By: Indu Eswar Shivani Nayuni,department Of Computer Science & Engineering(Data Science), Student Of Computer Science & Engineering(Data Science) , Madanapalle Institute of Technology and Science, Angallu(517325), Andhra Pradesh. indunayuni1607@gmail.com
Abstraction
The disruptive potential of generative AI in sales and profit forecasting is outlined in this paper. By using advanced algorithms to analyze historical and current information, organizations can uncover hidden patterns, detect market changes, and perform what-if analysis with unmatched accuracy. Data collection, preprocessing, model selection, validation, training, deployment, and versioning are the stages of implementation. In spite of the significant advantages that generative AI brings along with it, there are still several challenges to be addressed in this context like biasing of results generated by biased data, scaling issues when attempting to fit a generative network in a limited memory footprint., poor or unavailable quality data etc. Model complexity has always been a bane for such algorithms. Lastly , Integration concerns are due to interdependencies between multiple systems for optimal performance. Nonetheless , Generative AI presents a revolutionary transformation in forecasting capabilities which would facilitate organizations to take better decisions to improve effectiveness and increase profitability.
Keywords: generative AI in sales and profit forecasting, analyze historical and current information, limited memory footprint, a revolutionary transformation in forecasting, better decisions.
Introduction
Accurate sales and profit forecasting has never been more challenging in today’s corporate climate of massive data and fast pace[1]. That’s when generative AI can help a game-changing way of forecasting that employs state-of-the-art machine learning models to generate predictions with levels of insight and precision that were previously unimaginable[2].
A subtype of artificial intelligence, called “generative AI,” can generate new data samples similar to an example dataset. By analyzing large historical and current datasets, generative AI can potentially unveil intricate patterns and signals that may not be captured by more traditional approaches.
There are a lot of possibilities of using generative AI in sales and profit forecasting. From data analysis to identifying and forecasting the demand of markets and coming up with different scenarios, generative AI has all the tools, any business organization that wants to be ahead of its competitors needs. It also makes reporting and even updating possible in real-time, which means that organizations can easily adjust to the given circumstances.
However, to realize the full potential of generative AI it has to go through a systematic process including data acquisition, data preparation, selection of AI model,[1] AI training, verification and deployment of the model. Nevertheless, it is necessary to pay attention to the difficulties like data quality, model complexity, and integration, as well as to address the issues as bias and scalability.
This article focuses on how generative AI can be used for sales and profit forecasting and gives a step by step guide of its use and the pros and cons of its use. The competition of various companies on the modern market environment became a challenge that requires finding the right solutions – and the use of generative AI is designed to provide the most accurate vision of future revenues with minimal risk.
Top of Form
Bottom of Form
Comparison: Newer Demand Planning Procedures and Generative AI
Nevertheless, at the same time, as multiple businesses try to pursue better accuracy and effectiveness in sales and profits forecasting various business companies, it gets crucial to determine the specifics of applying generative AI and comparing it with traditional generative AI techniques of this indicator calculation.[3] This also gives a relative opinion about the weakness and strength of both the approaches and represents how generative AI is slowly emerging as the choice. The following table content shows the comparison on various aspects[4].
