Implications for Industrial Analytics in Supply Chain Management

Teri Grossheim
Jan 7, 2015 · 3 min read

Recently, there has been widespread discussion on a variety of data intensive topics and their application to supply chain management. The Internet of Things, location-based technologies, operational technology, and IT all have data components with immense value for asset-intensive organizations. Industrial analytics, which is the process of capturing, organizing, combining, and applying specific and applicable OT data with IT, has certain implications for the supply chain industry. This article discusses impacts in these areas, thoughts around applications, and best practices for organizations in the supply chain industry looking to optimize data.


Chief Supply Chain Officers are currently faced with transforming supply chain operations, including moving from being a service provider or product manufacturer to one that combines services and products based on customers in both established and growing demographic segments. Data from a variety of areas within an organization can support this transition, presenting a complex situation for decision makers. Manufacturing and transportation organizations are currently analyzing operational data such as transactions, log data, machine data, and sensor data as part of their big data initiatives. There is tremendous opportunity for engineers, system designers, and business process architects to automate and optimize functions that may have required a manual touch in the past. There are five areas that have potential impact in operational technology:

Dark Data: This is the data that is swept under the rug — information obtained during the normal course of business that stays in an archive or cannot be structured for analysis, such as multimedia or emails.

Operations Data: Data about suppliers, partners, customers, and employees typically including contract, master, and transactional data. Smart meters, machine data, and Internet-connected devices are also examples.

Commercial Data: Data from industry-specific sources such as Nielsen. Data marketplaces have been emerging quickly but since they most contain behavioral and transactional activity, its impact will be low due to certain applications.

Public Data: Even though public data is typically for innovation and economic development, OT can use weather and geolocation data to enhance the performance of construction processes and machinery.

Social Data: Tweets, LinkedIn posts, and Facebook provide mostly unstructured data which requires considerable extraction and structuring to have any value.


Operational technology data from sensors, location-based technologies, smart devices are now merging with traditional IT data, providing compelling results for the supply chain industry:

  • Logistics companies such as FedEx use industrial analytics by monitoring traffic patterns, vehicle locations, and weather conditions to optimize routes, which reduces gas consumption, vehicle maintenance, and improving safety.
  • Rail sensors are in use by the Swiss Rail’s dispatch system, which generate 100 messages per second that improve on-time performance and optimizing rail network loads.
  • Johnson Controls has systems that monitor and analyze dozens of continuous data streams such as HVAC performance, elevator and stairwell usage, and room temperature to make automatic adjustments and optimize elevator patterns.

Sensor integration should be improved for asset-intensive organizations due to proprietary standards at the basic level up to Internet-connected seniors with cross-connected integration, along with sensors with distributed processing and self-learning capabilities.

Getting Started & Best Practices

An asset-intensive organization should identify a high-value use case for optimizing industrial assets by identifying the correct data and ensure the right data is being collected, which be difficult given that IT and OT have evolved differently since they are based on different information types and information architectures. IT has evolved top down, whereas OT has evolved from the bottom up. Once this takes place, develop a pilot program to explore a tangible, high-value outcome. The decrease in cost of sensors, data security, network connectivity and standards seem to factor most into these types of pilot programs. It seems the Internet of Things has created a platforms for standards discussions. Several protocols such as machine to machine (M2M) and OPC Unified Architecture (for integration of OT data into programmable logic controllers, distribution control systems, and other production equipment/machinery), can help steering the pilot program process.


Agenda Overview for Supply Chain Strategy & Enablers. Gartner ID G00259822

Industrial Analytics Revolutionizes Big Data in Digital Business. Gartner ID G00264728. April 2014

The Confluence of Operational Technology & Big Data. Gartner ID G00246899. August 2013

Teri Grossheim

Written by

Solution Architect/Program Mgr | IT Engineer & Developer | Musician & @AESChicago | Educator | @DePaulMBA | Boardsports | Fmr @JamfSoftware @CDWCorp

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