this session, Tim Lombardo, Senior Director for Food Consulting Services, EAS Consulting examines the challenges of identifying emerging hazards associated with Food Fraud, Food Fusion, Clean Labeling, and Protein Alternatives as well as mitigation strategies to minimize these risks.
Make Data Useful Again: Building an Analytics Strategy to Drive FSQA Performance
Are you tired of sifting through vast amounts of data that don’t provide the valuable insights you need for your business? We understand that not all data is created equal, and it can be overwhelming to determine which information truly matters for making critical decisions. In today’s digital, world where every solution promises data insights, finding the right analytics and meaningful insights is crucial for success. Join our panel discussion where three seasoned F&B industry experts will share their hard-earned lessons and best practices for navigating the data deluge. Learn how they have successfully identified and utilized the data that matters, enabling them to drive important decisions and uncover critical gaps in visibility to revolutionize FSQA and supply chain programs.
Panelists: Gary Smith, Vice President, Quality Systems, Food Brands, 1-800-Flowers and Paul Bradley, Senior Director Product Marketing, TraceGains
Registration options are available for in-person and hybrid team attendance.
ISO compliance is essential to maintaining high levels of food safety and quality. Trying to manage the ISO compliance process manually—with paper files or Excel spreadsheets—is an expensive, time-consuming, error-prone process. Manual systems make it difficult to spot noncompliance issues, track certification paperwork, and get real-time visibility across an enterprise. Technology can be a game-changer when it comes to achieving and maintaining ISO compliance.
SaaS-based quality and audit software can automate ISO compliance-related tasks, making it easier as well as more efficient and accurate to track quality metrics, document corrective actions, and generate reports. Additionally, this software can save time and costs, while reducing the risk of errors. It also provides real-time visibility into the compliance process, allowing organizations to quickly identify and address any issues that may arise, ensuring that they stay compliant.
While technology has already elevated ISO compliance dramatically, there are some exciting trends we are watching that have the potential to significantly improve the process:
Recently, we have seen large companies adopting technology to improve their quality and safety initiatives. Some notable examples include consumer goods giant Procter & Gamble, who implemented a comprehensive quality management system that incorporates ISO standards. P&G has worked hard to achieve ISO certification across many of its global operations, vowing to operate responsibly, build and maintain public trust in their products, and meet (or exceed) all legislative and regulatory safety requirements.
Similarly, Swiss fragrance and flavor manufacturer Givaudan has implemented a digital quality management system to automate quality data collection and analysis, helping the organization achieve compliance with ISO standards and improve product quality. They have developed a structured system to identify, assess, respond to, and mitigate risks to protect the company’s products and assets. They also vow to improve compliance with proper corporate governance guidelines and to follow all applicable laws and regulations. Hopefully, we’ll see more organizations following their lead.
There are many benefits to adopting new technologies to achieve ISO compliance. These include:
Technology can make a dramatic difference in achieving ISO compliance, transforming the process from the manual methods that organizations have used for years. By automating the necessary tasks, you’ll save time, identify (and fix) areas of noncompliance, reduce errors and headaches, boost efficiency, increase visibility, and centralize data. Now is the time to ditch your paper certifications and overflowing file cabinets and embrace a smarter, easier, more efficient way of working.
The world’s population now exceeds 8 billion people. For the food industry, this means more than just additional mouths to feed; it also means changing consumer tastes, higher demand for more product variety, and the need to balance product safety and sustainability, along with profitability.
In a rapidly changing environment, digital solutions help companies make informed business decisions quickly. The missing component is rarely a lack of data itself. To stay competitive, and appropriately balance safety, sustainability, and profitability amid a growing population, companies need to reap the full benefit of their existing operational and process data to gain valuable insights and efficiencies. Data analytics and digital tools empower companies to collect, centralize, and extract actionable data-driven insights generated throughout the entire manufacturing lifecycle. While digital transformation can sometimes be overwhelming, there are specific steps you can take to realize important benefits, quickly.
Food and beverage manufacturers generate large amounts of data during the process of turning raw materials into finished products, including information about equipment run time, product quality, and energy consumption.
Despite the need to share and analyze this data collaboratively, industrial data is often stored in separate systems, managed by different teams, and at times governed by inconsistent processes. These data silos impede collaboration, diminish quality control and safety measures, and ultimately hurt the bottom line. In a highly regulated industry, simple missteps can negatively impact brand reputation in addition to causing compliance violations and regulatory penalties.
