This post is sponsored by Deloitte.
Advanced capabilities in safety and quality analytics are creating opportunities for food and beverage manufacturers to manage the risks associated with product safety and ensure regulatory compliance. A panel of specialists from Deloitte discuss how artificial intelligence (“AI”) is advancing analytics, specific components of a safety analytics program and how this technology can help companies avoid or lessen the scope of a recall.
Why is a well-developed quality and safety analytics program essential in today's food and beverage industry?
Product defects and recalls are exponentially increasing with adverse effects to both consumers and food and beverage companies. Food recalls cost an estimated $55.5 billion a year when factoring in medical expenses, productivity loss and mortality, according to a 2015 study by an associate professor at Ohio State University. Many companies are seeking to reduce the manual effort and improve accuracy in issue identification, yet many lack the resources and technological know-how to proactively analyze the massive amount of information from internal and external sources. The explosion of social media also adds another layer of complexity -- companies are under increasing pressure for proactive detection across many different data sources, especially text-rich narratives and complaints, to quickly filter out noise and draw relevant insights. This is why a well-developed quality and safety analytics 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 analytics solution should have a few specific components:
- Natural language processing tools to sift through massive amounts of textual data to identify the true signals that indicate quality or safety issues
- Models developed with advanced data science (such as real-time online learning ) to detect emerging issues and anomalies
- An alert engine to generate detailed, relevant alerts in real time or near real time, prioritized to help companies better understand critical issues and allocate appropriate resources
In addition to incorporating tools and technologies in a quality and safety analytics program, companies should also consider tools to enable business process re-engineering (or engineering) as well as change management within the company culture. These are not new tools, but they are important to help enhance the values of a well-developed quality and safety analytics program.
How do advanced analytics capabilities help companies avoid or lessen the scope of a recall by identifying quality and safety issues?
Analytics enable companies to review and process their data in its entirety, as opposed to sampling a subset of the 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. Analytics can cross-correlate disparate data sources to draw insights that may otherwise be impossible for humans to spot. Additionally, detecting safety and quality issues early on helps put a stop to manufacturing more defective products, thus reducing the scope of a recall.
What are the limitations of software used for advanced analytics? Why is the human touch still an essential part of safety and quality analytics?
The term “augmented intelligence” has been gaining popularity. Augmented intelligence emphasizes artificial intelligence’s assistive role and focuses on the fact that artificial intelligence is designed to enhance human intelligence rather than replace it. A well-blended safety and quality analytics solution should utilize the power of humans with machines through cognitive, artificial intelligence and data technologies. The advanced analytics engine constantly learns from human interactions and becomes smarter and faster in detecting known and unknown issues from many internal and external data sources.
What data sources can be used in a food safety and quality analytics program to detect and mitigate issues?
Internal data sources include call center transcripts, customer surveys, emails, product return comments, warranty comments, etc. External data sources include social media posts, blogs, forums, e-commerce website reviews, regulator reports, etc. It’s important to proactively mine social media data sources. News stories that go viral due to safety concerns or poor quality can sometimes have a detrimental impact on a food and beverage company’s brand name and share value.
Derek Snaidauf is a principal 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 Analytics and Automotive Analytics practices, as well as its Intelligent Quality & Safety and Warranty Cost Reduction service offerings.
Helen Chen is a strategy and operations manager with Deloitte Consulting LLP. She has more than 13 years of experience in consumer product, retail and restaurant industries. Prior to Deloitte, Helen was with a large consumer products company and focused on food safety and strategic supply management.
James C. Cascone is a partner at Deloitte & Touche LLP. Cascone is the US and global leader for strategic and reputation risk sensing, as well as the restaurant and foodservice industry leader for the Deloitte Touche Tohmatsu Limited network of member firms.
Hong (Jo) Zhou is senior manager in Deloitte Transactions and Business Analytics LLP. Zhou specializes in quality and safety analysis and regulatory and compliance risk analysis. She has significant experience in collaborating with clients to create and help them implement innovative analytic capabilities and solutions to support risk modeling, safety and recall management, and predictive quality and warranty analysis.
This article contains general information only and Deloitte is not, by means of this article, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This article is not a substitute for such professional advice or services, nor should it be used as a 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 shall not be responsible for any loss sustained by any person who relies on this article.
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