Every agribusiness – whether a small farm cooperative or a large food processing company – runs on information. But not all information is used the same way. A warehouse manager tracking daily dispatches has different needs than a CEO planning next year’s procurement strategy. This is exactly why organizations use multiple, purpose-built information systems rather than one single system. Each type is designed to handle a specific level of work, from routine daily transactions all the way to high-stakes strategic decisions. Understanding these systems helps you see how modern agribusinesses stay organized, efficient, and competitive.
Table of Contents
- Why organizations need different types of information systems
- Office automation system (OAS)
- Who uses it and at what level
- Transaction processing system (TPS)
- The foundation for higher-level systems
- Decision support system (DSS)
- DSS in agriculture
- Executive information system (EIS)
- Customized for the C-suite
- Business expert system
- How the inference engine works
- Applications in agribusiness
- How these systems work together
Why organizations need different types of information systems
An organization operates at multiple levels simultaneously – operational staff handle day-to-day tasks, middle managers analyze performance and solve problems, and top executives chart long-term direction. Each of these levels requires a different type of information system: operational levels rely on transaction processing and office automation, management levels use decision support and reporting tools, and the strategic level depends on executive-oriented systems. Layering these systems together creates a complete information infrastructure that serves everyone in the organization.
In agribusiness specifically, information systems are used to reduce operating costs, increase profits, enhance problem-solving ability, and improve decision-making processes. From a grain trader recording daily sales to a farm cooperative’s board reviewing annual market trends, information systems make it possible.
Office automation system (OAS)
The office automation system is the most widely used and universally familiar type of information system. OAS supports data workers who analyze information and transform or manipulate it before sharing it with others in the organization. In practical terms, this includes word processing, spreadsheets, email, electronic scheduling, desktop publishing, voice mail, and video conferencing.
Employees perform tasks electronically using computers and other devices instead of manually, which reduces paperwork, speeds up communication, and cuts down on administrative errors. In an agribusiness setting, an OAS might be used by a procurement office to draft supplier contracts, schedule meetings with logistics partners, or circulate internal memos about harvest schedules.
Who uses it and at what level
An OAS is primarily focused on communication technology, computers, and people, and it supports official activities at various organizational levels – for both managerial and clerical responsibilities. Unlike more specialized systems, OAS tools are used by virtually everyone in an organization, from front-desk staff to department heads. The key benefit is that it reduces the manual effort in routine office work and ensures smoother information flow across the organization.
Transaction processing system (TPS)
If the OAS handles communication and documentation, the transaction processing system handles the financial and operational heartbeat of a business. A firm’s integrated information system starts with its TPS, which receives raw data from internal and external sources and prepares it for storage in a database. Every time a sale is recorded, a payment is processed, stock is updated, or a purchase order is raised, the TPS captures and stores that event.
Transaction processing systems support the operations through which products are designed, marketed, produced, and delivered. In a seed company, for instance, the TPS would record every sale to a retailer, update inventory in real time, trigger restocking orders when stock falls below a threshold, and generate billing entries automatically. These systems automate routine and tedious back-office processes such as accounting, order processing, and financial reporting – reducing clerical expenses and providing basic operational information quickly.
The foundation for higher-level systems
One of the most important functions of the TPS is that it feeds data to other systems higher up the chain. Transaction processing systems are boundary-spanning systems that permit the organization to interact with external environments, and because managers look to TPS data for up-to-the-minute information, it is essential that these systems run without interruption. Without a reliable TPS, there would be no clean, accurate data for managers and executives to analyze.
Decision support system (DSS)
While the TPS records what has happened, the decision support system helps managers figure out what to do next. A DSS is an interactive software-based system intended to help managers in decision-making by accessing large volumes of information generated from various related information systems, including the TPS and OAS. It works with summary information, exceptions, patterns, and trends derived through analytical models.
Critically, a DSS helps in decision-making but does not necessarily give a decision itself. Instead, it gives managers the tools – data models, scenario analysis, forecasting tools – to examine options and weigh consequences. In an agribusiness context, a DSS might be used to evaluate whether to expand cold storage capacity, which crop variety to recommend to contract farmers, or how to respond to a sudden change in fertilizer prices.