Table 1:difference between general and gen ai method in sales and profit prediction
no | Criteria | General method | Generative AI method |
1 | Accuracy and Adaptability | Performs well most of the time when the business environment is stable but is ineffective in unstable conditions | Has high volatility and complex pattern handling capabilities and accuracy that constantly increase with the accumulation of data. |
2 | Measurement Complexity | Generally easier to measure but may need a quantitative change by using preselected values | More difficult to measure; it can takes high level of skills and tools to compose and launch. |
3 | Historical various quantitative data needs | A simple quantitative data input requires historical sales data and other primary statistical figures | Covers enhanced data inputs, which are beyond the limit of simple primordial quantitative data |
4 | Cost and Resource | Less expensive upfront but may be more expensive thanks to mistakes | More expensive at the beginning, because of the need in technology and specialists, but costs will eventually pay off. |
5 | Flexibility and Scalability | Suffers from the necessity of constant updates and work of analysts | Highly scalable and flexible; can easily add new data types and adapt to new conditions of the market |
6 | Techniques Applied | Historical records, Statistical techniques, CPFR, Demand Sensing | Machine learning models, Artificial neural nets, NLP, Generative modeling. |
7 | Strengths | A simple approach based on aggregating current data further increases the accuracy due to cooperation and the use of real-time data | The simple approach based on aggregating current data further increases the accuracy due to cooperation and the use of real-time data |
8 | Weaknesses | inaccurate in specific volatile markets, need historical data to make calculations, difficult to implement | Consume a large amount of computational power, complex in terms of installation and cost, may be expensive. |
The following bar graph shows the details regarding the comparison general method and generative AI
Fig1: Bar Graph- Representing the various aspects between traditional and Gen AI method
Analysis of GEN AI in sales and profit prediction
Due to this, with the use of generative AI, the accuracy of sales and profit forecasting has received multiple merits over traditional approaches. This paper explains how generative AI enhances the advent of the forecast, the rate at which it is performed, and the decisions made, as well as the impact it has on firms[5].
Enhanced Data Handling and Analysis
Comprehensive Data Integration: Concerning the sources, it can function working with such data as the historical sales, the profiles of the customers, social nets, and even macroeconomics. This makes it easier for the analysis since the structure gives a broader picture as compared with the structure using the elements[6].
Advanced Pattern Recognition: AI is most effective when dealing with big and highly—in terms of numbers of variables—structured data since it is capable of finding connections missed by classical methods.
Scalability: It can be stated that AI models are quite data processing focused and as such, quite scalable. They are capable of handling the modern business data in terms of complexity as well as volume and can provide the forecast with better accuracy than the conventional method, besides, the time interval involved in the process is comparatively less. [7]
Superior Accuracy and Predictive Power
High Accuracy: There is improved accuracy due to AD models because it captures interactions of the data that may be non linear. It learns from very large data sets and makesle adjustments form new patterns thus enhancing the predication accuracy.
Real-Time Adaptability: ароднее: Implements with flexibility; processes changes and new data, and can forecast alongside the real time data in the market. This enables the businesses to respond easily to the changes that are stand as permanent fixtures in the market place.
Scenario Simulation: AI can perform various tests so that people will be able to get the overall picture to specific potential occurrences. This assists the businesses to be in a position to predict different situations, and hence, make right decisions[8].
Implementation and Usability
Complex Implementation: It also may be not easy to integrate AI into your platform it may demand the knowledge of machine learning and AI systems. The few activities that are carried out during the setup process include; The decision of the type of model to implement is made, the chosen model is developed and tested before being put into real use[9].
Higher Expertise Requirements: Thus, for generative AI to operate it involves data science and AI modeling Interventions and as such, it would be crucial to hire professional specialists or, if they’re not available, train the existing in-house staff.
Advanced Integration Needs: Organizational usage of artificial intelligence can be a little on the complex side but if integrated the climate is even better in terms of features and functionalities. These transitions are moderate more or less when it is about meaning in data or when it comes to compatibility with stiff structures already in existence. [10]
Automation and Real-Time Capabilities
High Automation: Based on the information, it can be pointed out that AI models are rather self-operated, as well as contain the characteristic of self-updating, as soon as the new data are launched; however, this process is still controlled by man to a significant extent[11]ss.
Real-Time Forecasting: Thus, while comparing the generative AI with other forms of AI, it can be stated that generative AI is capable to predict real time data and also can be updated real-time which is helpful in real time decision-making applications. This assists the businesses to align with the market forces; which is very useful for the various businesses and their competitiveness, in their survival.
Cost and Resource Considerations
Higher Initial Investment: As for the generative AI usage in relation to the above-said idea the following has to be mentioned: their application presupposes the requirement of higher primary investment in technologies, necessary tools, and skilled employees.
Higher Ongoing Costs: While, operating cost is high since models need to be constantly referred and updated. However these costs are countered by more permanent advantages which are improved accuracy and the speed of work.
