Breaking Down Silos with Big Data Analytics in the Supply Chain

Breaking Down Silos with Big Data Analytics in the Supply Chain

Bob Bova  |  2/25/2019

With Big Data analysis being at the forefront of intelligent decision support right now, the ability of warehouse management systems (WMS) to deliver so much relevant data to supply chain and distribution managers with respect to operational effectiveness and efficiency, has reached new levels never seen before. In addition to making sense of this wealth of information, a key challenge is to find applications that can work with the WMS companies currently have installed. The benefits of leveraging insights from the abundance of operational data can be astounding…but a secondary benefit can be the breaking down of information silos long existing in many companies. It can also lead to the sharing of intelligent solutions that help the entire organization, blurring previous informational divisions across departments.

It is no secret that many companies have distinct silo type of infrastructures when it comes to applications, processes, support, hiring methodologies and future strategic directions. With decentralization occurring in many departments in most companies (for good reasons), forward thinking teams have pushed technology and process evolution in imaginative and substantive ways. Keeping their eyes on key performance metrics for both the employees and these departments has provided a work environment that is challenging and fulfilling, proving the management strategy of decentralizing and experiencing thought leadership and planned execution in these departments within the firm.

The time has come to now bring these departments together for Big Data and AI to flex their decision support muscles. Now that these departments have worked diligently to deliver the mandated performance and economic measurements denoting success, the next step is to use these newer, more powerful software tools and computing power that can provide a more comprehensive decision support model. Providing the access to these silos and their data can provide quite the challenge. The biggest asset is the WMS and ERP systems the companies have as a backbone to run the company’s business, and this is where it can get interesting.

There are companies that have been running their businesses on home grown WMS systems or are running an older version of the WMS application. This reality is starting to bubble up to the top for many companies. Fact is, these companies have been running on these applications for years, supporting old versions of applications, mobile hardware that is 10 years old and processes that haven’t changed. This is not because they didn’t want to upgrade, but the economics of having these assets perform for long periods of time provide outstanding ROI metrics and the older they are, the more pure profit drops to the bottom line.

The time has finally come, with the advent of the newest generation of Big Data tools, to revisit old decisions that were solid back in the day, but now just cannot exploit the new power of Big Data analytics. The newer WMS/ERP systems can provide a wealth of important data, integrating decision support across silos and presenting a comprehensive, full supply chain snap shot of an operation (not just a single application at a time, as some legacy systems tout). This review does not just mean the WMS/ERP systems. The new paradigm allows for revisiting mobile hardware choices, including operating systems, peripherals, network improvements as well as older, legacy single application server based applications (like voice picking) that may have been installed over a decade ago and have been unrelenting in maintenance costs and denying WMS version upgrades specifically because the reintegration of these applications would be like purchasing that application all over again.

By revisiting decisions that were made in the past, operations, IT and warehouse management can evaluate the impact a new WMS, mobile hardware and applications can have on their supply chain performance. With newer, more specific success metrics now the norm, these application and technology refreshes are a critical first step in providing the runway to utilize Big Data and continue down a path to innovative decision support. Just looking at voice picking, there are now device-based, voice solutions capable of voice enabling picking as well as any other application in the warehouse, without proprietary hardware which significantly reduce maintenance costs, while supporting multiple operating systems, commercially available mobile hardware platforms, and are WMS/ERP agnostic.

These solutions can be deployed using Big Data metrics to analyze and confirm which specific processes in the warehouse are the best choices for delivering increased productivity and error reduction. Having the power and flexibility to deliver voice enabled mobile solutions provides a strategy of integration across data silos, thereby breaking down walls that in the past, had no communication or technology partnering. The ability to analyze the significant amount of supply chain Big Data and rapidly deploy solutions such as mobile voice technologies – which benefits the entire organization – is an example of leveraging the key contributors to every department, utilizing decision support tools that were not available in the past.

While a decentralized strategy is still the best way for organizations to keep an expert focus on these individual functional areas, the advent of technologies like AI, Big Data and device-based voice enablement can provide access and tools to take steps into the future of a successful supply chain.

2019-02-25T09:57:14-08:00