The Implications of big data in manufacturing: How to super charge your real-time connected supply chain

The Implications of big data in manufacturing: How to super charge your real-time connected supply chain


For years, supply chain professionals in manufacturing industries have been aspiring to create a truly demand-driven supply chain. Actual progress, in reality, has been slowed by both the limited availability of real-time supply chain data and the inability to dynamically optimize actions based on this information. However, as the Big Data movement continues to revolutionize industry, the real-time connected supply chain is finally becoming a reality.


Consider the explosion of supply chain-oriented Big Data. Analysts predict 30X growth in Connected Devices by 2020 (Gartner), 26.9% growth for IoT in Manufacturing thru 2020 (Forbes), 13.5% compound annual growth in Connected Trucks thru 2022 (Frost and Sullivan) and growth in RFID tags from $12 million to $209 BILLION by 2021 (McKinsey).

The impact of these trends will be huge – organizations will soon be awash in all the real-time Big Data necessary (coming from devices, sensors, vehicles and long histories of operational transactions) to transform their supply chains. The question is not WHETHER this will happen – it’s what impact this trend will have on your company and, more pointedly, what will your organization do about it in 2017?


I have already witnessed several examples of real-time, connected supply chain processes being implemented across all pillars of the extended supply chain: Design, Purchasing, Manufacturing, Distribution and Marketing & Sales.

In Design: leading companies are increasingly leveraging vast volumes of Social Big Data to understand product requirements, “math-based” Big Data to drive virtual and 3D printed prototypes and sensor Big Data to drive digital test simulations.

In Purchasing: enterprises are analyzing long histories of sourcing event data to identify exactly those variables (i.e. time of day or year, number of suppliers invited, energy costs) that led to the lowest cost sourcing outcomes – and then quickly applying this knowledge into current day sourcing practices.

In Manufacturing: practitioners are collecting and analyzing shop floor sensor Big Data to monitor real-time operational performance, discover optimal process parameters to maximize quality and yields and predict optimal maintenance intervals for equipment.

In Distribution: professionals are increasingly analyzing logistics Big Data (i.e. GPS, RFID, traffic, weather) to dynamically re-route trucks and optimize the design of their distribution networks.

In Marketing & Sales: real-time analysis of demand Big Data (i.e. Social, web logs, POS, consumer location) is providing the ability to understand and predict consumer needs and actual demand, while analysis of long histories of marketing campaign data is providing the ability to identify the key marketing variables driving effective marketing outcomes.


With Big Data impacting so many Supply Chain processes, Hortonworks customers often ask me where to begin. I suggest the following steps to start:

#1 – Align Big Data to Your Company Objectives

There is no easier way to gain approval for Big Data initiatives than to align with your company’s objectives. For example, if increasing revenues is a high priority, consider a Design or Marketing & Sales related Big Data use case. Conversely, if cost reduction is a major focus, consider use cases across the purchasing, manufacturing or distribution domains.

# 2 – Identify Your “Line of Business” Champions

I have found that Technology Architects, however enthusiastic they may be regarding Big Data technologies, often have difficulty getting supply chain initiatives approved within their companies. The advice I offer them is to identify and join forces with business process owners who can serve as champions for Big Data transformation projects moving forward. Without such line of business support, selling Big Data transformations is an uphill battle.

#3 – Start with Visibility into Your Supply Chain

While huge, transformational supply chain Big Data programs are sexy, they can be difficult to sell – so start small, with more digestible and realistically scoped projects.

First, identify the state of your data. How accessible is it? A great initial step involves creating a “Data Lake” that will support future supply chain transformation initiatives. Without your ecosystem data under management, it’s difficult to move on.

Next, don’t jump to the most complex use cases immediately. The old adage of “walk before you run” applies beautifully in this context.

Supply chain use cases generally range from visibility-related (simpler) to optimization-related (more complex) examples. Often, just gaining basic VISIBILITY to supply chain information (i.e. process monitoring or inventory location tracking) can provide immense value, without the need to resort to more complex OPTIMIZATION use cases (i.e. quality/yield optimization or predictive maintenance).


In short, I see the influx of real-time Big Data and high-performance analytics enabling new levels of supply chain performance, underpinned by supply chain visibility, performance monitoring and the ability to optimize current and future actions based on lessons learned from the past. Industry leaders are already building, evolving and benefiting from their Big Data foundations – so don’t wait, the race is on!

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