In today’s fast moving landscape, manufacturing operations face several challenges. These include maintaining proper stock levels to meet ongoing customer demand yet not to boost inventory, addressing seasonality sales, ensuring adequate personnel and equipment availability, all of which significantly influence manufacturing decisions.
AI technologies such as machine learning and predictive analytics play a crucial role in improving the accuracy of demand forecasting, resulting in increased value generation, heightened customer satisfaction, and sustaining a competitive edge.
In this blog, we dive into the significance of demand forecasting in manufacturing, its advantages, the associated challenges, and strategies for overcoming them.
Demand forecasting in the manufacturing industry
Manufacturers are increasingly adopting real-time data integration to gather and analyze data for more precise forecasting. This trend was highlighted in a recent Gartner report, which found that by 2026, more than 75% of commercial supply chain management applications will incorporate advanced analytics, artificial intelligence and data science.
Sustainability considerations are also becoming integral to demand forecasting as manufacturers aim to align with eco-friendly practices, driving the need for eco-conscious demand predictions. Manufacturers are also looking at regionalizing their operations to boost supply chain resilience and mitigate against the risks associated with complex supply chains.
Despite the ongoing shift in supply chain strategies, there remains a notable gap in the adoption of new technologies for forecasting. McKinsey reports that a significant 73% of enterprises continue to rely on manual or outdated forecasting methods, highlighting the need for further technological advancement and modernization in this critical aspect of business operations.
Some of our customers have been hesitant to shift from traditional forecasting methods to trusting machine learning (ML) systems, citing challenges such as difficulty understanding complex ML algorithms, assigning responsibility and job displacement concerns.
However, ML forecasting has demonstrated its superiority over traditional forecasting methods in several ways. According to Gartner, 76% of organizations have reported enhanced forecasting accuracy with ML techniques. Additionally, a McKinsey report highlighted that ML-based forecasting can substantially reduce errors, up to 50%, leading to improvements in both revenue and profitability.
These findings highlight the potential of ML to outperform traditional methods by leveraging advanced algorithms, data analytics, and pattern recognition to provide more precise and actionable insights for manufacturers.
Benefits of demand forecasting in manufacturing
Demand forecasting offers numerous benefits to manufacturing operations. For example, it plays a vital role in cost reduction by preventing overproduction, high inventory levels and minimizing waste. Accurate forecasts enable efficient resource allocation, ensuring that raw materials, labor, and equipment are utilized effectively, leading to significant cost savings.
Demand forecasting also contributes to revenue growth by aligning production with anticipated demand. It reduces the risk of stockouts, ensuring products are available when customers are ready to purchase. This optimized product availability captures more sales opportunities and maintains high customer satisfaction levels. Additionally, accurate demand forecasting allows manufacturers to plan for peak demand periods, optimizing sales during seasonal trends and holidays.
Another key benefit is the reduction of lead times in the supply chain, facilitated by accurate demand forecasts, offering the agility required to effectively respond to changes in demand or supply chain disruptions.
How demand forecasting is integrated into supply chain management
Demand forecasting is integrated into supply chain management to help businesses predict future customer demand for their products or services. This information enables efficient inventory management, production planning, and distribution, reducing excess inventory costs and stockouts. By aligning supply with anticipated demand, companies can optimize their supply chain operations and better meet customer needs.
Here are three key aspects of integrating demand forecasting into supply chain management:
Demand forecasting guides production planning by providing insights into anticipated customer demand over a specific time frame. With accurate forecasts, manufacturers can align their production schedules with expected demand levels, avoiding overproduction or underproduction.
This helps optimize resource allocation, reduce wastage, and maintain efficient production processes. It ensures that the right amount of products is manufactured at the right time, leading to improved operational efficiency and cost control.
Demand forecasting plays a pivotal role in inventory management by helping companies determine the appropriate inventory levels to maintain. Accurate forecasts enable manufacturers to avoid excessive stockpiling, which ties up capital and storage space, as well as stockouts that lead to missed sales opportunities.
By aligning inventory with anticipated demand, manufacturers can strike a balance between holding enough stock to meet customer requirements and minimizing carrying costs, resulting in improved cash flow and operational efficiency.
Procurement and supplier management
Effective demand forecasting is vital for procurement and supplier management. With insights into future demand patterns, manufacturers can collaborate closely with suppliers to ensure the timely availability of raw materials and components.
