The wave of data science for business
By leveraging the huge influx of data, businesses may not only overcome challenges but also operate supply chains that can cuts costs and meet expectations of consumers, shares Prabhakar Chaudhary, MD, HAL Robotics.
Supply chain management is a crucial wing for any enterprise, e-commerce industry or even a start-up. With increasing globalisation, technology complexity and given how volatile the supply chain systems tend to be, businesses that are unable to adequately regulate supply chains stand at a greater risk.
Logistics, as a sector, is widespread across the globe with complex networks. It is a vital industry, clocking in revenues worth $8 trillion. Furthermore, the sector is expected to be valued $15.5 trillion by 2024, clearly demonstrating the huge potential of the industry. At the same time, artificial intelligence has made significant inroads amidst our daily lives. As per the forecasts released by IDC, the global spending on AI will grow at a CAGR of 55 per cent to be worth USD 47 billion by 2020. Amalgamating artificial intelligence with the growing adoption of Internet of Things can revolutionise the logistics sector across the globe. UNDERSTANDING STRUCTURED TECHNOLOGY
While leveraging the true potential of data sciences may appear to be the most plausible solution, getting started with the same would warrant paradigm shifts and technology revivals. As of today, several factors have contributed to the upsurge of data. This includes operational data, social media, data sourced from sprawling IoT devices, navigations services and more. While a portion of this data is structured, a major chunk continues to be unstructured. Structured data may include information regarding transactions, such as sales, purchases and production orders. The logistics companies have been dealing with the structured data for a long while, however, taking advantage of the unstructured data allows companies to operate in real-time, enhance visibility, and optimise operations live.
Making sense of all the available data and translating the same in business metrics require data science to become an integral part of the businesses today. The IoT technology is becoming increasingly affordable, and can be integrated with a wide gamut of functions, such as low-cost consumer goods, at the production floor etc to collect data in real-time. The sensors help regulate the transit environment, update routes in real-time incase of any delays. Furthermore, companies also need to get a realistic estimate of the customer sentiments. The same allows logistics firms to get foresee risks and opportunities.
In addition, logistics companies operate in a globalised scenario, coordinating with a diverse and complex network comprising vendors, vehicles, partners and subcontractors. The complex network exists within the spheres of ever-changing global economics, spikes in regional demand, climate, geopolitics and more. The learning faculties under data sciences can integrate these real-world challenges to upgrade the existing models and better adapt to the changing environment. ENABLING SUITABLE PROCESS
Adopting data sciences has significant direct impact on cost savings as well. Take for instance the road freight. Logistics companies must coordinate with a number of fleet vehicles, spread across the geography. To ensure proper health, constant maintenance is required. While preventative maintenance may cost more, data sciences provide savings in the form of predictive maintenance. By analysing transport data, logistics firm can foresee the likelihood of a component failing. Owing to the same, the components can either be fixed or replaced ahead of time; hence, saving logistics companies enough time, money and loss of business due to unforeseen delays and resulting customer grievances.
In addition to saving costs, data sciences also allow logistics firms to usher in a wave of operational efficiencies. For instance, by considering myriad factors like economic indicators, past transactions and consumer profiles, local market dynamics etc, data sciences can provide logistics firms with accurate demand forecasts. The same can be broken down to compute daily volumes, properly allocate resources, optimise the delivery routes etc. As a consequence, not only the entire process is efficient but also helps logistics companies deliver a greater satisfaction to their customers.
Speaking in regards to fourth industrial revolution, the World Economic Forum cited that the current world is standing “on the brink of technological revolution that will fundamentally alter the way we live, work and relate to one another.” With the emergence of start-ups that combines data sciences and logistics, the space is going to remain abuzz with activity in the coming years. Ambitious logistic firms need to be quick to take note of this upcoming revolution and invest in establishing learning systems that can make sense of all the disparate and complex data, while creating greater opportunities for cost reductions, drawing operational efficiencies and streamlining supply chains. (The views expressed are solely of the author. The publication may or may not subscribe to the same.)
Prabhakar Chaudhary MD HAL Robotics