Article

Driving change: The new era of logistics and distribution

Here’s what your industrial company should zero in on to gain a competitive edge

Aug 14, 2023

Key takeaways

Logistics companies prioritizing digital transformation can tap into enormous opportunities.

As customers demand more precision in tracking their goods, there will be less room for error.

Predictive capabilities are key, and advanced data analytics can enhance these capabilities.

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Predictive analytics
Manufacturing Supply chain Artificial intelligence Digital transformation

Logistics and distribution businesses face significant challenges, including global supply chain disruptions, workforce shifts, evolving delivery methods and heightened customer expectations. These businesses must adopt advanced technologies, prioritize data-driven decision making, enhance information technology (IT) infrastructure and streamline inventory management processes to be successful.

Saturation in the logistics and distribution space has exploded in recent years. Combined with anticipated advancements for autonomous vehicles and rising energy costs, this has made adopting advanced technologies more critical for companies in this space than ever before.

Digital transformation technologies play a crucial role in helping you manage costs, reduce risks, stay ahead of the competition and generate greater long-term stakeholder value. Such technologies also enable you to pivot more seamlessly in the face of ongoing upheaval. It can be challenging to figure out how to begin or how best to continue along your digital transformation journey; RSM can help you understand where to start.

We understand how the supply chain affects all parts of your business and can advise you on effectively addressing these challenges. Defining a data-driven supply chain strategy, prioritizing digitization, and harnessing advanced technology are some of the most critical areas to focus on.

Define a data-driven supply chain strategy

Having a clear supply chain management strategy that accounts for total landed cost, physical network movements, alternative sourcing options and key supply metrics is essential if your business wants to distill and harness that information into actionable, predictive insights.

Companies must be data-driven, using information to describe past events, forecast future outcomes and make informed decisions. For logistics and distribution businesses, predictive capabilities are particularly crucial, and advanced data analytics can drive and improve these capabilities. Effective use of data requires connectivity among machines, products, employees, suppliers, customers and processes throughout the entire value chain.

This connectivity is essential for unlocking the full potential value of the data.

To be able to take full advantage of the enormous amount of data on hand, logistics and distribution companies need to focus on three foundational areas:   

  • Data and system architecture  
  • Data governance  
  • Data analytics, both traditional and advanced  
cogwheel moving into cloud, data center, data numbers
icon - data - salesforce

If the last few years’ supply and demand roller coaster was not enough to motivate companies to prioritize a data-driven approach, other economic uncertainties should spur action. Your organization should implement technologies now that can help better predict demand, conduct cash forecasting and plan with confidence.

Prioritizing digitization

A robust IT infrastructure is required to enable a data-driven approach, and most middle market companies’ IT infrastructure is not ready to support advanced Industry 4.0 technologies. You will need a more scalable and highly interconnected IT architecture to support the shift to more data-driven operations and meet future challenges.

For many middle market organizations, shifting from on-premises IT systems to cloud-based IT operations can seem daunting. But because cloud computing has lowered the barriers organizations may have previously faced in becoming a data-driven enterprise, these platforms also hold enormous possibilities to transform companies’ IT systems.

Alongside that transformation, though, businesses also need to be aware of evolving cybersecurity risks. Teams should prioritize processes for reducing and mitigating such risks during infrastructure upgrades. This process prioritization might include identifying system dependencies, implementing monitoring and detection mechanisms, backing up critical data regularly and conducting regular security assessments.

As customers demand more precision and reliability in tracking and delivering their goods, there will be less room for error. Having a resilient IT infrastructure will be paramount.

Harnessing advanced technologies

Artificial intelligence, predictive analytics, cloud computing and the industrial Internet of Things are just some of the advanced technologies companies use to streamline operations and accelerate innovation. Organizations might consider using AI-driven simulation tools and digital twins, for instance, to better anticipate supply chain disruptions and understand when to adjust their plans accordingly.

In the logistics space especially, harnessing these capabilities can be a major differentiating factor; technologies that enable a more precise, real-time understanding of supply chains and the flow of goods have a competitive edge. Technological adoption will also continue to have significant implications for warehouse operations and inventory planning, both critical for success in the sector.

Logistics and distribution companies that prioritize digital transformation will be able to tap into enormous opportunities. Plenty of businesses in this space still operate with manual, paper-based systems. Some subsectors, such as trucking, rely heavily on individual contractors for labor. Companies need to examine where there are opportunities to standardize and streamline operations across the business and how advanced technologies can help improve processes, attract and retain talent, and enhance customer visibility.

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