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Business Intelligence in The Food Industry

As companies generate a large amount of data daily, only a few are incorporated for meaningful insights. This is mainly because these organizations follow a food bi strategy that extracts useful knowledge. Companies and organizations implementing a smart plan of action are more likely to stand out and have a competitive advantage. 

These enterprises pay attention to analytics and traditional reporting and focus on predictive analytics to provide high-quality products. Logical frameworks and business intelligence strategies have replaced intuition and experience brought to the table by mid-level management in today’s organizations and companies. 

Suppose your company or business competes in the food bi industry. In that case, there are high challenges in ensuring regulation compliance, increasing portfolios, and managing a complex supply chain with various demands. 

Unlike other industries, the FMCG industry relies on strict and prompt market research, which can be used to execute effective marketing campaigns. Moreover, any company in this industry needs a well-structured distribution network paired with a professional sales force to apply strategies and be prompt to act quickly to meet specific targets.  

Food bi industry has come up with various components to strengthen its position in the market, improve and develop new sales channels, and enhance the effectiveness of its supply chains. FMCG should combine business intelligence, technology, and management to win the market. 

Since the Fast-Moving Consumer Goods (FMCG) industry is highly competitive, organizations must stay ahead while possessing high business acumen and intelligent tools. And that entails, making use of DotNetReport’s Ad Hoc Report Builder for features that are simply too good to let by!

Business Intelligence (Food BI) Puts Focus on Information

food bi

The main goal of business intelligence is to translate current data into meaningful information. This information is used as the foundation on which tactical, operational, and insightful decisions are made within any enterprise. This translation of data is in the form of reports and analyses. 

Business intelligence is unique mainly because it takes information from internal and external sources within an organization. 

Food bi helps in optimizing business processes once they are analyzed. As a result, you are better able to make choices about, among other things, the distribution of goods and services, the management of stocks, the control over production, and the procurement of materials. 

You can make strategically-based decisions with the help of business intelligence. BI allows you to determine which market is more interesting, how to produce high-quality items, how to achieve customer satisfaction, how logistics can be optimized, etc.  

Importance and Limitations of Data Analytics in The Food Industry

food bi

In today’s competitive climate, many companies face challenges in growing effectively. 

The company is using data analytics food industry in the FMCG market to expand its whole range.

FMCG companies currently have the opportunity to restructure their marketing and operations. They may embrace proactive tactics by utilizing data analytics techniques, moving beyond reactive functions.

The food bi industry is affected by various factors, including marketing, inventories, seasonal fluctuations, returns, out-of-stock items, the availability of raw materials, and regional pricing. In these unpredictable times, the FMCG industry must rely on data analytics to find patterns, gaps, and opportunities in customer behavior.

Summing it up, here are some of the main highlights of having business intelligence in food industry as a technological advantage.

Key Takeaway:

Data analytics plays a crucial role in the food industry, offering valuable insights and driving informed decision-making. 

It enables businesses to optimize processes, enhance customer experiences, and improve overall operational efficiency. However, there are also limitations and challenges to consider when implementing data analytics in the food industry.

Let’s explore the importance and limitations in more detail:

Importance of Business Intelligence in Food Industry:

Demand Forecasting: Data analytics helps businesses accurately forecast demand by analyzing historical sales data, market trends, and customer preferences. This enables efficient inventory management, minimizing waste and ensuring products are readily available to meet customer demands.

Supply Chain Optimization: By analyzing data related to suppliers, transportation, and logistics, data analytics improves supply chain efficiency. It helps optimize sourcing, reduce costs, and minimize delivery delays, ensuring timely availability of ingredients and products.

Quality Control and Food Safety: Data analytics aids in monitoring and ensuring food safety and quality. It enables real-time monitoring of various parameters, such as temperature, humidity, and ingredient quality, to detect and address potential issues promptly. This helps prevent foodborne illnesses, maintain product quality, and adhere to regulatory requirements.

Customer Insights and Personalization: Analyzing customer data allows businesses to gain valuable insights into preferences, purchasing patterns, and behavior. This helps in tailoring marketing strategies, developing personalized offers, and enhancing customer experiences to drive customer loyalty and satisfaction.

