Mastering Sales ROI in Manufacturing: The Art and Science Behind Sales Forecasting

sales forecasting

Welcome to the second part of our four-part series, Mastering Sales ROI in Manufacturing: A SugarCRM Guide. In this second part, we dive into the Art and Science Behind Sales Forecasting. In the first part of our series, we discussed The Strategic Facets of Lead and Opportunity Management, where we dove into the details of how you can masterfully craft a lead management strategy, how to master opportunity management to drive higher ROI and more. In this part, we will investigate how to approach sales forecasting and which tools will become your most extensive aid. So, buckle up, and let’s get started!

Sales Forecasting: What Is It and Why It Matters for Manufacturing Enterprises?

Manufacturing enterprises face different challenges than traditional retailers. For example, maintaining proper stock levels to meet customer demand or experiencing capacity dilemmas more frequently due to seasonality of sales, available employees, access to adequate equipment, or unexpected customer demands throughout the fiscal year.

Therefore, forecasting as a form of educated guesswork simply doesn’t cut it in manufacturing. A wrong forecast can impact the entire supply chain, severely damaging the organization’s ROI. For example, an overestimated sales forecast can lead to huge discounts on the stocks that result in delayed product launches and enormous expenses in raw materials. An underestimated sales forecast will lead to products that are out of stock for extended periods despite high demand, negatively impacting brand reputation and ROI. So, manufacturers need exact sales forecasts, not educated guesses.

Enter Predictive Analytics for Advanced Forecasting and Ditch the Guesswork

Often, salespeople handle enormous amounts of data when they generate sales forecasts. Predictive analytics helps them effortlessly generate precise predictions from that data and turn them into easy-to-follow insights. Here’s how predictive forecasting helps manufacturing companies:

  • Predict business and market trends
  • Anticipate which is the best way to connect with prospects
  • Set clear goals
  • Establish which products are the best-performing
  • See which products do not perform as well and cut back on production
  • Optimize the available resources

Overall, predictive analytics infused into your sales forecasts give manufacturing enterprises a clear view of their pipeline and eliminate the guesswork of their processes. It takes historical and current data sets and turns them into valuable insights that allow manufacturers to stay one step ahead of the market.
Predictive analytics play a significant role in all business areas, but sales forecasting may be the most important. AI-powered sales forecasting is essential to ensure optimal sales performance, especially in markets sensitive to multiple variables.

Ways to Leverage AI-Powered Analytics for Sales Forecasting

Many companies are intimidated at the sight of “Predictive Analytics”, but we’re here to offer some clarity into the process you must follow when leveraging it for your sales forecasts.

1. Collect and Organize Data

The quality of your forecasts is only as valuable as the data you look into. Data collection is probably done automatically if you’re using a CRM tool. The best way is to categorize the data you want to look into—this can be at a departmental level or at a product level. Identify your data sources (your CRM, sales automation tool, etc.), the relevant variables for your industry or niche, and performance metrics. Then, make sure you start collecting data on your popular products. Make sure you look closely at your sales funnel and identify peaks in your sales funnel, specifics of your repeat customers, and then organize the data you collect.

Once this step is over, choose the period you want to collect your data. Depending on your needs and goals, this can be set weekly, monthly, quarterly, or yearly. However, ensure you do this regularly.

The whole point of sales forecasting is to identify trends. The best tool to use in this case is predictive analytics powered. You don’t need to manually identify patterns. Predictive analytics can look at historical and current data, market trends, and other third-party data sets and generate insights.

You’ll notice peaks and valleys in your forecasts depending on your niche. Predictive analytics can help you identify if internal decisions influenced demand, such as adding a new product to your line.

At SugarCRM, we infused predictive analytics into our Advanced Forecasting Module. Our tool considers different time frames, how your sales team is doing, and the steps in your sales process. This helps you predict future sales and lets you factor in different variables that may influence your sales, such as weather or energy prices.

Advantages of Using Predictive Analytics in Sales Forecasting for Manufacturing Enterprises

Having accurate forecasts comes with a series of benefits. We have a few listed below, to offer you a clearer understanding of why this is mandatory for manufacturing organizations.

1. Optimize Asset Availability

You can confidently optimize your assets now that you have all these insights. Especially in manufacturing, this is crucial. Predictive analytics offers a clear view of your human and non-human resources, their allocation, and utilization. Based on those insights, you can identify bottlenecks by focusing your resources on the areas that need improvement, manufacturing high-demand products, or even expanding your product line.
For example, you can use predictive analytics to predict demand and avoid overstocking resources they don’t need.

2. Increase Business Resilience

Business resilience can be easily attained with accurate forecasting. Because predictive analytics use third-party data sets, your forecasts can predict changes in the economic landscape, rising energy or fuel prices, and even global conflicts that may affect your supply chain and manufacturing capabilities. Being aware of such situations and factoring them into your forecasts can help you overcome the risks that arise from such scenarios.

3. Improve Customer Experience (CX)

Sales forecasting is an art, and higher levels of CX may be one of the best results that emerge from it. Customer’s buying patterns change in response to various factors that predictive analytics can account for when generating forecasts. By adapting your product manufacturing strategy to customer demands you will ensure higher levels of customer satisfaction, as your stock levels will always be appropriate and you can even expand your product lines with products desired by customers.

If you enjoyed this part of our series, stay tuned. The next part will be on Automating Sales Processes for Maximum Efficiency. Until then, check out our CRM Buyer’s Guide for Manufacturing for the lastest on what forward-thinking enterprises are doing now to stay ahead of shifting markets and customer expectations.

Mihaela Chiurtu
Mihaela Chiurtu As a Marketing Content Writer, Mihaela is passionate about branding, content strategies, and customer interactions. When outside the office, Mihaela is a Netflix binge-watcher, skincare geek, and music lover.

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