My overview post, Surviving the New Competitive Landscape in Food and Beverage, shares the need for companies to digitalize to compete in today’s rapidly evolving market. The post makes the point that Food and Beverage manufacturers need to step up to compete with small, innovative companies that are changing the rules and shaking up the status quo by leveraging the digital enterprise to break into and redefine today’s omnichannel markets.
These new challengers do business differently. They’re able to identify and react to changes in market and consumer needs in ways previously unattainable. They can take advantage of new opportunities by rapidly developing innovative new products and deploying them across their global the supply chains with speed and quality, and doing so with very high levels of productivity.
What can companies do to compete with these industry disruptors? The post identified three primary initiatives companies can pursue to take advantage of the digital enterprise benefits for themselves:
The Analytics Opportunity for Food and Beverage in the Digital Age
One of the main differences in the way these companies operate is that they’re able to turn knowledge into a competitive weapon. They harness data to gain insights that help them identify and act on threats and opportunities. Analytics is their secret weapon to unlocking information to innovate and drive improvements to both the top and bottom lines.
Leveraging analytics on it’s own can help companies compete in the digital age, but it provides even more value in combination with digital R&D and manufacturing improvements. Let’s take a look!
Analytics in F&B
How does analytics unlock new business value for food and beverage companies? Analytics helps improve performance by creating insights from today’s vast amount of data about markets, products, supply chains, production, quality, consumer behavior, and more. It uncovers hidden data relationships that can lead to big opportunities.
The first step to keeping up with the innovators is being able to sense demand in order to react. Today’s consumer-oriented companies need to be in ever-closer touch with consumers and markets. Smaller, innovative companies can turn on a dime to take advantage of a new trend or need. They can respond with small quantities of targeted products customized to markets, geographies, or even individuals. They also leverage digitalization to stay closer to consumer trends and their desire for personalized products. Traditional companies typically find that almost impossible to do based on the way they’ve optimized their processes for mass production.
Beyond developing market intelligence, companies have to be able to turn an analytical eye toward product, production, and operational performance. They should use analytics to continuously optimize and improve downtime, productivity, and their human, plant, energy, and other resources for sustainable profitability.
Together, market intelligence and internally focused performance metrics help drive higher levels of consumer satisfaction and corporate profitability.
First, it’s important to understand that food and beverage companies have work to do in order to digitalize. Like most consumer-oriented companies, they’ve generated a tremendous amount of data. They have data on markets, sales, manufacturing, the supply chain, and more. They may also be gathering machine data and Internet of Things (IoT) information. But gathering the information has proven to provide limited value.
Now is the time to start unlocking the hidden knowledge inside of the data. Analytics can help pull together disparate data and put in into context so it makes sense. Beyond reporting, applying machine learning uncovers hidden correlations, providing new insights that were previously locked away in mountains of data. This new information opens up a wealth of improvement opportunities for products, processes, and plants. The potential value is significant. Let’s look at some key aspects of how digital food and beverage manufacturers leverage analytics.
Focus: Product Intelligence
Food and beverage companies need to know as much about their products as they can. While most consumer packaged goods companies have been using analytics on shipment, point of sale, and market data for years, they know far less about their products. Some of the most important things they can discover are how consumers relate to them, procure them, and consume them. But it’s also important to know about the product itself. For example, companies should be able to evaluate:
How much is being produced and where?
Where is the inventory in supply chain?
What are the actual ranges of final product specifications?
How much did it actually cost to produce it?
How much variability is there between batches or plants?
What are the largest contributors to quality issues and complaints?
Answering these questions is hard in most companies because of disparate systems and limited reporting capabilities. Applying analytics not only provides information, it helps understand the reasons behind the answers. For example, if one plant is producing a slightly different product than others, analytics can determine if it’s due to a different process setup, environmental differences, inconsistent operator training, manufacturing anomalies, handling procedures, ingredient variability, or some other factors.
Systems have been put in place to track this information, but the data tends to be spread across too many systems to be valuable. For example, actual ingredient costs may be in a procurement or ERP system. Quantities used, processing parameters, and other batch data may be in an MES system. Operator training may be in an HR or Training database, and operator time spent may be in a batch record. The more this information is digitalized and available, the more information can be pulled together for analytics.
