Building product manufacturers can benefit from analytics to help them determine how best to leverage the industry, how much they can expect to sell, and track the costs of materials. The challenges manufacturers face are similar to other suppliers and can therefore use big data and predictive analytics to help them improve their bottom line and increase specifications.
With the aid of machine learning and artificial intelligence, data can be combined into digestible information that can span years, products, industry trends, and product releases. This data can be applied to existing and future business models, product innovation, and projected sales numbers, to name a few.
Manufacturing analytics
Manufacturing analytics can provide real time contextual awareness that allows companies to optimize costs, improve quality, accelerate innovation, and redefine the customer experience.
For manufacturers to achieve their objectives they need to analyze data from all sources in order to view a full picture. Analytics from such areas as sales, marketing, lead time, supply chain, socioeconomic factors, and digital footprint will provide the greatest value when viewed in conjunction with each-other as opposed to viewing each as a siloed effort.
Page Views
Page views are simply the number of visits a site receives from a given user. But what does that mean for building product manufacturers? Page views give insight into brand awareness and customer buying intent. This data tells a company is how well their marketing campaigns are working, and that coupled with specifications and sales should suggest how well-received the brand is as a whole and how well each product is doing with different demographics.
Ability to Pivot
The pandemic hit every industry, and manufacturers have been facing pressure to keep prices reasonable in the face of a dramatic increase in raw material costs. One way they can address this is through streamlining operations, but such changes effect supply chains and threaten jobs. Data analytics can be used to assess the potential results, and when such data is applied to the business at large, it can be used to minimize the negative effects on the company, its employees, and customers.
Preventative maintenance
One of the difficulties building product manufacturers face is the reliance on physical equipment. No matter how well-maintained machines are, they will, at some point, breakdown, and when they do, it is often unexpected. Such an event can cause reduce production rates or worse, leaving interruptions in the supply chain which reach beyond the manufacturer to customers and contractors. However, with the right data it is possible to predict when parts and components are approaching their death knells and can provide the company with enough time to prepare and arrange for new equipment.
Furthermore, a company can make their schedule around the impending issues—whether that means working at a reduced production plan or lengthening the estimates for product delivery.
Expanding into New Markets
For companies looking to expand into new territories or regions, data and analytics can help predict the best region, products, and timeframe for their move. Current marketing conditions can be analyzed in advance, which gives the company enough notice to add their new ambitions to their roadmap. And when everyone is prepared, there is a better chance for ultimate success. There is an added benefit of increasing the number of employees which also adds more brand recognition when the manufacturer is large enough to make a noticeable impact on the community.
Improve End Product
When data and analytics are applied to manufacturing, a company can create better and more useful end products, because they are armed with the knowledge of what the industry wants and will need in the future. Data-driven product optimization can benefit from machine learning models that will help direct the course of a specific industry, which allows for more innovative products that will become the next great thing. And when a company is in the forefront, it is better poised to make meaningful moves that translate into industry standards and yield more profits.
Conclusion
Building product manufacturing can use data analytics to increase productivity, direct innovation, predict market movement, anticipate equipment failure, and improve customer experience. With the power of real data, a company can implement longer term plans that translate into valuable and effective roadmaps. Furthermore, if a company knows the direction the wind is blowing, it can work proactively to address future industry concerns, which help secure its success, but also offer better options to its customers. There is the added benefit of predicting when certain machines will need repair or replacement, which will inevitably disrupt the production line. But if data can be used to reduce its effects, the supply chain will remain intact, and the company is less likely to be replaced with a competitor’s product. Additionally, if analytics are applied to the cost of raw materials, the company can temper their losses by addressing the increase before it hits.