Menu Optimization: Data analytics helps optimize menu offerings based on customer preferences, sales data, and profitability analysis. It enables businesses to identify popular dishes, eliminate underperforming items, and introduce new menu options that align with customer preferences and maximize profitability.

Pricing and Promotion Strategies: Data analytics aids in determining optimal pricing strategies based on factors such as cost, competition, demand, and customer behavior. It also enables businesses to evaluate the effectiveness of promotional campaigns, identify trends, and make data-driven decisions to maximize revenue.

Operational Efficiency: Data analytics improves operational efficiency by analyzing data from various operational processes, such as production, distribution, and inventory management. It identifies bottlenecks, streamlines processes, reduces waste, and enhances overall productivity.

Limitations and Challenges of Food Marketing Intelligence:

Data Quality and Availability: Data analytics heavily relies on the availability of high-quality and accurate data. In the food industry, obtaining reliable data can be challenging due to fragmented data sources, inconsistent data formats, and data privacy concerns.

Ensuring data quality and establishing proper data governance processes are crucial for effective analytics.

Data Integration and Compatibility: Integrating data from disparate systems and platforms can be complex, especially when dealing with legacy systems or different data formats. Incompatibilities between different data sources can hinder the seamless flow of data and require additional efforts for data integration.

Data Security and Privacy: The food industry handles sensitive information, including customer data and proprietary recipes. Ensuring data security and protecting customer privacy is of paramount importance. Businesses must adhere to data protection regulations and implement robust security measures to safeguard data.

Analytical Skills and Resources: Implementing data analytics requires skilled professionals who possess analytical expertise and domain knowledge in the food industry. Finding and retaining talent with the necessary skills can be a challenge. Additionally, acquiring and maintaining the required technological infrastructure and resources for data analytics can be costly.

Interpretation and Actionability: Extracting meaningful insights from data is just the first step. Interpreting the analysis results and translating them into actionable strategies can be challenging. It requires collaboration between data analysts and domain experts to effectively apply the insights to business operations and decision-making processes.

Benefits of Leveraging Business Intelligence in The FMCG Industry

food bi

Have a look at the functions of business intelligence in the FMCG industry: 

Optimize FMCG distribution networks

A very crucial component of the FMCG supply chain network is product delivery. By incorporating business intelligence, your company may process geo-analytics to integrate and simplify distribution networks. This makes delivery more prompt and effective, thus increasing the accuracy of services; items are delivered on time, and there is no delay in deliveries. 

Enhanced warehouse management

The FMCG supply chain network also improves warehouse management via business intelligence. It enables real-time monitoring of all the activities such as inventory levels, performance tracking, delivery records, etc. 

When we talk about food-producing companies, it is important to keep records of the production process, such as plans, reports, forecasts, productivity, etc. 

In food-producing companies, forecasts, plans, reports, follow-up, productivity, and other aspects of the production process need to be under control.

Our Ad Hoc Report Builder gives you the necessary overview of all data relevant to your production performance, so you can make informed decisions.

Elevated efficiency

The effectiveness of the items can be increased by using data analytics food industry and different ML algorithms on the collected data. For instance, weather forecasts can be useful to shippers for shipping, restaurants for customer availability and pricing, and farmers for planting.

Additionally, knowledge of the severe effects of crop production in a particular farm region can be gained from data on the soil’s temperature, humidity, nutrients in the soil, etc., of farm areas.

The use of predictive algorithms can prevent harm to several goods. Thus, being aware of the weather will help shippers deliver goods effectively.

Restaurant operators can utilize data science as a potent tool to help them create a brand-building or brand-maintaining business strategy.

More swift deliveries

The food bi industry is one where timing is highly important. The first and most important duty of any company operating in this sector is to ensure that the food item is delivered to the customer on time. Despite all the complexities involved in this process, couriers and delivery companies today have access to cutting-edge technologies. 

To enable prompt deliveries, data analytics can be used to keep track of the different variables such as distance, construction, weather, route modifications, and factors like traffic and route changes. The time required to get to a specific delivery location is calculated using a sophisticated and complex system, such as artificial intelligence.