This minimizes lead times, reduces the risk of production delays due to shortages, and allows for negotiation of favorable terms with suppliers. Accurate forecasts also enable manufacturers to order materials in appropriate quantities, preventing overstocking and reducing carrying costs.
Challenges of implementing ML for demand forecasting in manufacturing
Demand forecasting in manufacturing, when combined with machine learning, offers significant advantages, but it also presents certain challenges.
Data quality and consistency
For example, collecting sufficient historical data and ensuring its quality can be tricky, particularly for new products or emerging markets. But even well established companies and products often do not have historical data or this data is not consolidated and structured. CThen choosing the right ML algorithm and optimizing its parameters can be complex, while the interpretability of these models may be limited.
Also, the dynamic nature of markets make them unpredictable, with factors such as shifting consumer preferences and economic fluctuations requiring continuous model monitoring and retraining to maintain accuracy.
Strategies to overcome forecast errors and variability
Forecast errors and variability can be challenging to deal with, but there are several strategies manufacturers can implement to mitigate them and improve the accuracy of forecasts:
- Diversify forecasting methods – manufacturers can enhance forecast accuracy by using a variety of techniques, such as time series analysis, regression, and machine learning, resulting in more precise forecasts
- Prioritize data quality – rigorous data validation and cleansing should be a top priority, ensuring a reliable foundation for trustworthy forecasts and confident decision-making
- Foster a culture of continuous improvement – by continuously monitoring and adjusting models based on historical performance and market insights, manufacturers can become more adaptable and agile, effectively responding to changing conditions and minimizing the impact of forecast errors and variability
By collaborating with an external partner who specializes in data science and machine learning, you can address key business-critical areas and processes, as well as gain access to expertise that may be lacking internally.
They can also help you in identifying and hiring the right talent, which is often in high demand and can be difficult to find in the competitive job market with more than one in five manufacturers expecting a significant increase in demand for data skills by 2030. This skills gap includes data science, machine learning, and data engineering expertise, making it challenging to develop and deploy ML models.
Partnering with external experts enables you to bridge the skills gap more effectively and accelerates your journey toward implementing machine learning for demand forecasting, ensuring a smoother and more successful transition to digitized manufacturing operations.
Organizational culture and resistance to change
The path to implementing ML for demand forecasting involves complexities that require strategic expertise. Let’s look at some of these challenges:
Changing any part of your organization can cause confusion and disruption among your employees if not managed correctly. Numerous individuals in your organization will need to contribute to the decision-making process and the design of the demand forecasting process in your transformation project.
Often, businesses overlook this crucial step, focusing on solution implementation and its benefits rather than on facilitating the transition of people from their current state to a desired new state, which can lead to the solution or software failing to deliver the expected benefits or value. The success of change management lies not in the change itself but in how that change is managed.
That's why change management should be an integral part of organizational change. It's vital to involve all stakeholders from the outset to mitigate resistance and ensure project success.
And you should approach the implementation project as a business transformation, not just an IT project and help you break down organizational silos and foster collaboration between departments, leading to a more holistic understanding of your business's needs and objectives, and maximizing the business value it can bring.
Cost and return on investment (ROI)
Implementing ML in demand forecasting can be a substantial investment, including expenses for hiring skilled professionals, acquiring computational resources, and developing ML models. Calculating the ROI may be uncertain, as benefits may not be immediate, and ongoing maintenance costs must be considered in ROI assessments.
A well-qualified external partner will be able to break down the scope of your project into iterations, ensuring you see progress in weeks. Each iteration allows you to evaluate the direction of your transformation project and make adjustments as needed. In this way, you'll be able to see business processes in action and ensure they deliver the planned outcomes. The right partner will also create value as early as possible and incrementally, ensuring effective value management.
Navigate the future of manufacturing with precision forecasting
The future of demand forecasting in manufacturing is exciting, with emerging technologies like the Internet of Things, AI, and big data analytics driving advancements. By accurately predicting future demand, manufacturers can optimize their supply chain operations, reduce costs, boot customer satisfaction and gain a competitive edge.
At Columbus, manufacturing is at the heart of what we do. From engineering and shop floor to distribution on-site and after-sales services, we can help you enhance supply chain management, improve efficiency, streamline operations, reduce overheads and exceed customer expectations.
Our one-day workshop can help your team learn:
- How to improve your forecasting accuracy in the manufacturing industry
- Which technologies should be considered for demand forecasting
- How to get started with Machine Learning driven demand forecasting