Business Information System

Two examples of computer-based reservation systems are:

• Airline reservation system (e.g. Amadeus, Sabre), used to book flight seats.
• Online railway/hotel reservation system (e.g. IRCTC for train tickets, or Booking.com for hotel rooms).

Business process improvement (BPI) is the systematic analysis and gradual redesign of existing business processes to make them more efficient, effective and adaptable. It aims to cut cost, time and errors through incremental changes, without radically replacing the whole process.

A database stores current, detailed, operational data for day-to-day transaction processing (OLTP) and is updated continuously. A data warehouse stores integrated, historical, subject-oriented data from many sources, mostly read-only, and is used for analysis and decision making (OLAP).

Two types of wireless network are:

• Wi-Fi (Wireless LAN), which covers a building or campus.
• Bluetooth (Wireless PAN), which covers short distances between personal devices.

Others include cellular networks and WiMAX.

Two importance of Web 2.0:

• It allows users to create and share content (blogs, wikis, videos), which encourages participation and collaboration.
• It enables social networking and two-way interaction between businesses and customers, which supports marketing, feedback and customer engagement.

One desirable characteristic of a TPS is rapid processing: transactions such as sales or payments must be captured and processed quickly, often in real time, so that business operations are not delayed. Other desirable characteristics are high reliability, accuracy, security and the ability to handle a large volume of data.

Operational supply chain management is the day-to-day management of the flow of goods, information and money across the supply chain, covering activities such as order processing, procurement, production scheduling, inventory control, warehousing and delivery, so that customer orders are fulfilled efficiently.

Business analytics is the use of data, statistical and quantitative methods, and information technology to analyse past and present business performance, find patterns and predict future outcomes, so that managers can make better, fact-based decisions.

A data mart is a small, focused subset of a data warehouse that holds data for a specific department or business function (such as marketing, finance or sales). It is cheaper and faster to build and gives that group quicker access to the data it needs.

An information system (IS) is an organised combination of people, hardware, software, data, networks and procedures that collects, processes, stores and distributes information to support operations, decision making, coordination and control in an organisation.

Operational excellence means running business operations with high efficiency, quality and productivity. Information systems help achieve it in these ways:

• Automation of routine work reduces cost, time and errors. For example, a bank's core banking system processes deposits and withdrawals instantly.
• Better coordination and information flow through ERP or supply chain systems cuts inventory and delays. For example, Walmart's retail link system shares sales data with suppliers so that stock is replenished automatically.
• Real-time monitoring and analysis lets managers detect problems early and improve processes continuously.

Together these improve productivity, lower costs and raise customer satisfaction.

Advantages of the database approach:

• Reduced data redundancy: data is stored once and shared, which saves space.
• Data consistency and integrity: a single copy means that updates are applied everywhere and validation rules are enforced.
• Data sharing: many users and applications can access the same data at once.
• Data independence: programs are separated from the way data is stored, so changes to storage do not affect them.
• Improved security: access can be controlled by user and by privilege.
• Easier backup, recovery and querying through the DBMS.

Three advantages of mobile commerce (m-commerce):

• Ubiquity and convenience: customers can shop, bank or pay from anywhere at any time using a phone, for example eSewa or Khalti payments.
• Personalisation and location-based services: businesses can send offers matched to the user's location and preferences, which makes marketing more targeted.
• Wider reach and lower cost: mobile phones are far more widespread than computers in Nepal, so firms reach more customers cheaply. Quick, easy payments also improve the customer experience.

Social computing helps marketing in the following ways:

• Customer engagement and relationships: social media (Facebook, Instagram, TikTok) lets firms talk directly with customers and build brand loyalty.
• Market research and feedback: comments, reviews and ratings reveal customer needs and opinions at low cost.
• Advertising and promotion: targeted ads and viral content reach many people cheaply, and influencers and user-generated content raise brand awareness.
• Customer service and word of mouth: complaints can be answered quickly in public, and satisfied customers recommend the product to others.

Functional area information systems (FAIS) support the activities of a particular business function. Two types are:

• Accounting and finance information system: it records transactions, manages budgeting, payroll, financial statements, auditing and cash management, and helps control costs and investments.
• Marketing information system: it gathers and analyses data on customers, sales, pricing and promotion. It supports product planning, sales forecasting, market research and customer relationship management.