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Consider a food manufacturer that produces packaged goods. An unplanned stoppage in production can lead to safety incidents, product contamination and losses, and energy wastage. Connecting manufacturing data across functions and generating contextual insights will result in proactive planning and maintenance scheduling.
A manufacturer with disconnected inventory management and production planning systems cannot accurately forecast product demand or adjust its production schedules. This results in missed insights and excess inventory or stockouts that lead to increased costs, and/or missed sales opportunities. In both scenarios, a robust data and digitalization strategy could reduce process disruptions or deviations, product waste and recalls, and revenue loss.
Robust data analytics and management tools can provide value across the organization. The following strategies will help ensure digital initiatives deliver maximum value nearly right away throughout the entire manufacturing cycle. These quick successes help the pave the way for long-term digital integration.
Start with small initiatives. It is usually most effective to start small when it comes to digital initiatives. Pick a single site or production line, identify an urgent pain point to solve, and develop a focused approach to applying analytics. This strategy ensures a targeted investment of resources and time, as well as the opportunity to test and refine strategies.
After initial project success, it is often helpful to step back and evaluate what worked, what did not work, and review best practices for deploying the technology at scale.
Set clear goals and metrics. Setting clear, measurable goals and metrics allows you to monitor progress. For example, a company might want to work toward reducing equipment downtime by 20% within the next six months. In addition, it’s often helpful to have a champion in the organization who will push for innovation, track progress, and serve as a motivator across the company.
Build data-driven operations. Comprehensive data-driven solutions have embedded domain expertise to make employees’ jobs easier by freeing them up for high value work. The key is to invest the time, energy, and resources in training employees effectively.
When deploying AI and other data-driven technology, focus on high-impact areas for quick wins. Centralized data management systems can seamlessly connect sensors, IoT devices, legacy systems, or other interfaces in production processes, allowing engineers and managers to focus on problem solving versus data collection and analysis.
For instance, AI-powered process analytics software can analyze process data, monitor variables that are likely to produce an off-spec product, automatically alert the team when a problem needs to be addressed, and prescribe changes to address the issue. Likewise, predictive maintenance solutions can use this data to detect equipment failures early and accurately, thereby reducing downtime events. These solutions also have built-in prescriptive capabilities to guide and empower maintenance personnel to address equipment issues in a timely manner.
Leverage data to identify areas for future innovation. Beyond solving today’s production pain points, data analytics can transform manufacturing for the future by helping companies respond to changing consumer preferences and needs, including the increase in health-conscious consumers and the global rise in food allergies. By analyzing this kind of data, the industry can identify areas where innovation is needed. And, as new products are developed, existing solutions can be leveraged to increase flexibility and efficiency in production lines for data-driven growth.
By unlocking the true value of data, companies can generate insights with their digital transformation strategies that optimize production, reduce waste, and improve profitability while ensuring consumer safety.
Mérieux NutriSciences has acquired Blonk, an international expert in food system sustainability. Blonk helps organizations better understand their environmental impact in the agri-food value chain by offering Life Cycle Assessment (LCA)-based advice and developing tailored software tools based on the latest scientific developments and data.
Blonk was founded in 1999 and today includes a team of 50 food and sustainability experts, software developers, and data and methodology specialists. The company is based in the Netherlands. Working with various players in the food chain, from ingredient producers to food manufacturers and retailers, Blonk has a proven track record of providing customized, science-based advice and intelligent software solutions to define the environmental footprint of products. They are also at the forefront of sustainability research in the agri-food sector. In particular, Blonk works with international and governmental organizations to define sector-specific standards and build databases allowing for assessment of environmental impact of food products combined with nutritional parameters for optimized diets.
“Bringing together Blonk and Mérieux NutriSciences is an exciting step in strengthening and accelerating our contribution to the sustainability of food systems,” said Nicolas Cartier, CEO of Mérieux NutriSciences. “Sustainability is at the heart of Mérieux NutriSciences’ vision, mission, and business roadmap, which is highlighted by our ESG commitments for Better Food. Better Health. Better World. Our ambition is to become the reference partner for science-based sustainability solutions in the food sector to support the transition to more positive food systems. With its specific experience in standards definition and data collection for the agri-food sector, Blonk strategically consolidates our existing capabilities in the field of environmental footprinting and enriches the sustainability solutions we already offer in packaging, new food alternatives, responsible sourcing and soil health, as well as transparent labeling.”