Sentiment analysis

Sentiment analysis of customer sentiment, which implies a customer’s feelings or emotions about a brand and its products, is a technique that companies frequently use to understand the sentiments of their customers and what they think of their brand/product. 

It is a very important component for any business in the modern world. This type of study is used in the food sector to understand current trends and well-liked products.

Companies use Natural Language Tools and Toolkit to help them gain deep insights into the thoughts and feelings of their customers, which in turn helps to increase sales.

By analyzing client emotions displayed on social media platforms like Twitter or Facebook, Big Data assists in deciphering customer feelings. Big data can assist in analyzing bad evaluations and initiating the necessary precautions before the harm worsens. 

Simplified supply chain procedures

The benefits of business intelligence FMCG industry are significantly influenced by the supply chain, with stock availability being a major decision factor for industry players. Business intelligence may be related to streamlining the supply chain process from beginning to finish by giving stakeholders accurate data analysis rather than depending on guesswork to ensure that your product is always available.

Quality control

Many fruits, vegetables, dairy products, and other temperature-sensitive foods must be monitored. Big data Analytics can be used to track these things while taking into account the entire supply chain cycle. When quality is compromised, you have full access to replace or refund these things and to take preventive action.

The quality of the raw materials used to manufacture a product can also be checked using big data-powered technologies.

Additionally, using data analysis and machine learning, the business may improve customer service and inadequate food quality management by surveying to gather consumer input on supply change management and product quality.

Promotions targeting specific customers

Customer insights provided by BI allow for more targeted consumer campaigns that directly address the customer’s needs. Your business can develop a customized campaign that encourages brand loyalty and suitably. 

Develop products that consumers want

With this information, you can also make changes and start producing goods that people want rather than trying to sell them things that are already on the market or have been for a while. Big companies benefit from business intelligence FMCG and use crucial information to launch new product lines based on this data and meet current and evolving demands.

To ensure that you keep profiting from this fantastic investment, BI tools also provide ongoing business information assistance. It provides granular business information solutions that let businesses track operational effectiveness, control spending, and manage budgets in real-time.

Ad-Hoc reporting aims to enable end users to ask specific questions about their firm data and generate various reports for various functions and purposes without the involvement of IT.

In a Forbes report, 78% of organizations cited Ad-Hoc analysis as a critical or very important feature in business intelligence adoption. This trend continues, as Ad-Hoc reporting is still the most popular BI feature.

Managing your company’s business intelligence is totally unique, which is why it’s important to develop a personalized approach.

The strengths of self-service BI

Ad hoc analysis could be useful depending on what you’re trying to accomplish.

  • The software is easy to use, agile, and quick to learn
  • Due to the fact that most of these tools reside in the cloud, they are easily accessible
  • Ad hoc reports can be accessed in many different ways (e.g., drilling down into data, viewing charts, or creating crosstabs)
  • The system is designed with reusable components, so you can create more and more ad hoc reports, or interact with multiple reports at once

With Ad-Hoc reporting, end users can create a variety of reports for different functions and purposes by asking specific questions about their company data without the involvement of IT. When multiple users can look at, understand, and act on data independently, while looking at the same numbers, ad-hoc reporting makes sense. Multidimensional data can be analyzed on the fly with ad-hoc reporting. Using it, you can create meaningful custom reports without having to build formal templates.

Key Takeaway:

The FMCG (Fast-Moving Consumer Goods) industry greatly benefits from leveraging business intelligence (BI) tools and techniques. 

Business intelligence in food industry provides valuable insights, enhances decision-making processes, improves operational efficiency, and enables competitive advantage. 

Here are the key takeaways concerning the benefits of leveraging business intelligence in the FMCG industry:

Enhanced Decision Making: 

Food intelligence empowers FMCG businesses with data-driven decision making. It enables executives and managers to access real-time and accurate information about sales, inventory, market trends, and consumer behavior. 

With this insight, decision makers can make informed choices regarding product development, pricing, distribution, and marketing strategies.