Beyond bringing the data together and putting it into context, analytics helps make sense of the data by uncovering connections. In the example about product differences above, there are too many independent variables for most people to consider, particularly if it’s not an intuitive combination. Analytics helps uncover relationships between data that would be difficult or impossible to identify otherwise. There are many questions to be asked about products. Analytics can help provide answers that were previously unreachable.
Focus: Production / Process Intelligence
Beyond the product, food and beverage companies need to know their operations inside and out. They have to understand their performance, including what’s working well and what isn’t.
Analytics can be used to monitor production or troubleshoot issues. It can provide alarms to flag potential issues, or be used to compare performance across equipment, lines, or plants. It can be applied to production data to analyze yield in order to look for improvement opportunities or find out what interferes with efficiency. Analytics for production should include time series data and display results graphically. It can also be used to look for opportunities to improve energy consumption by finding anomalies.
The opportunities are numerous. Sometimes the biggest challenge is deciding where to focus analytical efforts. Modern equipment generates a lot of data, and older equipment is often retrofitted to do so. In addition, many companies have created data backbones for automation and can leverage their PLC data. Emerging IoT and IIoT (Industrial Internet of Things) data can play a significant role as well. The challenge isn’t finding production data; it’s making sense of it all! Analytics can put the vast amounts of process data companies collect to use, instead of locking it away in data historians.
One area a lot of companies are choosing to improve is equipment uptime. Using analytics, companies are moving from preventive maintenance to a predictive approach. They’re using analytics to monitor data to find conditions that lead to failures, for example vibration or excess heat in a motor. Some are taking this a step further to prescriptive maintenance, using analytics and machine learning to not only identify a potential failure but to make recommendations for actions to prevent it. For example, companies can use machine learning and IoT data to identify similarities in issues across equipment that suggest replacing all equipment of a certain kind or from a particular supplier, even if they are currently performing well, because they have been seen to fail unexpectedly elsewhere. This is just one example of how understanding process data can help improve performance metrics.
Other Potential Opportunities
The analytics opportunity is extremely broad when applied to internal systems. Analytics, however, can go much further by expanding the data sources it taps into. For example, companies could include benchmark data from equipment providers to improve productivity or maintenance results. Additionally, manufacturers could analyze social media, plant, product, web, ERP, CRM, and other data sources to help understand consumer behavior or develop consumer profiles to recommend customized products.
Companies can use leverage analytics to find patterns or relationships between seemingly disparate data. For example, they could pull in information to help with food safety and traceability by gathering information about suppliers or logistics providers. Or, they could collect weather conditions from growing regions to compare to natural ingredient specifications to optimize procurement or formulation. Some innovative companies are even experimenting with machine learning to develop algorithms to predict consumer trends or develop new insights to spark innovation from social media data.
Digitalization opens up the opportunity for many kinds of initiatives that can help manufacturers identify and act on opportunities. It can also be used to gather better information to feed back into simulation and optimization to replace assumptions with hard data.
Digitalization Enables Analytics
Analytics can be applied as a standalone solution, but a platform of solutions gives analytics a head start. Integrated solutions provide a common data backbone to get more data from a single source. Perhaps one of the biggest benefits of integrated solutions is providing a cohesive data model that preassembles data in context. This helps solve one of the biggest challenges, determining how data fits together.
Analytics is a critical element of a digitalization transformation, helping pull information together and develop insights. Analytics as a part of a solution platform helps because the data is in context, it maintains a secure collection of information that can be accessed for analytics, and because it should provide common KPI analytics out of the box. Companies shouldn’t have to start from scratch.
The Analytics Payoff
Analytics helps companies compete in the digital age by letting them identify and act on improvement opportunities ranging from increased consumer responsiveness to improved production utilization. Of course it’s not always practical to blindly look at data to find correlations. Companies should start with an issue in mind, for example rework or scrapped batches, and work from there. There’s still a need for ad-hoc analytics, but analytics focused on a particular problem area are particularly valuable.
The key is to create new knowledge from the masses of data currently available, to find underlying causes and correlations, and use those insights to improve cost, quality, productivity, uptime, and consumer intimacy. This is the way analytics can be harnessed to compete with today’s digital challengers. How will your company leverage analytics to improve customer responsiveness and operational performance?