Others include the human resource and production/operations information systems.

Business analytics usually proceeds in three stages (some texts add a fourth):

• Descriptive analytics: summarises historical data to show what happened, using reports, dashboards and charts such as monthly sales totals.
• Predictive analytics: uses statistical models and data mining to forecast what is likely to happen, for example demand forecasting or customer churn.
• Prescriptive analytics: uses optimisation and simulation to recommend the best action, for example the best price or inventory level.

Some texts add diagnostic analytics, which explains why something happened.

Business process reengineering (BPR) is the radical redesign of business processes to achieve dramatic improvements in cost, quality, speed and service. The main steps are:

1. Set direction and obtain management commitment: define the objectives and the need for change, and form a reengineering team.
2. Identify the processes to be reengineered: select the critical, problem-ridden processes that most affect customers.
3. Analyse and document the existing processes: map the current flow, find delays, redundancy, bottlenecks and non-value-adding steps.
4. Redesign the processes: question every assumption and build a new, simplified process, often using IT to automate, integrate or eliminate steps.
5. Implement the new design: develop systems, train staff, manage resistance and run a pilot before full roll-out.
6. Monitor, evaluate and improve continuously: compare results with targets and make adjustments.

Business analytics converts raw data into insight, which improves decisions in these ways:

• Descriptive analysis shows past performance (sales by region, product or month), so managers see where the business stands.
• Predictive analysis forecasts demand, risk and customer behaviour, so plans are made on evidence rather than guesses.
• Prescriptive analysis recommends the best course of action (optimal price, stock level or route).
• Speed and objectivity: dashboards give real-time data, which reduces bias and uncertainty.
• Better resource use: it identifies profitable customers and products and removes waste.

Example: a supermarket analyses its sales data and finds that certain items sell together and that demand rises before festivals such as Dashain. It then stocks more of those items, places them together and offers combined discounts. Sales rise, and stock-outs and overstocking fall.

Three types of customer relationship management (CRM) systems:

• Operational CRM: automates front-office customer-facing processes such as sales force automation, marketing automation and customer service/call centre support. It records every customer contact and handles orders and complaints.
• Analytical CRM: analyses customer data held in a data warehouse using data mining and OLAP. It finds buying patterns, customer lifetime value and segments, and supports targeted marketing and pricing decisions.
• Collaborative CRM: shares customer information across departments and with partners and suppliers (and through channels such as email, web and phone). This gives a consistent, coordinated service to the customer.

Some texts also list strategic CRM.

Knowledge management (KM) is the set of processes and systems that create, capture, store, share and apply an organisation's knowledge. A knowledge management system (KMS) includes enterprise-wide content and document systems, expert-location tools, collaboration tools, learning management systems and knowledge repositories. The statement that a KMS improves overall organisational performance is justified for the following reasons.

1. Better and faster decision-making Employees and managers find best practices, past cases and expert advice quickly, so decisions are better informed and less repeated trial and error is needed.

2. Improved productivity and lower cost Reusing existing solutions avoids "reinventing the wheel", and time spent searching for information falls. Standard procedures reduce errors and rework.

3. Retention of organisational memory Tacit knowledge in the heads of experienced staff is captured as explicit knowledge. When people retire or leave, the knowledge stays with the firm.

4. Innovation and new product development Sharing ideas across departments encourages creativity and shortens the development cycle.

5. Better customer service Service staff can access a knowledge base of solutions and customer history, giving quicker and more consistent answers and raising customer satisfaction.

6. Faster learning and training New employees learn from stored lessons and expert networks, so they become productive sooner.

7. Collaboration and competitive advantage Communities of practice, intranets and wikis connect people, build a learning culture, and create a hard-to-copy advantage.

Examples

• Infosys / Accenture: use knowledge portals where consultants store project documents and lessons, and search for experts. New projects start faster and quality is more consistent.
• Toyota and Siemens: capture shop-floor and project know-how in repositories, which spreads best practices across plants.
• A bank or telecom (such as Nepal Telecom): a customer-service knowledge base lets any agent resolve a fault or product query quickly, reducing call time and complaints.

Conclusion By turning individual know-how into a shared asset, a KMS speeds up decisions, cuts cost, supports innovation and customer service, and so improves overall organisational performance. Success depends on top management support, a culture of sharing, and incentives for contribution.