Improved Demand Forecasting: 

FMCG companies heavily rely on accurate demand forecasting to optimize production, inventory management, and supply chain operations. 

Business intelligence tools provide sophisticated forecasting models based on historical sales data, market trends, and external factors. By leveraging these tools, FMCG businesses can optimize production plans, minimize stock-outs, reduce wastage, and meet customer demands more efficiently.

Customer Insights and Personalization: 

Food market intelligence allows FMCG companies to gain deep insights into customer preferences, buying patterns, and behaviors. 

Analyzing customer data from various sources, such as point-of-sale systems, loyalty programs, and social media, enables businesses to segment their customer base and develop personalized marketing campaigns. 

By delivering tailored product offerings and targeted promotions, FMCG companies can enhance customer satisfaction, loyalty, and ultimately drive sales.

Supply Chain Optimization: 

Effective supply chain management is critical for FMCG companies to ensure timely delivery, minimize costs, and maintain optimal inventory levels. 

Business intelligence in food industry also provides visibility into the entire supply chain, allowing businesses to identify bottlenecks, streamline processes, and optimize logistics. 

By analyzing data related to suppliers, transportation, production, and inventory, FMCG companies can improve supplier relationships, reduce lead times, minimize stockouts, and increase overall supply chain efficiency.

Competitive Advantage: 

In the highly competitive FMCG industry, gaining a competitive edge is crucial for success. Business intelligence enables FMCG businesses to monitor market trends, analyze competitor performance, and identify emerging opportunities. 

By leveraging this information regarding food market intelligence, companies can make strategic decisions regarding product innovation, market expansion, pricing strategies, and promotional campaigns, giving them a competitive advantage in the market.

Operational Efficiency: 

Business intelligence tools enable FMCG companies to streamline their operations, optimize resource allocation, and improve productivity. 

By analyzing data on production processes, inventory levels, and sales performance, businesses can identify areas for improvement, reduce waste, and enhance operational efficiency. This leads to cost savings, improved profitability, and better resource utilization.

Real-time Monitoring and Reporting: 

Business intelligence provides real-time monitoring and reporting capabilities, allowing FMCG companies to track key performance indicators (KPIs) and metrics. 

By accessing dashboards and reports, decision makers can stay updated on sales trends, inventory levels, market share, and other crucial metrics. 

This real-time visibility enables proactive decision-making, quick problem identification, and timely corrective actions.

In summary, leveraging business intelligence in the FMCG industry offers significant benefits, including enhanced decision making, improved demand forecasting, customer insights and personalization, supply chain optimization, competitive advantage, operational efficiency, and real-time monitoring and reporting. 

By harnessing the power of data and analytics, FMCG businesses can gain valuable insights, drive growth, and stay ahead in a dynamic and competitive market.


To conclude, business intelligence is an essential tool in the fast-moving consumer products sector, and more notably in the food and beverage industries, where it enables management to produce accurate forecasts and effectively evaluate new prospects. The combination of advanced technologies such as Machine Learning and Artificial Intelligence ensures accurate and fast analyses by calculating the colossal mass of data available in any enterprise. To increase the success of one’s firm in this industry specifically, it is essential to ride the wave by keeping an eye on consumer purchasing trends and the market. 

Simply acquiring business intelligence software platforms will not suffice: a new and comprehensive data collection strategy is necessary.

With DotNetReport, a ready-to-use business intelligence solution, you can begin making reports right away! Reach out to us today! 


Aside from ‘Dotnet Report, what are the best reporting software for the businesses in the food industry right now?

There are several reporting software options available for businesses in the food industry, each with its own set of features and benefits. Here are some of the popular reporting software solutions used in the food industry:

Power BI: 

Power BI, developed by Microsoft, is a robust business intelligence tool that offers powerful reporting capabilities. 

It allows businesses to connect to various data sources, create interactive dashboards, and generate visually appealing reports. Power BI provides advanced analytics features, data visualization options, and integration with other Microsoft products. 

Its benefits include real-time data updates, collaboration features, and a user-friendly interface.


