Consumer forecasting is a significant component of managerial economics, which provides a basis for making strategic decisions in different sectors. This process is developed by studying historical data, market trends, and other articulated variables to forecast future demand for a product or service. Correctly forecasting demand is a key managerial tool for smooth production, inventory management, and pricing strategy for increased efficiency and profit margins. In recent times, big data has been the game-changer in the business economy, with evidence-based management principles becoming more easily accessible, idle business resources getting mobilized, and organizations now being able to rapidly identify consumer and industrial trends. Big data involves large and multi-dimensional datasets derived from social networks, online commercial activities, and sensor networks. Big data analytics offers the ability to tackle these masses of data and gain valuable information from them, which can later be turned into useful actions.
This research aims to examine how big data affects managerial economics demand forecasting. Big data analytics improves demand forecasting, resource allocation, and decision-making for those who do this. This research will examine big data’s theoretical foundations and practical applications in demand forecasting to determine its pros and cons. This post will briefly explain demand forecasting and its importance in managerial economics. It will then briefly introduce big data and demonstrate its growing relevance in economic research. However, the research should address how big data can be used for demand forecasting theoretically and practically using case studies. Finally, the publication will discuss the study’s main findings and suggest future research.
Background and Literature Review
Definition and key concepts in demand forecasting
The process of prognosis of the demand is an estimation of future demand for products or services. It is one of the essential parts of managerial economics that helps make the credentials required to make a business decision regarding production, inventory, and pricing. Demand forecasting consists of several major concepts. They include identifying those factors that decide consumer demands, like preferences, incomes, and behaviors. The supply/demand forecasting methods can be grouped into qualitative and quantitative methods. Qualitative methods emerge from practitioners’ experiences and customers’ reactions, and quantitative methods are based on past data and numbers (Aamer et al., 2020). Demand forecasting performs an essential function by letting enterprises see crucial market trends in advance and make responsible choices based on end users’ preferences.
Historical approaches to demand forecasting and their limitations.
Over time, demand forecasting has improved while using new data collection and processing methods. The only forecasting methods in the early 19th century were simple extrapolation methods using moving averages because they needed help to handle complex market dynamics. More complex statistical models were created in the mid-20th century using regression to include variables (Cappa et al., 2023). The negative is that data quality and minuteness still affected the large array of data. Due to real-time analysis, enhanced big data, which included data from multiple sources, made demand forecasting easier in the previous two decades. In this way, more advanced and dynamic forecasting models, such as AI algorithms, can react to changes through machine learning (Aljumah et al., 2021). While new demand forecasting methods have been created, historical methods still face data quality issues and the need to enhance models constantly.
Overview of Big Data
Big data is large, complicated data sets that standard data processing methods cannot process. Big Data comes in many formats, speeds, and volumes. Volume is a broad phrase for the amount of data that can be generated, from terabytes to exabytes. Speed, or velocity, is how fast data is created and processed, often in real-time. “Variety” refers to all structured, unstructured, or semi-structured data types, including text, photos, videos, audio, and sensor data (Cappa et al., 2023). Dual technologies, social media, mobile devices, sensors, and others generate massive amounts of big data. Big data analytics uses all these new technologies to extract information, patterns, and trends from data to help firms make data-driven decisions and gain a competitive edge.
Review of existing literature
Big data and demand forecasting are subjects of growing interest and research. In their study, Aamer et al. (2020) discuss machine learning applications for demand forecasting, which may automate knowledge work and improve supply chain efficiency. The summary of these 79 papers shows that neural networks, artificial neural networks, support vector regression, and support vector machines were widely adopted, with industry-leading the way. Acciarini and colleagues (2023) conclude that huge data can innovate company strategies but also note its drawbacks. The articles in their comprehensive literature review highlight how big data can be leveraged for innovation in several business verticals. As Cappa, Boccardelli, Oriani, and Acciarini (2023) stressed the importance of comprehending big data’s worth, it should be understood that minimal costs yield the most return. The paper discusses how big data is used in the business and public sectors and provides ideas and models for innovation with big data.