(a) Deficiencies in data management and how to make it efficient

Deficiencies

1. Manual data entry: monthly orders are keyed manually into the planning system, and production and inventory data are keyed again into the order system. This causes errors, delay and duplicate effort (nine of 17 days are spent entering and validating data).
2. Data redundancy and inconsistency: sales and production keep separate systems with separate copies of data, and their estimates differ.
3. Lack of integration: order management, production and inventory systems do not talk to each other, so there is no shared view.
4. Outdated, non-real-time data: plans use last month's data and guesses based on past orders, so they cannot adjust to changed or unexpected orders.
5. Slow planning cycle and poor decision support: 17 days, with manual reconciliation in meetings, leads to stock-outs, excess inventory and lost customers.

Making it efficient

• Use a centralised database or a data warehouse linking orders, production and inventory, so every department uses a single version of the data.
• Implement an ERP/supply chain management system (with an advanced planning module) that updates plans automatically when orders change.
• Capture orders electronically (web/EDI) and use validation rules to reduce entry errors.
• Provide analytics and dashboards for demand forecasting, with real-time data.
• Define data ownership and standards, and train staff.

(b) Social computing in customer service

• Create official pages and accounts on Facebook, X, WhatsApp and YouTube where farmers and dealers can place queries, track orders and report problems, with quick replies.
• Build an online community or forum where customers share advice on chemical use, and experts answer, which reduces call-centre load.
• Use customer reviews, ratings and comments for feedback on products and service, and for early detection of demand changes or complaints.
• Use social listening and analytics tools to monitor sentiment, and feed this into forecasting.
• Offer chatbots and a knowledge base/wiki for common questions, and short videos on product use.
• Link social media data to the CRM so that each customer's history is available to service staff. This improves responsiveness, loyalty and lowers service costs, and gives demand signals for production planning.

(c) Advanced IT to minimise order fulfilment time

1. ERP and SCM systems integrating sales, production, inventory and finance in real time, with automatic order-to-delivery workflow.
2. Cloud computing for a shared, scalable platform accessible to all locations.
3. Business analytics and AI/machine learning to forecast demand more accurately and optimise production schedules and inventory.
4. Internet of Things and RFID/barcode for real-time tracking of stock, machinery and shipments.
5. EDI / e-commerce portal and mobile apps so customers place and change orders directly online, avoiding re-keying.
6. Robotic process automation (RPA) to automate repetitive data transfer and validation. With these, planning could move from a monthly manual cycle to a near real-time one, and fulfilment time would fall greatly.

• Information systems improve access to services such as education, health, banking and government (e-governance), making them faster and cheaper.
• They create employment and new businesses (IT, e-commerce, digital payments) and promote social connectivity and communication.

A business process is a set of related, structured activities or tasks, performed in sequence by people and systems, that takes inputs and produces a specific output of value to a customer, such as order fulfilment, hiring or payroll.

• A DSS gives quick, data-based support for semi-structured and unstructured decisions, improving decision quality.
• It lets managers test "what-if" scenarios and alternatives, saving time and reducing risk.

Mobile commerce (m-commerce) is the buying, selling and conducting of business transactions, such as payments, banking and information services, using wireless handheld devices like smartphones and tablets over mobile networks.

Business analytics is important because it turns data into insight, supporting faster and better fact-based decisions, improving efficiency, predicting trends and customer behaviour, and giving the firm a competitive advantage.

Web 2.0 is interactive and user-generated: users can create, share and comment on content (blogs, wikis, social networks), whereas Web 1.0 was static, read-only pages with one-way information flow.

A desirable characteristic of a TPS is high reliability and accuracy: it must process a large volume of routine transactions correctly, quickly and securely, with data integrity and error recovery.

Two functional area information systems are the accounting and finance information system and the human resource information system (marketing and production/operations systems are others).

The Internet of Things (IoT) is a network of physical objects (devices, vehicles, appliances, sensors) embedded with sensors, software and connectivity, which collect and exchange data over the internet, often without human intervention.

Business process reengineering (BPR) is the fundamental rethinking and radical redesign of business processes, often using information technology, to achieve dramatic improvements in cost, quality, service and speed.

