Food and beverage manufacturers collect enormous amounts of data every day, from quality checks on incoming ingredients to the fill accuracy and net weight on packaged goods. This data is typically collected for a single purpose, like traceability or audits, and often done so manually. But, unbeknownst to most manufacturers is the transformative potential of their quality data – the ability to provide strategic insight that can help uncover opportunities to make enterprise-wide improvements.

To realize their data’s full potential, manufacturers need to re-imagine quality and the long-held notion that data collection is only used for compliance and traceability purposes. If data collection is automated and the data is unified from across the enterprise, it creates visibility into the manufacturing performance of the entire organization, instead of just one plant or production line. When rolled up to the corporate level for strategic analysis, the data becomes actionable as operational insights that can drive global improvements, mitigate risks and save money.

Unfortunately, many manufacturers rely on manual data collection and 20- or 30-year-old systems that store data at the local level. While these systems might be useful for addressing problems in a reactive manner, they are prone to human errors and create data silos, which prevent manufacturers from looking beyond the production line or a single plant’s performance.

To bring manufacturing technology up to date and reap the real benefits of quality data, food and beverage processors must automate data collection, employ cloud technology and adopt state-of-the-art quality software, combined with new data collection practices and modern analytics.

Automate data collection

According to a study of 260 manufacturers conducted by InfinityQS, Fairfax, Va., 75% of respondents are still manually collecting data, and 47% of those rely on pencil and paper. Not only does manual data collection put a manufacturer’s data at risk for human errors, but it also lacks efficiency. Plus, once the data is placed on paper and filed, it is virtually impossible to retrieve, summarize and aggregate for analysis purposes.

Previously, operators would manually take readings, calculate an average of those readings and plot the result on a paper chart that quality assurance personnel would use to calculate trends and create reports by hand. Automating data collection eliminated the 8- to 10-hour process in favor of near-instantaneous reporting and major reductions in paper usage.

Because the data is collected in real time and is easily accessible by anyone with a computer, performance can be tracked across production lines, and issues throughout the enterprise are quickly highlighted and acted upon. This ensures a higher quality product across all production lines, less raw material variation and consumption and increased cost savings – especially when high-quality ingredients are required.

Unify plant-floor data

Most food and beverage manufacturers have multiple geographically dispersed plants, and each plant often has its own unique naming conventions for the same products, lines, machines and tests. When each plant is speaking a different language, aggregating and summarizing cross-plant data for macro-level analysis becomes nearly impossible.

The key to unifying plant data and attaining enterprise visibility is hosting quality management software in the cloud. By doing this, companies can centralize data into one unified database, thereby breaking down data silos for a real-time look across operations. A centralized database helps enforce universal naming conventions across all plants, which means everyone – from individual plant floor operators to quality assurance executives – is speaking the same language and on the same page.

Roll local data up to the corporate level

When plant and supplier data is unified from sites around the world, they can be easily rolled up to the executive level for analysis by quality professionals, vice presidents, operations managers and C-level executives. Slicing and dicing the data to compare plant-to-plant, product-to-product and line-to-line differences can uncover operational insight that drives improvements across the enterprise.

Such comparative analyses can show which sites need help and where the biggest efficiency gains and cost-savings opportunities are located. Company resources can be allocated where they’re needed most, and new efficiencies and strategies can be applied throughout the entire organization. Manufacturers can also see where the top performing lines and plants are and then take those best practices and standardize them across all facilities. Moreover, with a cloud environment, production data can be analyzed, refreshed and evaluated in real time, eliminating decision-making lags that can compound quality issues.

One innovative multi-national producer and distributor of refrigerated food products deployed cloud-based quality management software and can now easily look across the enterprise to identify trends and improvement opportunities for reducing costs. They can identify root causes of variations and make adjustments before problems occur. Desktop dashboards with a variety of analyses and charts display exactly what is happening across multiple plants in real time, so supervisors and executives can see which plants, lines and/or products are running efficiently and consistently.

Using quality data beyond the plant floor is something most manufacturers have not yet embraced. But, by adopting new data collection standards and practices, modern analytics and a strong cloud-based quality software backbone, the information that manufacturers are already collecting on the plant floor can create enterprise-wide visibility that, when analyzed, offers operational insight. With this wider view, companies can look at previously fragmented operations as a whole and can realize their data’s full potential and make organization-wide improvements toward manufacturing excellence.