“Better food safety begins and ends with better data,” remarked FDA Deputy Commissioner Frank Yiannas during a speech delivered on World Food Safety Day 2022 that emphasized the immense power of data in our food system. Digitized traceability data is critical not only for efficient recalls but also for root cause analysis of foodborne illness events. Product movement, performance and environmental data sets—when aggregated and analyzed—have the power to generate valuable trend insights and inform continuous improvement initiatives in food safety.
Embracing the opportunities provided by better data, the FDA has incorporated data sharing, data quality and data analysis themes into each of the core elements of the New Era for Smarter Food Safety Blueprint. Companies across the food industry mirror that focus, integrating data-based initiatives in their organizational goals. Following are some the latest and emerging technologies entering the food safety and traceability space to support industry efforts to harness the power of data.
Though data collection efforts often rely heavily upon human labor, the use of Internet-connected devices to collect food safety and traceability data is expanding throughout the food and beverage industry.
Sensors at the harvest level can be used to monitor climate conditions in the field, automatically alerting farmers to weather events that may impact the quality and safety of food crops. Processing facilities use sensors to monitor the temperature of ingredients and raw materials through the production process, while logistics providers are using IoT technology for cold-chain monitoring.
Radiofrequency identification (RFID) scanners can be used to track the movements of tagged food products, supporting end-to-end food traceability efforts throughout the supply chain. The range of sensors, cameras, scanners and other IoT devices empower food industry actors to access and collect more comprehensive datasets than those collected with human labor.
Data gathered by these devices can be used to manage food safety deviations in real time, quickly recall unsafe products and create valuable predictive models.
Traceability begins with data collection, but it does not end there. With complex, multi-party supply chains that stretch across our global food system, data communication is critical for end-to-end traceability.
Data standards and communications protocols facilitate seamless data exchange between trading partners. Published in July 2022, GS1’s EPCIS 2.0 standard provides businesses with a standardized way of capturing and sharing traceability data. This presents a common language to capture the what, where, when, why and how of supply chain events. Digital systems that elect to speak the same “language” enable interoperable communication, simplifying the flow of data from one end of the supply chain to the other. These systems can help to reduce the incidence and severity of outbreak occurrence through quicker, more accurate recalls and investigation.
With large pools of data at their fingertips, many organizations are looking to AI to analyze and make use of their food safety data.
During the March 2022 FDA TechTalk podcast, Maria Velissariou, VP of global corporate research and development and chief science officer for Mars, Inc., discussed the company’s use of AI in management of aflatoxin: a toxin that’s prevalence is likely to increase with climate change. Meteorological, geospatial and temporal data are analyzed to create AI-based models that predict the generation of aflatoxin in food crops. This model aims to provide farmers with the tools and information needed to prevent toxin formation in the field.
Regulatory agencies are also taking advantage of novel data analysis technology. Armed with two years of seafood import data, the FDA used machine learning to develop and pilot a predictive model for the identification of non-compliant seafood shipments. The program aimed to improve the agency’s ability to target seafood products that may pose a food safety risk, allowing for more efficient use of limited product testing and investigation resources. FDA plans to apply key learnings from the pilot to explore predictive models with other regulated food products.
As the global food supply chain becomes increasingly complex, the food industry must integrate data-driven solutions by expanding the adoption of technologies that enable data collection, exchange and analysis. We’ve already seen the power of food safety and traceability data in creating predictive and preventative models that benefit public health. Now, moving forward, stakeholders from across the industry must share their findings and work collaboratively to continually raise the standard of food safety practices worldwide.
The modern food supply chain must function efficiently to get people the goods they need while keeping them safe. Preventing long-term outages leading to empty grocery store shelves worldwide is vital. Industry professionals often debate the best methods to improve food logistics and make companies more resilient against supply chain shocks. Here are some of the leading options.
Digital twins are computerized representations of physical objects. They can help retailers, supply chain managers and other stakeholders identify culprits of spoilage and remove much of the guesswork from shifting consumer behavior. Digital twins are also valuable for helping food supply chain partners spot bottlenecks and predict the impacts of process changes before implementing them.
