How advanced analytics can help manufacturers manage risk and meet regulatory requirements

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This article is sponsored by Deloitte.

Advanced safety and quality analysis capabilities create opportunities for food and beverage manufacturers to manage risks associated with product safety and ensure regulatory compliance. A panel of specialists from Deloitte discuss how artificial intelligence (“AI”) advances the analysis, the specific components of a safety analysis program, and how this technology can help organizations avoid or reduce the scope of a recall.

Why is a well-developed quality and safety analysis program essential in today’s food and beverage industry?

Product defects and recalls are increasing exponentially with adverse effects for both consumers and food companies. Food recalls cost an estimated $ 55.5 billion a year when factoring in medical expenses, lost productivity and mortality, according to a 2015 study by an associate professor at Ohio State University. Many companies are looking to reduce manual effort and improve the accuracy of problem identification, but many lack the resources and technological know-how to proactively analyze the massive amount of information coming from internal and external sources. . The explosion of social media also adds another layer of complexity: Companies are under increasing pressure to proactively detect in many different data sources, especially text-rich stories and complaints, to quickly filter out noise. and extract relevant information. This is why a well-developed quality and safety analysis program is so important – to help improve brand reputation, increase customer satisfaction, and comply with regulatory requirements.

What new tools are available to help manufacturers produce safe, high-quality products that meet or exceed compliance requirements?

A robust quality and safety analysis solution should have a few specific components:

  • Natural language processing tools to sift through massive amounts of textual data to identify real signals that indicate quality or safety issues
  • Models developed with advanced data science (like real-time e-learning) to detect emerging issues and anomalies
  • An alert engine to generate detailed and relevant alerts in real or near real time, prioritized to help companies better understand critical issues and allocate appropriate resources

In addition to integrating tools and technologies into a quality and safety analysis program, companies should also consider tools that enable the reengineering (or engineering) of business processes as well as the management of change within the organization. corporate culture. These are not new tools, but they are important in helping to improve the values ​​of a well-developed quality and safety analysis program.

The Deloitte expert panel (clockwise from top left: Snaidauf, Chen, Cascone and Zhou)

How do advanced analytics capabilities help businesses avoid or reduce the scope of a recall by identifying quality and safety issues?

Analyzes allow companies to examine and process their data in its entirety, instead of sampling a subset of data for manual review. This can help mitigate the risk of not looking in the right places to find issues that would lead to potential recalls. Analyzes can cross-correlate disparate data sources to derive information that might otherwise be impossible for humans to spot. In addition, the early detection of safety and quality issues helps stop manufacturing more defective products, reducing the scope of a recall.

What are the limitations of the software used for advanced analysis? Why is human contact always an essential part of safety and quality analysis?

The term “augmented intelligence” is growing in popularity. Augmented intelligence emphasizes the assisting role of artificial intelligence and focuses on the fact that artificial intelligence is designed to enhance human intelligence rather than replace it. A well-combined security and quality analytics solution must harness the power of humans with machines through cognitive, artificial intelligence, and data technologies. The advanced scan engine continually learns from human interactions and becomes smarter and faster at detecting known and unknown issues from many internal and external data sources.

What data sources can be used in a food safety and quality analysis program to detect and mitigate issues?

Internal data sources include call center transcripts, customer surveys, emails, product return comments, warranty comments, and more. External data sources include social media posts, blogs, forums, ecommerce website reviews, regulator reports, etc. social media data sources. News stories that go viral due to safety concerns or poor quality can sometimes negatively impact a food and beverage company’s brand and share value.

Derek Snaidauf is a director at Deloitte Transactions and Business Analytics LLP and a recognized specialist in data science and artificial intelligence. He holds leadership roles in Deloitte’s forensic and automotive analysis practices, as well as its smart quality and safety service offerings and lower warranty costs.

Helen Chen is Director of Strategy and Operations at Deloitte Consulting LLP. She has over 13 years of experience in the consumer products, retail and restaurant industries. Prior to Deloitte, Helen worked for a large consumer products company and focused on food safety and strategic sourcing management.

James C. Cascone is a partner at Deloitte & Touche LLP. Cascone is the U.S. and global leader in strategic and reputational risk detection, as well as the restaurant and catering industry leader for the network of member firms of Deloitte Touche Tohmatsu Limited.

Hong (Jo) Zhou is a senior executive at Deloitte Transactions and Business Analytics LLP. Zhou specializes in quality and safety analysis and regulatory and compliance risk analysis. She has extensive experience working with clients to create and help them implement innovative analytical capabilities and solutions to support risk modeling, safety and recall management, and predictive analytics. quality and guarantees.

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This article contains general information only and Deloitte does not provide, through this article, any accounting, commercial, financial, investment, legal, tax or other advice or services. This article is not a substitute for such professional advice or services, nor should it be used as the basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor.

Deloitte will not be responsible for any loss suffered by anyone who relies on this article.

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