There are many different ways to use big data and analytics in manufacturing. One of the most popular is using these tools for forecasting demand levels. The idea behind this approach is that by predicting demand, you can produce more items ahead of time so they’re ready when your customers want them. This reduces inventory costs, increases customer satisfaction, and helps a company get a competitive edge over its competitors. But how does it work? What’s the benefit? And why do manufacturers find this so helpful? Let’s explore!
What Exactly is Big Data and Analytics
First, a little background on big data and analytics. What is it? Basically, it’s a way to make sense of all the information you have, everything from emails and social media posts to business documents and machine logs.
The goal is to identify patterns in your company’s history so you can make more informed decisions that will affect the future. This helps you understand your customers better and can help you plan for the future. Now, let’s go into more detail about how big data and advanced analytics in manufacturing can be used to improve manufacturing efficiencies. One of the best ways is to use it with predictive modeling. Basically, this type of system collects huge amounts of information so they can use statistical analysis and machine learning to identify patterns and trends. This is then used to predict future items such as demand, inventory levels, and production. Manufacturers can then deliver products ahead of this demand so their customers don’t wait for them!
Forecasting Demand Levels with Big Data
There are two approaches to using big data in manufacturing forecasting. The first way is through demand-based forecasting, which uses historical sales information combined with real-time data about factors that influence the demand of a product line. The analysis is done to predict how many units you can sell six months or a year from now.
The second way is through supply-based forecasting, which uses real-time information about your current inventory, production capacity, and workforce to predict how many units you’ll be able to produce in a certain time period. The analysis is done to determine the best level of production for a given timeframe.
Some companies can use both approaches, but it’s usually best to focus on just one at a time. The key here is to have the right data available that can be used in forecasting analysis. This could include things like machine-generated production information, sales history by location, demographic information about your customers, daily weather patterns, and so on.
What Is The “Right Data”?
Let’s take a step back and define what the “right data” really means. It’s essentially a collection of information about your process that has been organized in a way to make it easy for you to pull reports and charts when necessary.
This might include things like purchase order numbers, inventory serial or lot numbers, assembly codes, shipping dates, etc. If you have this data on hand then you can use it to create reports that offer insight into why some products sell more than others, discover the best selling items in your product line, and find out what inventory levels are ideal for maximizing production efficiency.
When utilizing the two manufacturing forecasting methods described above, this data can be used to identify what combination of inventory, production capacity, and workforce will give you the best chance of achieving a goal. The great thing about big data and analytics is that it gives manufacturers a way to improve their process, from idea through delivery. The more information you have, the more comfortable you’ll be making crucial decisions that affect your success.
Production Efficiency Benefits
Now that you understand how these tools work, let’s look at some of the benefits they can offer manufacturers.
- Increased production efficiency! Since you have more advanced notice of when demand is going to happen, you can manufacture your goods ahead of time so they’re ready for customers. This also reduces the amount of stock on hand which will allow you to save on inventory costs.
- More opportunities to increase customer satisfaction! By knowing that you’ll have a product ready for them when they want it, your customers feel valued and appreciated.
- Higher revenue! With more products ready to go, you’ll likely see an increase in sales as soon as you start offering additional inventory.
What to Keep In Mind
It’s important to keep your big data tools updated as new information becomes available to help you make better informed decisions. This means monitoring, processing, and analyzing the information so you can use it to predict future sales patterns.
If your company is using different channels of distribution (online vs brick-and-mortar retail, etc.) then these approaches will be used for different purposes. Demand-based forecasting is ideal for online sales where you have access to real-time data about your customers. Supply-based forecasting is ideal for retail outlets because you only know what inventory levels are when a product has been sold, not ahead of time.
Don’t forget to check out other areas that might also benefit from better analytics and big data! This could include things like customer service use, where you can track how many support requests are generated for certain products. This way you can quantify how your customers feel about the product and information on which they’d like to see improvements made.
Analytics, Big Data And Marketing
Another great example is marketing. If you have a better understanding of which products are popular, you can start promoting them more aggressively on social media and in your email marketing campaigns. This will allow you to generate higher product awareness and interest for new customers who might not be looking specifically at your product offerings right now.
Inventory costs are something you’ll want to keep in mind, too. Look into how you can improve your inventory turns by getting better insights into which products are selling often and in what quantities. With this knowledge, you can reduce your stock on hand and reallocate it to products that are selling.
There is a lot of data out there waiting for you to collect and process! If you have the right tools in place then you’ll be able to start making informed decisions about how best to increase production efficiency, improve customer satisfaction, generate more revenue, reduce operational costs and improve efficiencies within your company!