Consumers are notoriously fickle about their food preferences, which makes inventory control challenging. Today, companies are using digital twins to analyze and predict human behavior, allowing them to track trends and respond accordingly. Digital twins can assist with prototyping new food varieties or similar product debuts and provide insight into how consumers will likely respond to those offerings.
Another way digital twins are improving food logistics is by helping decision-makers determine what kind of packaging will allow products to travel with minimal risk of damage. Leaders must engage in a careful balancing act to locate options that meet all minimum requirements, which means finding packages that are lightweight yet sturdy or extra-resistant to crushing.
Earlier this month, researchers from the Swiss Federal Laboratories for Materials Science and Technology (EMPA) published the outcomes of a study that used a digital twin to reduce citrus fruit waste. The team tracked temperature changes in 47 containers of citrus fruits throughout the transport cycle. They then used the associated data to create computerized simulations that helped determine the likelihood of the fruits becoming unsellable during transit. The digital twins analyzed factors such as mold, moisture loss and damage from the cold.
The team confirmed that 50% of the shipments traveled in suboptimal conditions. At the end of 30 days, some of the fruits had a shelf life of only a few days. The team believes that companies will soon be able to integrate digital twin (aka virtual fruit) data along their production and supply chains to optimize storage conditions and reduce food losses.
Logistics professionals who handle consumables are turning to Internet of Things (IoT) sensors that help them understand and verify what’s happening along the supply chain at any time. For example, companies in the industrial food space often have on-site commercial thawing systems to defrost food previously frozen to prevent waste and bacterial growth. Careful monitoring and tight controls stop bacteria from proliferating as the product warms.
One of the primary benefits of IoT sensors is that they can give factory managers real-time alerts of abnormal conditions associated with thawing systems, freezers, refrigerators or other essential equipment supporting food logistics. Companies can then act faster, preventing catastrophic failures that could harm the bottom line and make consumers sick.
IoT sensors can also send time-stamped alerts of when products leave specific areas. Those details can assure supply chain managers that items are moving as they should and alert them to any potential delays. The sensors also record data to indicate if fragile items received rough handling or temperature-sensitive goods are at risk of spoilage due to subpar storage.
Sensors may even help once food reaches supermarkets and restaurants. In 2020, researchers at MIT developed Velcro-like microneedle sensors that pierce packaging and change color to indicate spoilage or bacteria. The research team believes their innovation can help prevent foodborne illness outbreaks and reduce food waste by allowing consumers to check their food before discarding items that are still OK to eat.
Industry professionals increasingly use data analytics platforms to improve food logistics. Many of those solutions help decision-makers choose the best ways to implement automation supply chain planning or other business enhancements. One study of consumer packaged goods (CPG) companies revealed that autonomous tools for planning could cut supply chain costs by up to 10%, raise revenue by up to 4% and reduce inventory by up to 20%, while still meeting customer needs.
In addition to reducing costs and streamlining inventory control, logistics professionals are also looking to data analytics to improve sustainability and reduce environmental pollution.
The Enhancing Agri-Food Transparent Sustainability (EATS) project at the University of Aberdeen views data analytics and artificial intelligence as a powerful combination to help reduce emissions in the food-and-beverage supply chain. EATS is bringing together researchers, businesses and industry stakeholders across the UK to gather data that will be used to build a digital sustainability platform. The platform will allow industry stakeholders to see the level of emissions created by food and drink items throughout their production. The team hopes that this will allow them to identify where improvements in processes could be made to lower emissions. The platform will also include tools to encourage changes in practice.
Food supply chains that mimic the structures of diverse ecosystems are more likely to withstand so-called “black swan” events and experience less-intensive disruptions, according to a study from researchers at Northern Arizona University and Penn State. Using a history of food flow data from U.S. cities, the researchers examined historical connections between supply chain resilience and localized diversity. They found that the diversity of a city’s supply chain explains more than 90% of the intensity, duration and frequency of significant disruptions. Another meaningful takeaway was that the researchers’ model functioned as expected regardless of what caused the supply chain shock.
These examples show just some of the many ways food and beverage industry professionals can use technology to improve logistics. However, there is no universally “best” strategy. Instead, companies interested in making improvements should take the time to identify their organizations’ most pressing pain points and research the most appropriate options. This type of personalized approach is most likely to deliver impactful results.
