Last updated on Aug 21, 2024

What are some emerging trends and innovations in demand forecasting?

Demand forecasting is the process of estimating future customer demand for products or services based on historical data, market trends, and other factors. It helps businesses optimize their inventory, production, pricing, and marketing strategies, as well as reduce costs and risks. However, demand forecasting is not a static or simple task, as it involves dealing with uncertainty, complexity, and volatility. Therefore, businesses need to constantly explore new trends and innovations in demand forecasting that can enhance their accuracy, efficiency, and agility. In this article, we will discuss some of the emerging trends and innovations in demand forecasting that are shaping the future of this essential skill.

1 Artificial intelligence and machine learning

One of the most prominent trends in demand forecasting is the use of artificial intelligence (AI) and machine learning (ML) to automate and improve the forecasting process. AI and ML are technologies that enable computers to learn from data and perform tasks that normally require human intelligence, such as pattern recognition, anomaly detection, and optimization. By applying AI and ML to demand forecasting, businesses can leverage large and diverse data sources, such as sales history, customer behavior, social media, weather, and events, to generate more accurate and granular forecasts. AI and ML can also help businesses adjust their forecasts in real time, based on changing conditions and feedback loops, and provide insights and recommendations for decision making.

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Emily Thomson

Senior Marketing Executive | Digital Marketing BA(hons)
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At Predyktable, we use AI and ML to automate and enhance our forecasting processes. By analysing diverse data sources like sales history, customer behaviour, social media, weather, and events, we generate highly accurate and granular forecasts. Our AI models continuously learn and adjust in real-time, providing actionable insights and recommendations for optimal decision-making.

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Andie Soetaman

Business Development @RELEX | Driving Supply Chain Transformation across Retail & CPG â™»ï¸ | Customers using Relex: Coles Group, 7-Eleven, Sephora, Tegel Foods, Snack Brands, Officeworks, Vita Coco, Doordash and more! ðŸš€
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AI-driven Demand Forecasting is exciting! ðŸš€ Check out RELEX and our solutions using AI & ML. We covered end-to-end from raw materials to consumer products. Connect with me on LinkedIn to find out more on how we can help your businesses too! ðŸ˜Š

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Vishnu Vardhan M

SAP S/4HANA & ECC || PP/MM/QM Consultant || PMPÂ® || MRP Planning || Helping Manufacturing/Process Industries with SAP S/4HANA Implementation
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AI and machine learning are revolutionizing demand forecasting by analyzing vast amounts of data to identify patterns and trends. For example, AI-driven tools can predict future demand with high accuracy by considering factors like seasonality, market trends, and historical data. Companies like Amazon use these technologies to optimize inventory levels and reduce stockouts.

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2 Cloud-based and collaborative platforms

Another trend in demand forecasting is the shift from traditional, siloed, and rigid systems to cloud-based and collaborative platforms that enable more flexibility, scalability, and integration. Cloud-based platforms are software applications that run on the internet, rather than on local servers or devices, and allow users to access them from anywhere and any device. Collaborative platforms are software applications that facilitate communication, coordination, and cooperation among multiple users, teams, or organizations. By using cloud-based and collaborative platforms for demand forecasting, businesses can benefit from lower costs, higher performance, and easier maintenance. They can also share and synchronize data, models, and forecasts across different functions and stakeholders, such as sales, marketing, operations, finance, and suppliers, and foster a more aligned and responsive demand planning process.

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Vishnu Vardhan M

SAP S/4HANA & ECC || PP/MM/QM Consultant || PMPÂ® || MRP Planning || Helping Manufacturing/Process Industries with SAP S/4HANA Implementation
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Cloud-based platforms facilitate real-time data sharing and collaboration across teams and departments. Tools such as SAP Integrated Business Planning (IBP) allow for seamless integration of data from various sources, enabling better coordination and more accurate forecasts. This collaborative approach ensures that all stakeholders have access to the latest information, improving forecasting accuracy and responsiveness.

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3 Scenario planning and simulation

A third trend in demand forecasting is the adoption of scenario planning and simulation techniques to cope with uncertainty and volatility. Scenario planning is a method of exploring and evaluating different possible futures based on various assumptions, drivers, and events. Simulation is a method of creating and testing virtual models of systems or processes to analyze their behavior and outcomes. By using scenario planning and simulation for demand forecasting, businesses can anticipate and prepare for various scenarios, such as demand shocks, disruptions, or opportunities, and assess their impact on their performance and profitability. They can also compare and contrast different strategies and actions, and identify the best course of action for each scenario.

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Vishnu Vardhan M

SAP S/4HANA & ECC || PP/MM/QM Consultant || PMPÂ® || MRP Planning || Helping Manufacturing/Process Industries with SAP S/4HANA Implementation
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Scenario planning and simulation involve creating various "what-if" scenarios to understand potential impacts on demand. For instance, a company might simulate the effects of a new product launch or a supply chain disruption on inventory levels. This helps businesses prepare for uncertainties and make informed decisions to mitigate risks and capitalize on opportunities.

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4 Prescriptive analytics and optimization

A fourth trend in demand forecasting is the integration of prescriptive analytics and optimization tools to generate actionable and optimal solutions. Prescriptive analytics is a branch of analytics that uses data, models, and algorithms to suggest the best course of action for a given situation or objective. Optimization is a branch of mathematics that uses mathematical techniques to find the best solution among a set of feasible alternatives, subject to certain constraints or criteria. By using prescriptive analytics and optimization for demand forecasting, businesses can not only predict what will happen, but also prescribe what to do about it. They can also optimize their decisions and actions, such as inventory allocation, pricing, promotion, or replenishment, to maximize their revenue, profit, or customer satisfaction.

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5 Ethical and responsible forecasting

A fifth trend in demand forecasting is the recognition of the ethical and responsible implications of forecasting practices and outcomes. Ethical and responsible forecasting is the application of ethical principles and values to the forecasting process and its consequences, such as fairness, transparency, accountability, and sustainability. By adopting ethical and responsible forecasting, businesses can ensure that their forecasts are not biased, misleading, or harmful to their customers, employees, partners, or society. They can also enhance their reputation, trust, and loyalty among their stakeholders, and contribute to social and environmental goals.

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6 Hereâ€™s what else to consider

This is a space to share examples, stories, or insights that donâ€™t fit into any of the previous sections. What else would you like to add?

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Directory

What are some emerging trends and innovations in demand forecasting?

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1 Artificial intelligence and machine learning

2 Cloud-based and collaborative platforms

3 Scenario planning and simulation

4 Prescriptive analytics and optimization

5 Ethical and responsible forecasting

6 Hereâ€™s what else to consider

Demand Forecasting

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