DSS in agriculture
The advent of precision agriculture led to the development of several Decision Support Systems that were helping farmers make informed decisions. Examples include Dairy Comp 305 for herd management of milking cows and DSSAT – a tool for land cultivation planning. These agricultural DSS tools pull in data from field sensors, weather records, and market databases to help farm managers optimize inputs and maximize yields.
Executive information system (EIS)
As you move to the top of an organization, the nature of decision-making changes. Top-level executives do not need a granular view of every invoice or daily transaction. They need a broad, strategic picture of where the organization stands and where it is heading. This is the purpose of the executive information system.
An ESS collects data from a DSS and MIS, then presents both internal and external information that decision-makers can use for their strategy. For an agribusiness executive, this might mean a dashboard that shows year-on-year revenue by product line, procurement cost trends compared to market benchmarks, and a forecast of next quarter’s export volume – all in one view, without having to dig through dozens of reports.
Customized for the C-suite
Executive information systems are customized to the needs of top management, offering drill-down capabilities so that an executive can start with a high-level summary and click into more detail when needed. The goal is to give leadership the right information at the right level of abstraction to support long-term planning, investment decisions, and organizational strategy.
Business expert system
The business expert system is a distinct and specialized type of information system. Rather than simply reporting or analyzing data, it actively mimics the reasoning of a human expert. In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert, designed to solve complex problems by reasoning through bodies of knowledge represented mainly as if-then rules.
An expert system has two core components. The knowledge base is where information drawn from human experts is stored, and the inference engine pulls relevant information from the knowledge base to solve a user’s problem using a rules-based approach. Together, these components allow the system to respond to queries in ways that resemble expert judgment.
How the inference engine works
An inference engine applies logical rules to the knowledge base and deduces new information – forward chaining starts with known facts and asserts new conclusions, while backward chaining starts with a goal and works backward to find what facts must be true. In a crop protection expert system, for example, forward chaining might take observed symptoms and infer the probable pest or disease, while backward chaining might start with a target diagnosis and confirm it against field observations.
Applications in agribusiness
Expert systems have been applied in diagnosis – inferring malfunction or disease from observable data – as well as in scheduling, planning, and process control. In practical agribusiness use, an expert system might help a farm manager diagnose crop disease based on symptom inputs, advise a livestock manager on feed formulation, or guide a processor on quality grading decisions. Expert systems also provide permanence – human experts eventually leave their roles, but a knowledge-based system retains that expertise as a permanent repository.
How these systems work together
These five information systems are not isolated – they form a layered ecosystem. The TPS generates the raw transactional data. The OAS keeps communication and documentation flowing. The DSS uses TPS data to support analytical decisions at the middle management level. The EIS synthesizes DSS and MIS outputs for top-level strategic oversight. And the expert system applies encoded domain knowledge to advise on specialized problems at any level of the organization.
Farm Management Information Systems have evolved from simple farm recordkeeping into sophisticated systems to support production management, meeting increased demands to reduce production costs, comply with agricultural standards, and maintain high product quality and safety. This evolution reflects how interconnected these information systems have become in modern agribusiness practice.
What do you think? With so many types of information systems available, how should a small or medium-sized agribusiness decide which ones to prioritize first? And as artificial intelligence continues to advance, do you think traditional expert systems will become more powerful – or be replaced entirely by newer AI tools?
References
- https://www.w3computing.com/systemsanalysis/types-systems/
- https://www.sciencedirect.com/science/article/abs/pii/S0168169900001447
- http://cs.furman.edu/~pbatchelor/mis/IStypes.doc
- https://ischoolonline.berkeley.edu/blog/what-is-information-systems/
- https://courses.lumenlearning.com/suny-osintrobus/chapter/management-information-systems/
- https://www.britannica.com/topic/information-system/Operational-support-and-enterprise-systems
- https://www.tutorialspoint.com/management_information_system/decision_support_system.htm
- https://www.igi-global.com/chapter/introduction-to-agricultural-information-systems/266572
- https://en.wikipedia.org/wiki/Expert_system
- https://www.techtarget.com/searchenterpriseai/definition/expert-system
- https://en.wikipedia.org/wiki/Inference_engine
- https://www.umsl.edu/~joshik/msis480/chapt11.htm
- https://www.sciencedirect.com/article/abs/pii/S0168169915001337
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