1. Transaction processing system (TPS): records and processes routine daily transactions such as sales, billing, payroll and reservations. Importance: it provides accurate operational data, keeps records, and supplies data to other systems such as MIS and DSS.

2. Management information system (MIS): summarises TPS data into periodic, exception and summary reports for middle managers. Importance: it helps managers monitor performance, plan and control, and make structured decisions (for example, monthly sales reports).

(Other types are decision support systems, executive support systems and expert systems.)

Procedure for business process improvement:

1. Select and define the process: choose the process to improve and set objectives and measures (time, cost, quality).
2. Map and analyse the current process: document the steps, find bottlenecks, delays, duplication and non-value-adding activity.
3. Redesign the process: eliminate, simplify, combine or automate steps, and design the improved process.
4. Implement the changes: pilot, train staff and apply the new process.
5. Monitor, measure and continue improving: compare results with targets and repeat the cycle (continuous improvement).

A wireless network is a computer network in which devices communicate through radio waves, microwaves or infrared signals instead of physical cables.

Advantages:

1. Mobility: users can connect from anywhere within the coverage area.
2. Easy and low-cost installation: no cabling is needed, so setup is quick and cheap.
3. Flexibility and scalability: devices can be added or moved easily.
4. Access in difficult locations: it reaches areas where wiring is hard, such as remote or hilly areas, and supports internet access for many devices.

A data warehouse is a central repository of integrated, historical data from many sources, organised for analysis. Its importance in decision-making:

• Single, consistent version of data: data from sales, finance and operations is cleaned and combined, giving reliable information.
• Historical analysis and trends: managers can analyse years of data to spot patterns and forecast.
• Fast queries and reports: OLAP, data mining and dashboards run without slowing operational systems.
• Better strategic decisions: supports market, customer and performance analysis.

Example: A supermarket chain loads sales data from all branches into a warehouse. Analysis shows that certain products sell together and that sales peak before Dashain, so management decides on product placement, promotions and stock levels for each branch.

Social computing is the use of social media, networks, reviews and communities for interaction. Its importance in shopping:

• Social proof and trust: customers read ratings, reviews and recommendations from other buyers before buying, which reduces risk.
• Product discovery and recommendations: friends' shares, influencers and social ads help consumers find products.
• Social commerce: shopping directly on Facebook, Instagram or TikTok shop makes buying convenient.
• Two-way communication and support: shoppers ask questions and get quick replies, and share complaints.
• Deals and group buying: communities share discounts and coupons, which brings lower prices. Sellers in turn gain feedback, wider reach and loyalty at low cost.

ERP (Enterprise Resource Planning) integrates all major functions of a business (finance, HR, production, sales, inventory, procurement) into one system with a common database. Its importance:

• Integration of functions: one shared database removes data silos and duplicate entry.
• Real-time information: managers see current sales, stock and cash positions, so decisions are faster and better.
• Efficiency and cost reduction: standardised, automated processes cut paperwork, delays and inventory carrying costs.
• Better customer service: order status, delivery and billing information is available quickly.
• Planning and control: supports forecasting, budgeting, MRP and performance monitoring across the organisation.

Organizations have many levels (operational, middle, strategic) and many functions (finance, marketing, HR, production), and each has different information needs. No single system can serve all of them, so different types of information systems are used.

• Transaction Processing System (TPS): serves the operational level. It records daily routine transactions such as sales, payroll and orders.
• Management Information System (MIS): serves middle managers. It summarises TPS data into periodic reports for monitoring and control.
• Decision Support System (DSS): helps managers with semi-structured decisions through models and "what-if" analysis.
• Executive Support System (ESS): gives senior management summarised internal and external data, such as trends and dashboards, for strategic decisions.
• Expert systems / Knowledge Work Systems: capture specialist knowledge and support professionals in design and analysis.
• Enterprise systems (ERP, SCM, CRM): integrate processes across functions and with partners.

Different systems are needed because the level of decision, the data detail, the time horizon and the users differ. Using separate but linked systems gives each group the right information at the right time, improves efficiency and gives better coordination and competitive advantage.

Knowledge management (KM) is the systematic process of creating, capturing, storing, sharing and applying the knowledge (both explicit, which is documented, and tacit, which sits in people's heads and experience) of an organization so that it can improve performance and decision-making.

