The manufacturing industry in Nigeria is marked by a lucrative zest from its players, ranging from food, beverages, textiles, chemicals, cement, textiles, automobiles, pharmaceuticals, plastics, and metals, contributing greatly to the growth of the nation’s GDP rate. It’s an industry vast in human capital, significantly tackling the problem of unemployment in the country. However, despite its richness in human and financial capital, most of its sub-sectors suffer business inefficiency, a situation where there’s low productivity and poor realization of set goals.
As compared to the driven and high-end manufacturing industries in countries like China and Germany, blooming and spiraling in endless success, Nigeria’s manufacturing industry suffers underperformance. An identified factor contributing to this plight is the lack of business intelligence (BI), the seasoned insights and strategies needed for an effective conversion of raw materials to finished products.
This intelligence is obviously missing in Nigeria’s manufacturing chain, and as global manufacturing industries transcend 4.0, it’s more than a necessity for Nigeria’s industrial terrain to adopt BI as a strategic force for economic uplift. Outlining how business analysts can contribute their professional input to closing this gap becomes the sole objective of this article, and this would be done with relevant case studies and statistical references.
The Role of Business Intelligence in Manufacturing
Business intelligence refers to a concerted process used by companies to analyze business data that would help in decision-making. This plan provides clarity for companies such that it optimizes operations and identifies creative opportunities companies can leverage to gain a competitive edge. In manufacturing, BI can be used to streamline operations and maximize operational efficiency, especially when simple data visualization tools bedecked by artificial intelligence (AI) and machine learning (ML) are utilized, making predictive maintenance and operational monitoring easy.
Alongside predictive maintenance and operational monitoring, business intelligence can help in compliance and risk management, product innovation and development, sales and market analysis, customer relationship management, production planning and scheduling, pointing out the weight of significance BI holds.
Recent studies by the World Economic Forum show that manufacturing companies utilizing BI solutions witness 20-30% of increased productivity across their service, giving a clear hint that the lack of BI in business is a straight road to stunted growth.
In Nigeria’s manufacturing industry, there are sectors occupied by small and medium enterprises (SMEs) who are mostly caught in the gap of business collapse due to zero business intelligence. A major reason for a lack of adoption is the unavailability of adequate resources to utilize technologies tailored to offer distinct services. This is backed up by the data revealed from the 2020 report of PwC Nigeria, where 68% of Nigerian manufacturers admit that limited technological resources act as a major barrier to improving operational efficiency, which further points to a business intelligence deficit. This deficit is more complex knowing that the manufacturing industry still relies on manual systems for production, giving room for poor and outdated decision-making.
The first step is to identify and integrate BI systems that are cost-effective and on budget only to cater for specific needs. It’s therefore important that business analysts help these companies evaluate BI tools towards selecting the best tools, ensuring return on investment (ROI), and being flexible enough to tolerate evolution.
Predictive Maintenance in Global Manufacturing
Business intelligence offers an array of innovative solutions, one of which is predictive maintenance. Systems used for predictive maintenance in manufacturing companies often use AI and ML to allow for easy data analysis. These tools predict when major operational tools should be maintained in order for potential breakdowns to be prevented.
This approach is very crucial in the manufacturing industry, as it not only conserves waste but also eases operations without delay or any need to have a pause and, of course, is better than traditional routines that result in unnecessary repairs and eventual failures. A typical instance in this regard is GE Aviation, where with the help of predictive maintenance across the majority of its jet engines, a record of 25% reduced maintenance cost and 35% reduced downtime effect was achieved. It’s therefore the duty of business analysts to direct local firms towards the deployment of predictive maintenance systems to drive operational efficiency. However, this should be done with friendly budgets and phasing into predictive systems in line with the given manufacturing environment.
