The global logistics sector is worth $8 trillion, and is expected to grow to $15.5 trillion by 2024. Yet it is one of the few remaining sectors that has yet to capitalise on the vast quantities of data that it generates. Supply chain operators are responsible for capturing an enormous wealth of data— from business systems, IoT devices, social media, plus others — and have the potential to transform their views of customers, suppliers, manufacturing, logistics, and more. Some pioneering organisations are already doing this, and seeing huge gains by utilising new data-centric methods, such as predictive analytics. But making sense of all this data can be more than challenging, and to crack this they will need to use data scientists.
There’s a major challenge to overcome, though. Research by Accenture revealed that while most companies have high expectations for big data analytics in their supply chain, many have had difficulty adopting it.
“97 percent of executives report having an understanding of how big data analytics can benefit their supply chain, but only 17 percent report having already implemented analytics in one or more supply chain functions”
[source: Accenture “Big Data Analytics in Supply Chain”]
However, there are signs that the logistics sector is taking action. Research by P&S Market Research has shown demand for data scientists is increasing most rapidly in the logistics sector. “Among the industry verticals, demand for data science from healthcare industry is expected to generate largest revenue for the global data science market during the forecast period, however, the demand is expected to grow at the fastest rate from transportation and logistics industry in the coming years.”.
First of all, lets look at the opportunities. If you think about the opportunities supply chain data provides is comparing it with the maintenance of cars. Historically, drivers would visit a garage when their vehicle broke down. Then came regular checks to identify problems before they deteriorated. Nowadays, smart vehicles are providing diagnostics in real-time. This provides huge benefits and cost savings for both businesses and consumers. A similar benefits curve exists in the supply chain context. Through data analysis it is possible to run analyses and algorithms that identify existing trends and constraints, and use it to predict future pain points or failures caused by shifting demand patterns. Through better prediction of demand, some companies have successfully cut 20% to 30% out of inventory while increasing the average fill rate by 3 to 7 percentage points. There are of course many other opportunities associated with the use of data science analytics:
…And How To Access Them
Predictive Analytics is the game changer. Speaking at a supply chain conference recently, Matthias Winkenbach, director of the Massachusetts Institute of Technology’s (MIT’s) Megacity Logistics Lab, said these innovations “can be a powerful resource for the sector, if businesses are able to effectively harness them”. How? Here are three applications:
Cost Savings – in the road freight and transportation sectors, fleets of vehicles often reach the tens of thousands — all of which require constant maintenance. Instead of defaulting to preventive maintenance, transport data analytics enables companies to model the data and determine which components are the most likely to fail. This type of analysis allows technicians to repair/replace components early instead of performing expensive post-damage repairs
Operational Efficiency – By linking historical activity data with consumer profiles, economic indicators, and localised market data, logistics and transportation providers are able to predict demand with increasing accuracy. This allows them to anticipate daily volumes, optimise delivery routes, and allocate resources accordingly to deliver the service more efficiently, ultimately increasing customer satisfaction. In addition, transport data analysis is extremely good at discovering bottlenecks, particularly those derived from inefficiencies and sub-optimal operations. The logistics industry, being primarily driven by economics, is at the mercy of fuel cost, security measures, time to delivery, air vs land, supply chain reliability, domestic distribution networks, offshoring, etc. There are many factors that can impact profitability, all of which need to be taken into consideration in order to achieve increased efficiency. The disparity and complexity of these factors means that data scientists are, realistically, the only way businesses can unlock the value of Big Data.
Dynamic Pricing – logistical efficiency also impacts consumer product pricing. A change in one input (e.g., increased fuel cost) can have a profound impact on the overall shipping cost and, consequently, the product price. Price determination should be variable and based on real-time cost data. Predictive analytics solutions are capable of factoring in cost-sensitive components and using that data, often combined with external dimensional data (e.g., weather patterns and transport time), to accurately predict an optimised price. This method of dynamic pricing ensures that your logistics operation balances competitive pricing with actual shipping costs.
3 Ways Logistics Leadership Can Get Data Science-Ready
Data science techniques can improve automation, increase freight usages, track vehicles more accurately, enhance customer interactions and visibility, and help responses to external factors such as weather. But these gains can only be fully realised if businesses are aware of, and avoid, some very common problems that hamper the use of data science in procurement. Here are three ways supply chain leadership can prepare their business for data science and predictive analytics:
- Solid data infrastructure – Multiple sources of data from inside and outside a business need to be collated and crucially, updated as close to real time as possible. Without the full picture, data science cannot provide the most useful insights and advice to a business
- Ownership and oversight of data science: Too often data science can be compartmentalised into one section of a business, for example, procurement. What’s needed is a virtuous circle where information and requests throughout a business, spurring better analysis and insights, which in turn prompts better understanding of the capability of data science within the business, encouraging more information sharing and ultimately better decision making. For example, efficiencies made in procurement could apply to marketing. Similarly, knowledge or data held within the marketing or customer service departments could help inform the analysis of the procurement process. To get the best use of data science, the data science team or service provider should have visibility and buy-in from the whole business
- Ask the right questions – Unlike most analysts who ask their data ‘what’, data scientists are programmed to ask ‘why’. By understanding ‘why’, data can reveal underlying, disparate, and often counter-intuitive, trends and factors that affect how a business functions. This can lead to innovative solutions for inventory management by ensuring that influences on stock forecasting such as weather and customer profiling are incorporated. Transport and logistics can be made more efficient by planning optimal routes, improving notifications based on geo-location and the incorporation of complex delivery preferences.
Data science can be a silver bullet that improves an organisation’s supply chain and creates cost savings and efficiencies. To be successful, data science requires access to the right information, support from the entire business and its full capability needs to be recognised. This is as true for using data science in procurement as it is for the whole business. Through our experiences we are discovering, companies that employ a dedicated team of data scientists are far more likely to generate a range of important supply chain benefits from their use of big data analytics.
To find out more about how Pivigo can unlock your supply chain data, contact us at email@example.com