Tableau is a widely-used data visualization and reporting tool that offers a range of features for the food industry. It supports data integration from multiple sources, including spreadsheets, databases, and cloud services. 

Tableau allows users to create dynamic and interactive visualizations, build customized reports, and perform advanced analytics. Its benefits include a drag-and-drop interface, seamless data connectivity, and a large user community for support and knowledge sharing.


QlikView is a data discovery and reporting platform known for its associative data model, which enables users to explore data intuitively. 

It offers interactive dashboards, ad-hoc reporting capabilities, and powerful data visualization options. 

QlikView allows businesses to analyze data from various sources, create dynamic reports, and make data-driven decisions. Its benefits include ease of use, data exploration capabilities, and efficient data processing.


Domo is a cloud-based reporting and business intelligence platform that caters to the needs of food businesses. It provides real-time data integration, customizable dashboards, and automated reporting functionalities. 

Domo offers pre-built connectors for popular data sources and allows users to create visually appealing reports with interactive elements. Its benefits include centralized data management, mobile access, and collaboration features.

SAP BusinessObjects: 

SAP BusinessObjects is a comprehensive suite of reporting and analytics tools designed for enterprise-level businesses. 

It offers a wide range of reporting options, including ad-hoc reporting, pixel-perfect reporting, and interactive dashboards. SAP BusinessObjects provides advanced analytics capabilities, data exploration features, and integration with other SAP products. 

Its benefits include enterprise-grade security, scalability, and integration with SAP ERP systems.


Looker is a cloud-based data platform that focuses on data exploration, analytics, and reporting. 

It allows businesses to connect to various data sources, create interactive dashboards, and generate customized reports. 

Looker provides features like embedded analytics, data modeling, and collaboration capabilities. 

Its benefits include easy data exploration, SQL-based querying, and a user-friendly interface.

These reporting software solutions offer a range of features and benefits, including data integration, interactive dashboards, advanced analytics, data visualization, and collaboration capabilities. 

When selecting a reporting software for your food business, consider factors such as your specific reporting needs, data sources, scalability requirements, ease of use, and the level of support and resources available for each solution.

What is the conventional method for report building and presentation in the food business intelligence industry?

The traditional approach to report building and presentation in the food business intelligence industry typically involves several steps and follows a structured process. Here is a general overview of the conventional method:

Data Gathering: The first step is to gather relevant data from various sources within the organization. This can include sales data, inventory data, customer data, market research data, and any other relevant information needed for reporting and analysis.

Data Cleaning and Transformation: Once the data is collected, it needs to be cleaned and transformed to ensure its accuracy and consistency. This involves removing duplicate entries, handling missing data, standardizing formats, and performing any necessary calculations or data manipulations.

Data Modeling and Analysis: After the data is cleaned and transformed, it is modeled to fit the reporting requirements. This may involve creating data tables, defining relationships between different datasets, and performing analysis using statistical methods or data mining techniques.

Report Design and Layout: The next step is to design the report layout and structure. This includes selecting appropriate visualizations, charts, and graphs to present the data effectively. The layout should be intuitive, organized, and visually appealing, making it easy for the audience to understand the information presented.

Report Generation: Once the report design is finalized, the actual report is generated using reporting tools or software. This involves populating the report template with the analyzed data and applying the chosen visualizations and formatting.

Report Distribution and Presentation: After the report is generated, it is distributed to the intended audience. This can be done through email, shared folders, or by publishing the report on a reporting portal.

In some cases, the report may be presented in person during meetings or presentations to discuss the findings and insights derived from the data.

Report Updates and Iteration: Reports in the food business intelligence industry often require regular updates to reflect the latest data and changes in the business environment.

As new data becomes available, the report may need to be refreshed, and the analysis and visualizations may need to be adjusted accordingly. This iterative process ensures that the reports remain relevant and up-to-date.

It’s worth noting that the conventional method may vary across organizations, and some businesses may adopt more streamlined or automated approaches using dedicated reporting software or business intelligence tools. 

The specific steps and processes may also be influenced by the organization’s reporting requirements, available resources, and technology infrastructure.

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