Before Warehouse Orchestration, Get Your Data in Order

The past few years have seen a wave of technological innovation across the supply chain, especially in the warehouse. But with the addition of new technology, such as robotics and automation, comes greater complexity and the added challenge of optimizing systems and making them work together. Hence the need for “orchestration” of all those elements to maximize the impact they can have.

Specifically, orchestration needs to happen at two levels: within the four walls of a warehouse to maximize the productivity of people and technology, and among all partners in the end-to-end supply chain that includes transportation, warehousing, packaging and inventory.

Leading supply chain organizations are beginning to apply orchestration strategies to increase resource utilization, reduce costs and enable faster and more efficient response to the many events that can disrupt operations on a given day. Orchestration represents the final frontier in supply chain digitalization and promises to bring a new level of sophistication to supply chain management that minimizes the costs and maximizes the speed of supply chain operations.

As your business looks to embark on orchestration, it is important to understand that the strength of your data analytics program is vital to the success of your orchestration strategy. Data analytics is the building block that allows orchestration to occur. It is the sheet music that guides each piece of technology to perform its role in the synchronized orchestration of warehouse operations.  It provides the visibility needed to efficiently manage and coordinate warehouse activities to maximize the productivity of people and technology.

So, before beginning orchestration, you first need to ensure your data analytics strategy will help position your orchestration efforts for success.

Enabling Data Accuracy and Accessibility

For your data to enable orchestration, it must be accessible, trustworthy and digestible.

1. Eliminate data silos that impede orchestration efforts by integrating systems and advancing digitalization to enable synergies.
2. Get your data in shape by identifying gaps or missing data sets and making sure the data is standardized and actionable.
3. Evaluate your foundation to ensure you have the process optimization and labor management functionality with your supply chain systems that provide the data needed to orchestrate people, technology and equipment.

Without access to the right data in the right form at the right time, humans and systems will not have the visibility and insight they need to make the autonomous decisions orchestration requires.

Given the important role automation and robotics play in orchestration, integration and connectivity across systems has become a priority for warehouses seeking to advance orchestration through the strength of their data.

Consider adopting an automation platform that enables fast and seamless integration of various automation technologies without customization. The platform should also aggregate and normalize data across technologies and vendors, which can simplify monitoring and enable interoperability. When integrated with data from labor and other systems, this allows potential bottlenecks in processes to be identified proactively and resources – whether human or robotic – deployed quickly to keep products moving.

When it comes to data analytics and orchestration, it might help to think about your data sets in terms of a pyramid with source data providing the base, data products in the middle and analytical products at the top.

In this context, source data refers to unrefined, unstructured or semi-structured data in a shape and form very close to, or exact to its precise origin (e.g., a copy of transactional systems data). This data has not gone through any significant transformation, it may be very system specific with limited metadata, making it hard to interpret without prior source-system specific knowledge. 

A data product is essentially an encapsulated piece of data that has been transformed, processed, and curated to make it easily usable by teams and individuals at any site and at various levels of an organization. Just as you don’t need to be an automotive engineer to drive a car you’ve never driven before, you shouldn’t need to be a data scientist to access and use supply chain data. Data products unlock the power of data for the people and systems that are best positioned to use that data to affect operations.

An analytics product refers to the tools, applications, or services that enable users to analyze and derive insights from the data, typically leveraging one or more data products. It focuses on providing capabilities for exploration, visualization, reporting and advanced analytics that utilize some machine learning and/or AI algorithms.

This sort of foundational work done on visibility and data analytics enables accelerated deployment of the advanced analytics and artificial intelligence solutions needed to optimize and orchestrate supply chain operations.

A fully orchestrated supply chain would rely on a range of digitalization technologies to create near-100% accurate inbound forecasts and then develop and execute a plan to ensure all supply chain resources – people, systems and automation – are balanced and used optimally to execute on that plan. It’s a daunting challenge but like digitalization in general, orchestration will advance in stages, with each stage delivering value until the ultimate goal is realized.

Digitalization is already delivering significant advances in productivity, speed and visibility to supply chain operations. Orchestration promises to build on and amplify those benefits. And ensuring your data analytics provides the needed foundation for your orchestration efforts is an important first step.

For more information about how DHL is using data analytics, visit dhl.com/allin

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