Quality Control with AI Business Intelligence Tools
Business intelligence covers quality control in significant ways, and this goes beyond inspection and supervision; it spreads to production planning and scheduling. In some large manufacturing companies, manual inspections are still conducted, and this often results in operational inconsistency, leading to incompetent service delivery. However, with AI-driven BI tools, processes of quality control can be automated where computer vision and algorithms can quickly detect faults in real-time production. BI also allows manufacturers to stay on top of their game by setting clear production plans, and with the use of prompted data, manufacturers can optimize the schedules of production to meet up with customer demands without overproducing or underutilizing. Manufacturers can also minimize production delays, especially when workloads across units are carefully balanced.
Mckinsey & Company reports that with AI-based quality control systems, defects can be reduced by 90%, also reducing inspection time by 50%. Industry giants like BMW and Foxconn are already utilizing these systems and yielding beneficial results. Business analysts in this regard must advise manufacturers to collaborate with BI specialists to give guidance on indulging quality control systems to improve both quality and profitability in the existing work framework.
Supply Chain Optimisation: The BI Advantage
Supply in every manufacturing company determines the success or failure of operation, and in Nigeria, manufacturers battle issues in affiliation with inefficient logistics, unstable demand forecast, and lack of transparency. These issues are heavy on manufacturers as they go through excruciating processes to navigate these complexities. However, with the help of business intelligence and its applications, a revolution for supply chain management is in action. BI provides data analytics that help with the optimisation of inventory, prediction of demand, and clarity of each stage in the supply chain.
In 2023, Deloitte found out that manufacturers implementing BI tools for their supply chain gained improved deliveries by 15% and also reduced logistics costs by 12%. Business analysts should point manufacturers to employing BI data analytics tools that would revolutionize their supply process to reduce waste, increase profit, and ensure customer satisfaction, which are paramount to sustaining growth.
Leveraging Big Data for Competitive Advantage
One of the ways to overcome the business intelligence deficit, Nigerian manufacturers must invest in the collation and critical analysis of big data. These data entail invaluable intricacies needed to foster innovation and efficiency. Companies are now on the move to stay interconnected, allowing data to be shared across the supply chain to allow for the trade of business strategic cues. A big data approach usually entails that a company gathers data to analyze weaknesses, strengths, and areas of improvement for other organizations, as this often leads to the replication of these insights for drastic improvement and increased efficiency. Business analysts are expected to point manufacturers in the direction of obtaining appropriate infrastructure needed for big data analysis. They can also assist by noting Key Performance Indicators (KPIs) to allow data collection to be tailored according to specific and peculiar needs.
Challenges and Solutions to Implementing BI in Nigerian Manufacturing
Business intelligence may have immense benefits, but the implementation of these strategies to drive solutions mostly poses a challenge. The head challenge, among many others, is the lack of adequate service infrastructure such as internet access, data storage facilities, and a lack of technical expertise. BI systems would not be functional if the aforementioned services were not provided; unfortunately, SMEs that are still grappling with finances fall into this category.
Business analysts should take keen cognisance of these challenges in order to guide manufacturers rightly when recommending BI solutions. Firms should adopt cloud-based systems for data storage, reducing cost and operational delays. For technical expertise, firms should invest in training for specific staff so when BI systems are fully adopted, they can be clearly operated for maximized results.
Conclusion: A Blueprint for Success
The business intelligence deficit in Nigerian manufacturing is a concern; however, it can be averted to the minimum. Business analysts must stay performative in exposing manufacturers to the right BI tools that catapult their operation into utter transformation. These tools must be selected and implemented in alignment with the given manufacturing environment, and to overcome its challenges, firms must stay within budget. Business analysts must guide the transition from low-cost to high-impact to prevent loss and irreparable errors.
With the right strategies and tools in place, Nigeria’s manufacturing sector can close the BI gap and unlock its full potential, positioning themselves for success in an increasingly competitive global marketplace.
Success Ajilore is a highly seasoned accounting professional and business analyst with over eleven years of experience, specialising in enhancing operational efficiency, policy improvement, governance, and process optimization of various companies in Nigeria.
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