Data-Driven Demand Forecasting in Manufacturing

Accurate demand forecasting is essential to a healthy manufacturing operation, helping to improve production planning, inventory management, and resource allocation. Balancing production with demand enables manufacturers to meet the needs of their customers while avoiding overproduction and associated costs. Big data technologies such as predictive analytics and machine learning (ML) are providing manufacturers with the ability to increase the accuracy of their demand forecasts. In this article, we’ll explore the importance of demand forecasting in manufacturing, leading methods used to forecast demand, and how data-driven manufacturers that invest are outpacing their peers.  

How Does Better Demand Forecasting Create Value for Manufacturers? 

More accurate demand forecasts empower manufacturers to generate higher value, improve customer satisfaction, and maintain a competitive edge. 

Managing inventory

Excess inventory must be warehoused, rotated, and insured, with costs adding up quickly. Although these costs are justified when inventory is moving appropriately, carrying excess inventory can tie up valuable operating capital that could be put to use elsewhere. But maintaining sufficient inventory to keep pace with demand is equally important. Failing to anticipate demand creates back orders and stockouts that come with their own costs, such as expedited shipping, lost sales, and unhappy customers. 

Machine learning-enabled forecasting tools (like Snowflake’s Snowpark) can identify inefficiencies in how products are stored, packed, and shipped. With this information, manufacturers can strategically position their products in various geographic regions for faster, more efficient order fulfillment. Machine learning can also find mistakes in orders before they’re shipped, ensuring customers get the correct products.

Budgeting and financing decisions

Better demand forecasts allow decision-makers to answer important questions such as how much to invest in production and when. Demand forecasting algorithms can also assist manufacturers to set optimal price points for each product, using advanced models to forecast demand at differing price points while taking business constraints into account to maximize profitability. 

Knowing when to grow

Pinpointing when to expand is crucial to successful growth. Premature growth ties up capital in underutilized manufacturing capacity, while adding capacity too late leaves customers underserved and creates new opportunities for competition. Decisions related to plant expansion, new product offerings, and investing in additional human capital are all tied to demand. 

Product innovation

Data-driven demand forecasting can guide manufacturers in anticipating emerging market trends. As consumer tastes change, certain products may become less popular, while demand for other products can spike unexpectedly. Using digital feedback loops and insights mined from consumer insight data, manufacturers can innovate the next generation of products. In addition, advanced data analytics tools look at data from social channels, videos, and online review sites to spot emerging trends, providing manufacturers an opportunity to respond quickly. 

Traditional Demand Forecasting Methodologies

There are many different methodologies used to forecast demand. Let’s look at four traditional methods before exploring more advanced approaches.

Sales-driven

Sales-based demand forecasting combines current demand data with sales pipeline data. By analyzing current pipeline data, decision-makers can predict future sales and production needs over a fixed period of time. 

Production-driven

As the name implies, production-driven demand forecasting focuses more heavily on production data. This method uses past production data to predict future production needs.

Pull systems

Demand forecasting based on pull systems uses only data on products actually sold. Pull systems trigger production in response to present demand, rather than focus on demand reliant on potential, future sales. 

Push systems

A push systems approach heavily relies on projected demand data of products with a low chance of unforeseeable demand fluctuations. Production decisions are made using projected demand, not solely on orders in hand.

Advanced Data-Driven Approaches to Demand Forecasting 

Data-driven demand forecasting leverages advanced analytics and machine learning to generate more sophisticated forecasts that take into account additional factors that are likely to influence demand. These tools are capable of capturing real-time fluctuations in purchase behavior, emerging trends on social channels, weather data, inflation data, supply chain information, and more. 

Today’s manufacturers can analyze all sources of in-house data as well as third-party and public data to gain a fuller, more accurate picture of how demand is likely to change over time. And advanced analytics and machine learning enable manufacturers to mine massive amounts of data quickly, ensuring they have the insights they need when they need them. 

Common Roadblocks to Generating Data-Driven Demand Forecasts

While the promise of data-driven forecasting is attractive, many manufacturers fail to experience its benefits because common roadblocks hold them back. Let’s look at each of these challenges and how to overcome them.

Fragmented/incomplete data

Many companies still use on-premises infrastructure or fragmented solutions that can’t keep pace with the processing and storage demands of modern data-driven operations. With data stored in different formats across various systems and applications, realizing the full potential of data remains out of reach. A modern cloud data platform provides manufacturers with the storage and compute power required to consolidate, access, and analyze data without worrying about integration and interoperability problems that hobble data analysis efforts. 

Inability to access third-party data

Demand in today’s world is affected by a myriad of factors. A company’s proprietary data can only provide insights into a few of these factors. Data-driven manufacturers enrich their internal data with third-party data products to improve decision-making and drive innovation. Third-party data marketplaces provide the opportunity to discover and purchase data, data services, and applications from high-quality data and solution providers.

Failure to invest in advanced tools

Manual analysis of the massive amount of data needed for modern demand forecasting isn’t feasible. Manufacturers need advanced analytics capabilities and machine learning technologies to fully benefit from the data available. Data-driven companies are using data platforms with strong partner ecosystems that make it easy to connect analytics and machine learning solutions.

Improve the Accuracy of Your Demand Forecasts With Snowflake

With near-unlimited storage and compute power, Snowflake for Manufacturing enables companies to aggregate large amounts of data in a variety of formats and quickly access and analyze data without integration and interoperability issues. Including more sources of relevant data facilitates superior predictive abilities, resulting in more accurate demand forecasts. Additionally, manufacturers can take advantage of the Snowflake Marketplace to supplement proprietary data with third-party data.