How does Big Data, AI, Real Time, Agile, ERP, MES and SCM contributes to positive EBITDA?

Production or Manufacturing houses has always been keen on reducing cost, improving Overall Equipment Efficiency OEE, reducing wastage and many more while achieving high profitability with the optimum customer satisfaction index.

Various technologies and methodologies have been found to contribute to positive EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization) in different ways:

1.     Big Data:

The term Big Data originated in 1990s. It started when an organization has Structured, Semi-Structured and Unstructured data from various sources that needed to be consolidated. By analysing large datasets, organizations can gain valuable insights for better decision-making, improving operational efficiency, identifying market trends, and enhancing customer experiences, ultimately leading to increased profitability.

2.     Artificial Intelligence (AI):

Artificial Intelligence or AI started way before Big Data. However, only lately which is post 2020, AI begin its footprints in many platforms. AI technologies can automate processes, optimize operations, predict outcomes, personalize customer interactions, and streamline workflows, all of which can positively impact EBITDA by reducing costs and increasing revenue.

3.     Real-Time Data Analytics:

Real-time data analysis allows organizations to make informed decisions quickly, respond to market changes promptly, identify opportunities for growth or cost savings, and improve overall operational performance, which can contribute to positive EBITDA.

4.     Agile Methodology:

Agile methodologies promote flexibility, adaptability, and rapid response to changing business requirements. By enabling faster project delivery, improved collaboration, and enhanced customer satisfaction, Agile practices can lead to increased revenues and cost efficiencies, positively impacting EBITDA.

5.     Enterprise Resource Planning (ERP):

ERP systems integrate various business functions, streamline processes, enhance data visibility, and improve decision-making. By optimizing resource utilization, reducing operational inefficiencies, and enhancing productivity, ERP systems can contribute to positive EBITDA.

6.     Overall Equipment Efficiency (OEE) Improvement /Manufacturing Execution Systems (MES):

OEE/MES systems optimize production processes to boost overall equipment efficiency, track key performance indicators, enhance machinery performance improve resource utilization, reduce downtime, and enhance quality control. These efficiencies can lead to cost savings, increased productivity, and higher profitability, positively impacting EBITDA.

7.     Supply Chain Management (SCM):

Effective SCM practices optimize inventory management, procurement, logistics, and distribution processes. By reducing lead times, minimizing costs, enhancing supplier relationships, and improving overall supply chain efficiency, SCM can positively impact EBITDA through increased operational performance and customer satisfaction.

8.     Cost Reduction:

Implementing cost-effective measures, optimizing processes, and minimizing expenses to improve the overall financial performance.

9.     Waste Reduction:

Implementing lean manufacturing principles, reducing scrap, and optimizing resource utilization to minimize waste and improve efficiency.

10.  Profitability Enhancement:

Focusing on revenue growth, cost control, and operational efficiency to increase profitability and financial success.

11.  Customer Satisfaction Optimization:

Understanding customer needs, delivering quality products on time, and providing excellent service to enhance customer satisfaction and loyalty.

Would you say AI is nothing without data or Big Data?

Big Data emerged in the 1990s to address the challenge of managing structured, semi-structured, and unstructured data from diverse sources. It aimed to consolidate and analyse large volumes of data efficiently.

Artificial Intelligence (AI) has a longer history than Big Data, dating back decades. AI involves the development of intelligent systems capable of performing tasks that typically require human intelligence. Post-2020, AI has become more prevalent across various platforms and industries, leveraging advancements in technology and data analytics.

Technology companies have created algorithms based on Machine Learning and Theory of Global Optimization to provide the right decision-making real-time data to its users. The information can begin from the point when an estimator creates a quotation or when a job order is created up to the final step of its cycle. Knowing your machine capacity planning, right material availability and resources availability will enable you to make the insightful commitment to your client. This will eventually have a direct impact on your cashflow with smart inventory management.

Despite having AI from the beginning, we all have experienced real life challenges where ad-hoc has become a norm. This will result to clients wanting to make decision in much shorter time frame. Agile Dynamic Scheduling has also been in talks since the late 1990s. Scheduling which is one of the crucial steps at a factory where it is usually left in the hands of the scheduler with sticky notes or the use of tables and rows in an office application tool. Being able to get assistance from the system in minutes if not seconds will enable clients to achieve their full potential on the Overall Operation Efficiency OOE. 

Once we have Big Data, AI and Agile Dynamics Scheduling, these means nothing if we unable to get the data in a real time basis with proper contingency in place. Having auditable data has become the key requirements for Environment, Social and Governance ESG's reporting. 

Now ESG is a topic by itself which I will not elaborate here and will probably delve deeper in my next write-up. 

Technology companies have been investing on its R&D especially to improve the above-mentioned solutions to meet the requirements of our valuable clients around the world while maintaining the industry standards. 

I personally have experienced the evaluation of all the above. If we recall Bill of Material BOM, Material Resource Planning MRP1 or MRP2, all of these was an extension from an Enterprise Resource Planning ERP solution which had a purposed built out-of-the-box Manufacturing Execution System MES and Supply Chain Manufacturing SCM. An ERP solution that has financial modules are known as the generic ERP. Most of the time they are a plug and play in most organization with the country's localization pack in place since it handles double entries and check-and-balance. The predicament begins when organizations tend to fall into the black hole of customization or custom-build MES or SCM solutions. I've seen and experienced many organizations big and small fall into this trap and sometimes they realize promptly but sometimes they will only realize when proper IT audit becomes a requirement at their grown organization. 

All of us certainly also witnessed the different evaluation of CXOs. The reason for this is certainly in the custom focus purpose expertise that was required at the board to make insightful decisions. The same is applicable in the industry solutions. 

The reason for me to start my write-up from Big Data, AI, Agile Scheduling, Real-Time-Data-Collection to MES and SCM is because in any production environment, having the optimum Overall Operation Efficiency OOE is crucial. Having the right stock balances, knowing the right machine capacity which leads to Overall Equipment Efficiency OEE, leveraging on the fullest machine and resource capacity provides the capability for an organization to have an organic growth with positive EBITDA or otherwise. 

Indeed, the effectiveness of technologies like Big Data, AI, real-time analytics, Agile methodologies, ERP, MES, and SCM is maximized when they work cohesively in an integrated environment. Siloed implementation of these technologies can limit their potential and hinder organizational performance. A harmonious integration allows for seamless data flow, process optimization, and holistic decision-making, unlocking the full benefits of these tools to drive efficiency, innovation, and profitability within the organization.

Investing when the right feasible return of investment (ROI) is key especially in the manufacturing and production world.

By Pratheep Arumugam

21st May 2024