Boosting Manufacturing Profits: A Smart Prediction Guide

Harnessing Machine Learning for Business Insight

Whether you’re a seasoned business owner or are just getting started with your operation, you likely spend a lot of your time trying to predict the future. This can be a seat-of-the-pants endeavor (e.g. “our demand always surges in the spring”), or perhaps you rely on carefully constructed spreadsheets where you use custom predictive formulas that yield data to help drive your decision making. With the integration of machine learning, you can address – among other things – scheduling supply orders, staffing needs, or even help decide whether a loan to expand your operation is a good idea.

If you’re relying on spreadsheets, or even a more task specific analytics and visualization tool like Tableau or Power BI, you’ve probably noticed that your systems need nearly constant attention as your business landscape evolves. As your business processes become more and more automated, you’re gathering more and more data, and it can be a daunting task to continually update your systems to consume this volume of data. And if you’re hand writing your formulas, you’re likely relying on your understanding of your own business coupled with a fair amount of intuition as you hunt for the key metrics and trends in the sea of data that you have accumulated.

Your analytics system is a thing of beauty, you’ve spent hundreds of hours (or more!) on it, yet you still wonder if you’ve missed a key trend buried in that data that will unlock the next level of efficiency and profit for your business. How can you possibly find that key trend? You’ve got dozens (or hundreds) of data points, incomplete and perhaps even inaccurate data. You can make educated guesses, but each new idea means another deep dive into your spreadsheet, tweaking the formulas to see if there’s anything that will bear fruit.

This is where machine learning can be a huge help. Machine learning is a branch of artificial intelligence (AI) that focuses on building systems that learn or improve from experience. Instead of being explicitly programmed, machine learning algorithms can identify patterns, and utilize insights to make accurate predictions or decisions on their own. This technology is used across various industries to automate analytical model building, making computer systems more intelligent, adaptable, and efficient. Recent developments in machine learning have made these types of predictive systems faster, more efficient, and accessible to those that want to leverage the power that these systems offer.

In this post, we’ll explore how machine learning can be an effective analytics tool while exploring its use in a manufacturing scenario. If manufacturing isn’t your industry, don’t worry, these techniques are equally useful in all sorts of situations; it’s just easier to talk about a specific industry rather than speak in generalities.

Machine Learning and Manufacturing

When integrated into our manufacturing processes analytics, machine learning can help drive process improvement. By analyzing historical data, machine learning can predict and prevent machinery failures, leading to improved maintenance planning and lower repair costs. It also supports quality control efforts by identifying actions and processes that ensure consistency. Furthermore, machine learning aids in supply chain optimization; by forecasting market demand, it helps in planning inventory management, preventing over or underproduction. In short, machine learning provides valuable insights which can be leveraged for more informed decision-making, leading to enhanced profitability and a significantly more agile and responsive manufacturing process.

Predictive Algorithms

Predictive algorithms are a key component of machine learning. A traditional predictive algorithm uses historical data to process current scenarios and predict future outcomes. In machine learning platforms, predictive algorithms usually follow a well-defined design process beginning with data collection, choosing an appropriate model, and then training that model using historical data. The model learns from this data, identifying patterns and making connections. Once trained, the model is tested and validated before we start using it to making predictions from new data. Depending on the results, the model may need fine-tuning or additional training. This iterative process ensures the accuracy and reliability of the predictive algorithm.

