How to improve manufacturing logistics with data analytics
90% of logistics experts state that data and analytics will be crucial for their business. Advanced data analytics creates better customer experiences, improves delivery quality and efficiency, and helps to reduce logistics expenditure. Find out how the harnessing of data is disrupting the market in this article.
In upcoming years, manufacturing logistics will face major challenges. Increasing customer expectations stemming from the “Amazon Effect” are already increasing the pressure to deliver goods faster, cheaper (or for free) and with greater flexibility. Additionally, new players are entering the industry with innovative solutions that disrupt the market and force manufacturers to update their logistics management systems.
Further to this, new platforms emerge which provide businesses with productive assets that aren’t utilizing their full capacity yet in order to optimize their transport services through backhauling or cross-docking or to overcome the problem of limited truck capacity. This trend is breaking down formal logistics models and proving essential to staying ahead of the curve. However, the one common denominator of all these challenges is that they can each be overcome using technology, in particular by leveraging the power of data analytics.
According to a study by PwC, 9 out of 10 transport and logistics industry experts believe data and analytics will be crucial for their business’ success in the next five years, a higher proportion than in all other industries. That figure has also increased sharply from 2016 when only 50% of respondents said that data and analytics were crucial to their business at that time.
Data and analytics are expected to become key for decision-making in logistics. Data from PwC’s report ‘Industry 4.0: Building the digital enterprise’.
Today, manufacturers have more logistics data available to them than ever before which, when combined with access to advanced analytics, can provide them with insights to help improve operations and efficiency.
How Data & Analytics Can Improve Logistics
Pick-up & Drop-Off Data
First and foremost, in order to truly leverage data and analytics, you must collect the right data. For manufacturing logistics, that means companies need to track a number of variables, including delivery times, success rates, incident percentages, the ratio of on-time deliveries and the type of carrier used. This allows logistic and warehouse managers to measure and analyse the performance of their daily operations and ultimately identify ways to improve operational efficiency.
Access To Data
Though there’s no denying the unprecedented quantity of data available to today’s manufacturing logistics managers, that data is useless without knowledge of how to extract useful information from it. By identifying methods that allow them to visualise data in meaningful ways, logistics managers can make better, more informed decisions in order to achieve optimised operational performance. Actionable dashboards enable them to boost forecasting to improve route-planning and enhance capacity utilisation. In the future, the combination of data analytics, machine learning and artificial intelligence could even let managers set-up dynamic routing in real-time, without the need to be involved and consulted constantly.
As the sharing economy paves the way for the development of more efficient delivery services for inner urban freight transport and inter-city logistics, warehouse and logistics managers must consider adding online marketplaces to their logistics strategy. By incorporating these platforms into their current solution portfolio, they can optimise their logistics by gaining access to thousands of qualified drivers in real-time and obtain the best carrier based on their needs.
Track & Trace
As customers demand higher standards in product availability, shipping and delivery costs, companies will have to provide upgraded traceability information, improve their delivery tracking systems and create more accurate prediction models. Indeed, a recent survey by Inbound Logistics revealed that 82% of logistics managers cite visibility as the biggest transportation challenge, demonstrating their awareness of these changing standards. An effective way to overcome this issue is by integrating social supply chains with data analytics strategies which result in improved traceability and predictability, allowing manufacturers to boost their daily operations.
Optimise Financials And Quality
Therefore, data and advanced analytics not only improve customer experiences in the long-run, they also lead to substantial financial savings in daily operations. Manufacturing logistics managers can leverage data analytics to elevate delivery quality, speed, on-time delivery ratios, and the number of incidents. Additionally, data analytics can also reduce logistics costs on a number of different levels, since it facilitates more efficient daily operations. Refined route-running and the harnessing of more economical online logistics platforms can also generate significant positive effects on the balance sheet.
Would you like to receive information about how we work? Get in touch with us, the Ontruck team will be happy to help you! Contact
How Ontruck guarantees the best price for LTL freight in the industry
Shippers spend a lot of time contacting several providers for spot pricing, shopping around to find the best price-quality trade-off. Ontruck gives shippers of all sizes the best contract and spot prices to ship less-than-truckload (LTL) freight.
Ontruck has recently been awarded by BloombergNEF’s annual Pioneers competition, recognizing early-stage companies with the potential to make a meaningful contribution to global decarbonization. Discover here why.
Delivering Industry-Best Service with Technology & Predictive Algorithms
Shippers are expecting greater supply chain visibility and customer service to meet their end customers needs, while at the same time tightening their transportation budgets. Ontruck delivers the industry-best service with technology and predictive algorithms.