Food For Thought | Reduce Food Waste with Data

Did you know? Around one-third of all food produced for people is wasted each year. That's about 1.3 billion tons of food wasted annually! It's a big problem. But data & analytics can help reduce food waste. It shows us how to use resources better and make smart choices, saving money and protecting the environment.

Using Data & Analytics to reduce food waste

Understanding food waste patterns is crucial for implementing effective waste reduction strategies. Data & Analytics plays a key role in this process for a food manufacturer. You might not realize it, but you likely have more data than you know that can help reduce food waste.

However, data sits in multiple systems and you need to collect, connect, and visualize it to uncover food waste patterns. To help you get started, here's a reference cloud-architecture for collecting data from common systems in the food industry:

Here's how data from the most common systems in your organization could be combined to identify food waste patterns:

Point-of-Sale Data and Inventory Tracking Systems

Combining sales data from point-of-sale systems with inventory tracking data enables organizations to analyze sales trends and inventory turnover rates. This comparison helps identify products with high rates of spoilage or expiration, indicating potential sources of waste.

Waste Audits and Consumer Surveys

Integrating data from audits with consumer surveys provides insights into the types of food waste and insight into consumer behaviors. For example, waste audit data may reveal that a significant portion of food waste consists of perishable items. And consumer feedback may indicate dissatisfaction with portion sizes or packaging.

ERP and MES systems

ERP and MES systems capture data on production processes, resource usage, and operational efficiency. Integrating data from these systems with PoS- and inventory tracking data, organizations can analyze production yields, process efficiencies, and inventory management. This integration helps identify areas of inefficiency in production and distribution processes that contribute to food waste.

Internet of Things (PLC)

Machine data from sensors and equipment in production facilities provides insights into equipment performance, energy consumption, and production throughput. Combining machine data with ERP and MES data allows organizations to monitor equipment, identify maintenance issues, and optimize production processes. This helps in minimizing resource waste and food spoilage.

By combining data from these sources, food manufactures gain a holistic understanding of food waste patterns. Secondly, they are able to identify opportunities for waste reduction within their own processes and even throughout the supply chain.

Who's already reducing food waste with data?

You’re not alone in this fight to reduce food waste. If you're wondering whether investing data & analytics pays off, just see what these companies achieved:

• Tesco: Through in-depth analysis of sales data, inventory levels, and customer behaviors, Tesco achieved an impressive 18% reduction in food waste in its stores.
  
• Walmart: Leveraging predictive analytics, Walmart optimized inventory management and achieved a noteworthy 12% reduction in food waste across its supply chain in 2022.
  
• Nestlé: By using machine data, Nestlé significantly enhanced production efficiency and slashed waste for disposal per ton of product by a remarkable 88%.

These real-world examples underscore the tangible benefits of data-driven approaches in mitigating food waste and fostering sustainability.

Challenges in data & analytics to reduce food waste?

Implementing data analytics to reduce food waste presents several challenges and considerations that organizations must address such as:

Data Collection and Quality Assurance Challenges

Gathering and aggregating data from multiple sources, such as point-of-sale systems, inventory tracking systems, ERP, MES, audits, can be complex and time-consuming. Ensuring the accuracy, completeness, and consistency of data is essential for meaningful analysis and decision-making.

Organizations may encounter challenges related to data granularity, with some data sources providing more detailed information than others. Harmonizing disparate data formats and standards to create a unified dataset can also be challenging.

Integrating Data & Analytics into existing workflows and systems:

Incorporating data analytics into existing workflows and systems requires careful planning and coordination. Organizations may face resistance to change from stakeholders accustomed to traditional methods of decision-making.

Technical challenges may arise when integrating data analytics tools and methodologies with existing IT infrastructure. Compatibility issues, data governance considerations, and cybersecurity concerns must be addressed to ensure integration of data analytics solutions.

Skills and expertise

Leveraging data analytics for food waste reduction requires specialized skills and expertise in data science, statistics, and information technology. Organizations may need to invest in training and development programs to build internal capabilities or engage external consultants and experts to support their initiatives.

Additionally, fostering a data-driven culture within the organization is essential for the successful implementation of data analytics initiatives. Employees must be equipped with the knowledge and skills to interpret and act upon insights derived from data analytics.

Cost and resource allocation

Implementing data analytics for food waste reduction involves significant investments in technology infrastructure, software tools, and human resources. Organizations must carefully assess the costs and benefits of data analytics initiatives and allocate resources accordingly.

Cost considerations extend beyond initial investments to ongoing maintenance, support, and scalability of data analytics solutions. Organizations must ensure sustainable funding and resource allocation to maintain and enhance their data analytics capabilities over time.

What does the future hold?

Advancements in data analytics, artificial intelligence, and machine learning offer innovative solutions to reduce food waste. Technologies such as predictive modeling, image recognition, and blockchain can revolutionize how organizations track, analyze, and manage food waste throughout the supply chain.

Collaboration between stakeholders across the food industry is essential for leveraging data effectively for sustainability initiatives. By sharing data, best practices, and resources, organizations can collaborate to develop solutions that address food waste at every stage of the supply chain.

Conclusion

To Reduce food waste with data and analytics an organization needs to overcome some challenges. It also underscores the critical role that technology, collaboration, and innovation play to increase sustainability within the food industry. 

Through data analytics, organizations gain insights into the root causes of food waste. This enables targeted strategies for waste reduction and resource optimization. Real-world examples from leading companies such as Tesco, Walmart, and Nestlé demonstrate the value of data-driven initiatives

However, the path to sustainability is not without its challenges. Data collection and integration, skills development and resource allocation is hard. Organizations must set up their organization for success to maximize the potential of data analytics for food waste reduction.

Emerging trends such as predictive modeling, blockchain traceability, and circular economy initiatives offer promising opportunities to drive sustainable change. These trends accelerate collaboration across the food supply chain. Next to that, stakeholders work collectively towards a more resilient, equitable, and environmentally responsible food system.

Don't wait, start reducing food waste with our managed data & analytics platform, FFA Titan!

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