Importance of knowledge management

• Better decision-making: relevant past experience and expertise are available when decisions are made.
• Avoids loss of knowledge: when employees leave or retire, their know-how stays in the organization.
• Avoids duplication of effort: lessons learned and best practices are reused rather than rediscovered.
• Innovation: sharing ideas across departments produces new products and processes.
• Faster learning and training: new employees become productive sooner.
• Competitive advantage: knowledge is a hard-to-copy asset that improves quality, speed and customer service.
• Improved productivity and cost saving: problems are solved faster and mistakes are not repeated.

A supply chain links suppliers, manufacturers, warehouses, distributors, retailers and customers. Information technology (IT) improves it by giving every partner accurate, shared, real-time information.

Ways IT enhances the supply chain

• Demand forecasting and planning: analytics and ERP/SCM software forecast demand, so production and purchasing match it and the "bullwhip effect" is reduced.
• Inventory management: barcodes, RFID and inventory software track stock levels in real time, reducing stockouts and excess stock.
• Procurement: e-procurement and EDI automate purchase orders and invoices with suppliers.
• Logistics and transport: GPS and route-optimisation software cut delivery time and cost.
• Visibility and coordination: shared portals and cloud systems let all partners see orders and shipments.

Example: A retail chain such as Walmart shares point-of-sale data with its suppliers through a supply chain system. When a product sells, the supplier sees it at once and replenishes the store automatically. This keeps shelves stocked, lowers inventory cost and shortens lead time.

Business analytics uses data, statistical methods and models to gain insight and support decisions. It has three main stages, usually extended by a fourth.

1. Descriptive analytics (What happened?) Summarises historical data using reports, dashboards and averages. Application: a retailer reviews last quarter's sales by product and region to see which lines performed best.

2. Predictive analytics (What will happen?) Uses statistical models, regression and machine learning on past data to forecast future outcomes. Application: forecasting demand, customer churn or credit risk, so stock and marketing can be planned.

3. Prescriptive analytics (What should we do?) Uses optimisation and simulation to recommend the best action among alternatives. Application: deciding the best price, the optimum inventory level or the best delivery route.

(Some authors add diagnostic analytics (Why did it happen?) between the first two, which finds causes through drill-down and correlation.)

Role in decision-making: descriptive analytics shows the current position, predictive analytics reduces uncertainty about the future, and prescriptive analytics suggests the action to take. Together they make decisions data-driven rather than based on intuition, which improves accuracy, speed and profitability.

(a) Using information systems for competitive advantage

ABC Retail used information systems in several ways to gain an edge over online stores.

• CBIS: it streamlined operations, improved inventory management and automated customer service, giving lower costs and faster response.
• Strategic alignment: the information system strategy was aligned with organizational goals, so IT investment supported the business plan.
• Strategic information systems: real-time insights into customer behaviour and inventory levels enabled quicker decisions.
• Social computing and Web 2.0: it engaged customers and personalised offers using customer data.
• Mobile app, IoT and analytics: these gave customers a seamless shopping experience, which increased satisfaction and loyalty.

Together these improved efficiency, responsiveness and customer relationships, which competitors found hard to copy.

(b) Impact of BPM and ERP on internal operations

• Process reengineering through BPM: processes were redesigned, so redundant steps were removed and work became faster and more consistent.
• ERP integration: one common database linked sales, inventory, finance and purchasing, and eliminated duplicate data entry and inconsistency between departments.
• Real-time information: managers saw current stock and sales, so decisions were quicker and stockouts and overstock fell.
• Lower cost and higher productivity: automation reduced manual work, errors and processing time.
• Better coordination and control: workflows were standardised and monitoring was easier, and this supported the supply chain system in timely replenishment.

(c) Role of mobile computing and business analytics

Mobile computing (wireless technologies and the app):

• Customers could browse, order and pay anytime and anywhere, which widened the market through m-commerce.
• It gave better customer interaction, with personalised offers and notifications.
• Together with IoT devices in the stores it made shopping seamless, and it supported timely stock replenishment.

Business analytics:

• Descriptive analytics showed trends in sales and customer behaviour.
• Predictive analytics forecast demand, so the right stock was held at the right time.
• It let managers decide on the basis of data about pricing, promotions and inventory, and not on intuition.

The result was better customer experience, faster and more accurate decisions, and higher satisfaction and efficiency.