Theoretical Framework
Forecasting demand often utilizes mathematical models to predict future demand, drawing on experience and market trend information (Wieland, 2022). Some common theoretical models include:
- Time Series Analysis: This model assumes future demand follows a trend from past observations. Historical data is used to discover seasonal patterns, trends, and outliers to predict future demand.
- Causal Models: These models examine the relationship between demand, pricing, revenue, and advertising content. The statistical models quantify these factors’ effectiveness and forecast demand.
- Econometric Models: Combining economic theory and statistics are used to predict customer demand for goods and services. Most current models estimate future demand using price elasticity of demand, consumer behavior, and market conditions.
Big data analytics sharpen or update these theoretical models by compiling information from multiple sources, which are very powerful. Big data analytics can help in the following ways:
- Improved Data Accuracy: Big data analytics improves accuracy by integrating different data sources, including social media, online profit centers, and sensor sensors, to improve product demand projections. This would provide a more complete view of customer behavior and market trends.
- Real-Time Analysis: Big Data analytics enables businesses to respond to market shifts and demand adjustments quickly. It reduces demand forecasting errors and protects the supply chain from run-outs and overstocks.
- Enhanced Predictive Analytics: This type of big data analytics uses mathematical computations and machine learning to examine previous data and anticipate future events (Aljumah et al., 2021). Data analysis methods like predictive analytics can assist in forecasting demand by identifying patterns and trends that standard models miss.
Big Data in Demand Forecasting
Methodologies of Utilizing Big Data for Demand Forecasting
Utilizing big data for demand forecasting involves several key methodologies:
- Data Collection: Big data collection is becoming easier, with sources like transaction records, social media platforms, and Internet of Things devices. Representing major sources of consumer behavior, trends, market, and demand variables.
- Data Integration: Integration of Data Analysis begins with connecting data sources to generate a consistent dataset. To reduce errors and inaccuracies, data must be cleaned and organized.
- Data Analysis: Machine learning algorithms and statistics models transform huge amounts of data into actionable insights (Kushwaha et al., 2021). These methods help you analyze data patterns, trends, and linkages, which can be used to predict business demand or operations.
Case Studies of Big Data in Demand Forecasting
Top retailers use data mining to estimate demand. The store increased supply chain management by including real-time sales data, social media customer feedback, and external elements like weather patterns in their projections. With higher customer satisfaction, stockouts became almost a zero-sum game, and inventory turnover was maximized. A power provider also employed big data from smart meters and weather forecasting algorithms. It can now optimize electricity demand. This improved energy management by increasing output and distribution, saving money, and improving efficiency. Telecom requires network service load forecast based on network traffic, phone usage, and customer service interactions. The system was built to use network resources properly, provide top-notch services, and increase customer happiness (Kushwaha et al., 2021). These case studies show how big data reduces demand forecasting, operation efficiency, and other inaccuracies and its extensive use.
Tools and Technologies Facilitating Big Data Analysis for Demand Forecasting
That includes big data and a demand forecasting tool that uses several technical technologies. Hadoop and cloud-based systems are effective data storage and management options for massive data volumes. These technologies let firms process and save lots of data quickly and cheaply. Data mining and predictive modeling use R, Python, and Apache Spark. Such technologies offer several statistical and machine learning algorithms to disclose essential facts on large datasets. Visualization tools like Tableau and Power BI are crucial for big data research (Aamer et al., 2020). This equipment allows organizations to create realistic displays and locate demand trends and patterns, making it easier to communicate research results to interested parties. These tools and technologies are essential for companies that want accurate and useful demand forecasting.
Enhancement in Forecasting Accuracy through Big Data
The much-improved accuracy in big data has made foreseeing much more efficient. Among the many case studies that have featured the development of this technology, companies have recorded a precipitous fall in forecasting mistakes and have been able to manage their inventory effectively. As an illustration, a retailer experienced a 20% increase in availability in the demand forecasting models using real-time sales data and instant client complaints from social media networks. Another case was an energy company that succeeded in raising its demand forecasting by 15% by implementing the systems from smart meters and weather forecasting models (Cappa et al., 2023). Another important feature worth mentioning in the context of big data is that it helps develop a well-rounded view of demand by allowing a deeper and more thorough analysis. Businesses can make more changeable and pinpointed forecasts by calculating and analyzing all the influencing factors, from consumer behavior to market trends to weather expectations. This helps them to manage their processes smoothly and respond quickly to market changes, thus boosting customer satisfaction and operation efficiency.
