Retail and manufacturing companies are facing challenges as they navigate a market characterized by volatile demand, supply chain adaptations, and fluctuating costs. The good news is that predictive forecasting with AI can transform planning processes, improve forecast accuracy, and enhance profitability. 

A recent poll of participants in a webinar with CohnReznick and Prevedere, a provider of external planning analytics software , revealed only 26.7% of them are using external economic data in their planning and forecasting. 

By utilizing reliable data and advanced forecasting methods, businesses can better foresee market trends and optimize their operations. In this article, we outline the benefits of predictive forecasting and business processes that can help you best leverage Prevedere’s forecasting tool. 

Improved forecasting accuracy

Implementing predictive analytics tools that leverage AI can improve forecasting accuracy by enabling you to gain: 

• Operational efficiency: AI optimizes production processes, including analyzing data from automated machinery equipped with IoT sensors. Feeding this data into your analytic models enables you to predict when to shift production lines based on supply chain constraints and align manufacturing processes with available materials and production capabilities.
• Predictive maintenance and quality control: AI can predict maintenance needs and identify potential quality issues before they become significant problems. Leveraging this data helps to minimize downtime in production, better utilize your workforce, and streamlines these critical processes. 
• Inventory management: By predicting demand more precisely, companies can avoid overstocking or stockouts, ensuring they have the right amount of inventory at the right time.

Cost optimization

With so many pressures on today’s global economy, it becomes even more critical to leverage predictive forecasting for proactive cost management, to maintain competitiveness, and to create shareholder value. Critical data inputs and usage include:

• Historical performance: Incorporate historical performance data to understand trends and predict future outcomes. Consider upcoming changes, such as opening new stores, to adjust your forecasts accordingly.
• External insights: Look outside your company to understand industry and category trends. Gather data from various external sources to guide your forecasts and make informed decisions. 
• Econometric modeling: Utilize econometric modeling, which combines AI and regression modeling. Some solution providers use external global data from thousands of sources to create explainable forecasts that provide an economic narrative.
• Data democratization:  Data democratization is the notion, through tools like Prevedere, that data doesn't have to be locked away behind the "paywall" of IT, rather modern data platforms enable everyday business users access to data, insights and information in a much more user-friendly manner than in the past. FP&A teams, as well as business analysts, can utilize data to inquire, derive, and test cost models themselves, in real-time, without having the burden of IT slowing down the speed of action or insight, allowing for faster reactions to market movements and changes.

Business organization and processes to adapt

Being able to realign your people, processes, and technology to support current and future market opportunities is critical. Some effective strategies include: 

• People and processes: Ensure you have the right people to interpret and utilize data. Training staff in low-code/no-code tools enables them to perform data analysis without needing extensive programming skills.
• Holistic data management: Shift data and analytics responsibilities to operational divisions, finance, and strategy teams. Empower business units with the information they need to create value on the shop floor, marketing, and other departments.
• Modern analytics platforms: Cloud-based analytic solutions can help reduce operational complexity and increase agility in digesting and analyzing data. Utilize low-code/no-code tools to enable business users to create and test models without extensive programming knowledge.
• Enhanced decision-making: Enable business users to write models, test hypotheses, and iterate quickly. Provide tools that facilitate real-time data access and analysis for better decision-making.

Know the KPIs

Knowing what you want to achieve will help you focus on deriving the most valuable insights from your data. You need to identify your objectives in order to develop KPIs and measure your success. While the specifics can vary by industry subsector, here are some performance indicators to consider:

• Customer demand and retention: Monitor customer acquisition costs and retention rates, track buying volumes, discounts given, and overall customer loyalty to gauge demand and retention effectiveness.
• Inventory levels and turnover: Assess current inventory levels and turnover rates. Based on forecasted demand, determine if you have the right inventory and avoid overstocking or understocking to optimize working capital.
• Cash flow and profitability: Measure cash flow and profitability with a detailed approach. Consider total cost allocations, including SG&A, landed, shipping, and transportation costs, to understand true profitability by product or SKU.
• Consumer debt and spending: Analyze how consumer debt, such as mortgage rates, influences spending and consumption rates. Monitor consumer sentiment data from the Federal Reserve and other organizations to anticipate market trends.

Improving your forecasting: Key questions to ask

When evaluating your planning and forecasting processes, it's crucial to assess various aspects that can significantly impact your business outcomes. Consider asking the following questions to identify areas for improvement and potential transformation:

• Is there room for improvement in our planning and forecasting processes? Assess whether your current methods are efficient, if they need some revisions, or if you should consider a more transformative update to enhance accuracy, better insights, and gain greater impact.
• What does it cost us to miss a forecast? Understanding the financial implications of missed forecasts can highlight the importance of precision and the need for robust forecasting systems.
• Are we incorporating external data into our projections? Including external data alongside historical data, can provide a more comprehensive view and improve the accuracy of forecasts by considering broader market and economic conditions.
• Do we know our leading drivers and how they impact our business? Recognizing the primary factors that influence your business outcomes is essential for making informed decisions and creating reliable forecasts.
• Are we challenging our hypotheses and exploring all options? Regularly questioning assumptions and considering alternative scenarios can lead to more resilient and adaptable forecasting models.

Leveraging AI-enabled predictive forecasting solutions and analytic tools can transform your business, allowing you to effectively address challenges and capitalize on market opportunities. By integrating external data and advanced analytics, companies can achieve greater accuracy in their forecasts, leading to improved efficiency and profitability. Embracing these technologies is essential for staying competitive and driving long-term growth.