Creating a predictive algorithm follows a systematic step-by-step process:

1. Define the Problem: The initial step involves a clear understanding and definition of the problem you are trying to solve. This helps in determining the kind of data needed and the type of predictive model that can provide the desired outcomes.
2. Gather and Preprocess Data: Data relevant to the problem is collected from various sources. It could involve major quantities of data which are then preprocessed. Preprocessing involves cleaning the data by handling missing values, removing duplicates, and outliers.
3. Choose a Suitable Model: The next step involves selecting a suitable model based on the problem definition and the nature of the data. The choice of model generally depends on whether the problem involves classification, regression, or clustering.
4. Train the Model on Data: The chosen model is then trained by feeding in the preprocessed data. The model learns from this input, identifying patterns and establishing relationships in the data.
5. Test and Evaluate the Model: Post-training, the model is tested on a separate set of data (data specifically not used in training) to evaluate its performance. Different statistical methods can be used to measure quality of the model.
6. Deploy the Model: Once the model proves satisfactory in testing and evaluations, it is deployed in a real-world scenario to solve the problem it was trained for. It is monitored regularly to ensure it maintains its accuracy and is updated or retrained as necessary.

Machine Learning Algorithms Solving Real World Problems

Predictive algorithms are revolutionizing the manufacturing industry in various aspects leading to substantial profits.

1. Preventive Maintenance: Companies like General Electric use predictive algorithms to anticipate machinery failures in factories. By analyzing real-time data from machines, these algorithms predict potential issues and schedule maintenance, reducing downtime and saving on repair cost. [1]
2. Quality Assurance: Amazon Prime Video uses machine learning to ensure the quality of the content on its streaming platform. [2]
3. Supply Chain Optimization: Coca Cola, for example, uses predictive algorithms to identify trends in customer demand, helping them to streamline their production and distribution processes. By ensuring they have the right amount of product ready at the right time, they minimize waste and maximize sales. [3]
4. Energy Efficiency: Siemens has effectively used predictive algorithms in their gas turbine manufacturing process. Their self-learning models predict the energy loads, resulting in efficient energy consumption, reducing costs, and contributing to sustainability. [4]

Challenges

Implementing a machine learning predictive algorithm, while rewarding, can present several challenges:

1. Data quality and availability: Predictive algorithms rely heavily on the quality and quantity of data. Lack of relevant, comprehensive data or poor quality data can affect the model’s performance. Cleaning and preprocessing data to make it suitable can also be time-consuming.
2. Skill gap: Implementing machine learning algorithms requires a particular set of advanced skills. Lack of personnel with expertise in data science, statistics, and machine learning can hinder the process.
3. Choosing the right model: Not every machine learning model suits every problem. Selecting the appropriate model to tackle the specific problem at hand requires comprehensive understanding and experience.
4. Overfitting and underfitting: Overfitting occurs when the model adapts too well to the training data, performing poorly on new, unseen data. Conversely, underfitting happens when the model fails to capture relevant patterns in the data. Balancing this is a significant challenge.
5. Scalability: The model might work well with a small dataset but might run into problems when used with a larger real-world dataset. Ensuring the predictive model can reliably function at scale is crucial.
6. Interpretability and Trust: Convincing stakeholders to rely on the outcome of a machine learning model can be difficult, especially when the working of the model – often termed the “black box” problem in AI – isn’t readily transparent or explainable.
7. Regulatory and Ethical considerations: With the increasing use of machine learning, considerations about data privacy, security, and ethical use of AI have become paramount. Complying with these regulations presents its own set of challenges.

Seizing the Future

Predictive algorithms backed by machine learning hold significant potential for transforming the manufacturing sector. Their ability to quickly process vast amounts of data and predict potential outcomes can drive operational efficiency, reduce costs, and enhance profitability. By enhancing preventative maintenance they minimize downtime and prolong machinery life. They can improve quality control by identifying errors in real-time, ensuring superior product quality and customer satisfaction. Through supply chain optimization, they ensure accurate inventory management and just-in-time delivery. Furthermore, they assist in making data-driven decisions for better resource allocation, product development, and business strategies. Effectively leveraging these benefits requires overcoming challenges like data quality issues, skill gaps, and scalability. With the right approach, machine learning algorithms can indeed propel the manufacturing industry to new heights of productivity and efficiency.

Are you using a predictive algorithm to optimize your business? We’d love to hear about it! If you’re not and would like to learn more, just drop us a line!