The manufacturing sector is undoubtedly one of the core pillars of growth in every major economy worldwide. From job creation to accelerating industrial growth and exports, manufacturers play a diverse range of roles in the economic engine of a nation.
However, it is also one of the sectors where disruptive events like the COVID-19 pandemic can wreak the most havoc. Production commitments coupled with risky work conditions can be a huge challenge for manufacturers to remain operational during such a crisis. They need to be prudent and efficient in their operations to remain competitive and sustain profitability. A major contributor to efficiency can be the data that lies untapped across their functional and operational business systems. Analytical processing of data within the manufacturing ecosystem can unlock significant value from both efficient execution of end-to-end operations and lowering expenditure.
Data analytics is rapidly being incorporated into the corporate work strategies of almost every major manufacturer worldwide. In fact, studies estimate that there will be a USD 4.55 billion worth of industry market size for big data analytics in the manufacturing sector.
The growing adoption of connected machinery, coupled with sensors and digitised processes, results in the rise of smart manufacturing facilities that create a massively high volume of data points for analysis. The reality is that several emerging players in the industry are not quite aware of the potential that lies underneath the huge swath of data.
Let us explore 4 ways in which manufacturers can use data analytics to drive operational efficiency:
Very often, the biggest hurdles to innovation and streamlined growth in the manufacturing sector are obstructive processes and workflows in their operations. By leveraging data analytics, manufacturers can gain insights into processes that create significant delays in the value chain due to several factors like dependency between departments, approval, audit cycle timings, etc. Once these insights are known, manufacturers can re-engineer some or all their key processes to enable a more flexible “build-to-order” approach in their production lines. This approach can help in reducing stagnant inventory and prevent excessive capital loss in the event of a product recall or expiry of a variant. It can also speed up manufacturing activity thereby directly contributing to improving the efficiency of production lines.
Availability of suitable raw materials and labour on time is critical to operating a manufacturing establishment seamlessly. In the wake of events like the COVID-19 pandemic, ensuring optimum availability of these critical resources will be instrumental in driving an efficient production capacity. This is where data analytics can make a difference. Through intelligent processing of historic material and workforce schedule data, manufacturers can get real-time visibility into resource utilisation patterns that yield the best throughput from a manufacturing unit. It can accordingly adjust production volumes, prepare staff schedules, order and stock raw materials, and ultimately forecast their order fulfilment times well in advance. This will greatly improve operational efficiency and help manufacturers tide over difficult times.
Like the case of raw materials, it is important for manufacturers to maintain adequate levels of inventory to meet commitments to retailers or sellers in the market. Overstocking may lead to wastages and subsequent capital loss. Out of stock in the inventory can result in missed order fulfilment and it can directly impact customer experience negatively. With data analytics, manufacturers can evaluate historic trends of demand and supply from markets and create the most optimal plan for inventory at their disposal. They can evaluate several data points along with market trends such as customer price preferences, seasonal upticks or reductions in demand, categoric preferences, etc. while arriving at decisions on inventory. This ensures that they stock the right product for the right market at the right time.
All the above credentials that data analytics can realise within a manufacturing business depend not just on the intelligent use of data but also on the ability of plant machinery and infrastructure to cope with dynamic production requirements. Maintenance cycles, periodic replacement of parts or machinery, factory fit-out replacements, etc. are necessary components in ensuring a disruption-free running of a manufacturing unit. Fortunately, data analytics can be the game-changer here as well. By analysing operational data and forecasted demand and supply credentials, manufacturers can gain insights into predictive maintenance schedules and infrastructure optimization needed to support their growth ambitions. Preventive maintenance and data-driven proactive monitoring of critical machinery and equipment can help in the efficient and disruption-free performance of the unit for longer durations.
By leveraging powerful data analytics, manufacturers can set the tone to enable more efficient processes, employee and resource utilisation, inventory planning, and lower maintenance. However, it is important to have the right roadmap to executing a data analytics strategy within a manufacturing business. Evaluating the right parameters and metrics to be tracked, selection of the right tools, formulation of reporting standards, etc. requires a strategic approach to enable faster ROI. This is where our consulting expertise can guide you into faster value realisation.
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