Benefits and Challenges
Big data in demand forecasting has many benefits. First, it improves forecasting accuracy by incorporating different data sources so firms can make data-driven decisions. Big data enables real-time analytics, allowing companies to quickly handle important issues like demand or market changes. Big data lets companies predict demand for smaller segments based on local patterns or macroeconomic conditions. Stumbling impediments and barriers allow this. Security, privacy, and dataset handling and analysis are challenging challenges. Big data can also be difficult to access and expensive to apply in existing models or invest in model technology or infrastructure (Acciarini et al., 2023). Despite the hurdles above, big data in demand forecasting has more benefits than drawbacks. Thus, organizations seeking demand-headmost forecasting employ it.
Real World Applications
Big data-based demand forecasting has changed several domains of enterprise market prediction and reaction. Big data analytics help merchants forecast consumer demand based on seasons, customer preferences, promotional activity, etc. It has improved stock control, preventing too much and too little product, increasing customer satisfaction and sales. Big data can predict patient intake into hospitals and clinics, improving resource allocation and staff scheduling. The efficient distribution of medical resources for patients’ demands has reduced wait times and enhanced healthcare operations.
Big data forecasts product demand in manufacturing, enabling effective production planning and supply chain management. There is less waste, lower production costs, and better production-market synchronization. Big data helps e-commerce platforms predict purchase trends, improve inventory, and create targeted marketing campaigns. This was done through tailored product suggestions, dynamic pricing strategy execution, and improved product availability and delivery times. In any financial services arena, big data predicts product and service demand, enabling better financial planning and risk management. The better product offers to optimize asset allocation and customer service through individualized financial solutions (Kushwaha et al., 2021). Big data-driven demand forecasting has altered key decision-making processes across many sectors, helping firms make better informed, strategic decisions.
Conclusion
Big data integration in managerial economics demand forecasting has benefited several sectors. Big data analytics improves forecasting, inventory management, and decision-making. Machine learning algorithms and advanced analytical tools allow firms to gain actionable insights from massive data, resulting in better strategic decisions and operational efficiency. Addressing privacy concerns due to data security issues or the limitations of collecting and analyzing such large amounts remains problematic. In the future, researchers can build sophisticated predictive models/analysis tools, explore blockchain and edge computing for demand forecasting, and analyze how big data affects Managerial Economics topics like pricing and market segmentation. Big data in demand analysis and forecasting has huge potential for change, so Managerial Economics is expected to continue integrating it to boost innovation and business performance.
References
Aamer, A., Eka Yani, L., & Alan Priyatna, I. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An International Journal, 14(1), 1-13.http://doi.org/10.31387/oscm0440281
Acciarini, C., Cappa, F., Boccardelli, P., & Oriani, R. (2023). How can organizations leverage big data to innovate their business models? A systematic literature review. Technovation, 123, 102713. https://doi.org/10.1016/j.technovation.2023.102713
Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Organizational performance and capabilities to analyze big data: Do the ambidexterity and business value of big data analytics matter? Business Process Management Journal, 27(4), 1088-1107. https://doi.org/10.1108/BPMJ-07-2020-0335
Cappa, F., Boccardelli, P., Oriani, R., & Acciarini, C. (2023). How Can Organizations Leverage Value from Big Data? A Systematic Literature Review. Technovation, 123, 1-18.https://iris.luiss.it/handle/11385/232178
Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Big data applications in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017.https://doi.org/10.1016/j.jjimei.2021.100017
Wieland, J. (2022). Relational economics: Theoretical framework and managerial implications—a short introduction. In The relational view of economics: A new research agenda for studying relational transactions (pp. 17–41). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-86526-9_3
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