Conclusion AI as a generative concept for sales and profit in business function is a change that the business applied in the management concept where it integrates it when planning for the future. Comparing generative AI to other conventional methods of making the earnings per share forecasts, generative AI is more beneficial as it is rather expansive, precise, and highly flexible. Hence, to an extent it is correct to describe generative AI as being somewhat of an instrument, the reality is that it is a movement in the sales and profit forecast. The application of the described above approaches is somewhat imprecise, whereas if actually accurate and related models can be achieved by generative AI, the potential for increasing the efficiency of business planning, outlined above, is multiplied many times. Thus, any organization that intends to survive and operate business in the existing and in the emerging global business environment must opt to utilize this technology.
References
- A. Chowdhury, A. Chowdhury, N. Hoque, and M. Moriwam, GENERATIVE AI: A SURVEY OF ITS DEVELOPMENT, TRENDS, AND FUTURE OUTLOOK. 2024. doi: 10.13140/RG.2.2.32224.08960.
- H. Abdi, “Profit-based unit commitment problem: A review of models, methods, challenges, and future directions,” Renew. Sustain. Energy Rev., vol. 138, p. 110504, Mar. 2021, doi: 10.1016/j.rser.2020.110504.
- “Future Internet | Free Full-Text | The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges.” Accessed: Aug. 06, 2024. [Online]. Available: https://www.mdpi.com/1999-5903/15/8/260
- M. Rahaman, F. Tabassum, V. Arya, and R. Bansal, “Secure and sustainable food processing supply chain framework based on Hyperledger Fabric technology,” Cyber Secur. Appl., vol. 2, p. 100045, Jan. 2024, doi: 10.1016/j.csa.2024.100045.
- M. Betaubun, D. E. L. Rokhmah, and J. Budiasto, “Personalized Pathways to Proficiency: Exploring the Synergy of Adaptive Learning and Artificial Intelligence in English Language Learning,” Tech. Romanian J. Appl. Sci. Technol., vol. 17, pp. 60–66, Nov. 2023, doi: 10.47577/technium.v17i.10047.
- S. Mishra and A. R. Tripathi, “AI business model: an integrative business approach,” J. Innov. Entrep., vol. 10, no. 1, p. 18, Jul. 2021, doi: 10.1186/s13731-021-00157-5.
- P. Pappachan, Sreerakuvandana, and M. Rahaman, “Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence,” in Handbook of Research on AI and ML for Intelligent Machines and Systems, IGI Global, 2024, pp. 1–26. doi: 10.4018/978-1-6684-9999-3.ch001.
- M. Moslehpour, A. Khoirul, and P.-K. Lin, “What do Indonesian Facebook Advertisers Want? The Impact of E-Service Quality on E-Loyalty,” in 2018 15th International Conference on Service Systems and Service Management (ICSSSM), Jul. 2018, pp. 1–6. doi: 10.1109/ICSSSM.2018.8465074.
- T. Haksoro, A. S. Aisjah, Sreerakuvandana, M. Rahaman, and T. R. Biyanto, “Enhancing Techno Economic Efficiency of FTC Distillation Using Cloud-Based Stochastic Algorithm,” Int. J. Cloud Appl. Comput. IJCAC, vol. 13, no. 1, pp. 1–16, Jan. 2023, doi: 10.4018/IJCAC.332408.
- A. M. Widodo et al., “Port-to-Port Expedition Security Monitoring System Based on a Geographic Information System,” Int. J. Digit. Strategy Gov. Bus. Transform. IJDSGBT, vol. 13, no. 1, pp. 1–20, Jan. 2024, doi: 10.4018/IJDSGBT.335897.
- J. Bertomeu, Y. Lin, Y. Liu, and Z. Ni, “Capital Market Consequences of Generative AI: Early Evidence from the Ban of ChatGPT in Italy,” SSRN Electron. J., 2023, doi: 10.2139/ssrn.4452670.
Cite As
Nayuni I.E.S. (2024) Gen ai in sales and profit forecasting, Insights2Techinfo, pp.1