While Big Data has certainly permeated nearly every aspect of today’s manufacturing and supply pipeline, some industry analysts still question the validity, value proposition, and staying power of Big Data for companies as they strive to streamline their operational platforms and leverage lean manufacturing principles for optimal productivity and profitability.

Competition. More so than ever before, today’s manufacturing companies are facing increasingly intense competition in an ever-expanding global marketplace. With new and emerging markets coming online in disparate parts of the globe, today’s manufacturing and supply network is growing more diverse, complex, and intertwined, and manufacturing companies need more powerful, integrated, and intelligent solutions to cut this complexity and maintain the efficacy of their planning and production programs.

Enter advanced analytics and its ability to give manufacturing companies the insight and visibility into their overall supply and production platforms. With an endgame of helping manufacturing companies analyze and sort data, streamline processes, and increase the efficiency of planning and production programs, advanced analytics is a critical value proposition in a variant-rich industry where network partners operate at numerous disparate points across the globe. Because advanced analytics relies in large part on the adoption of intelligent technologies or solutions like Industry 4.0, Big Data, and The Internet of Things (IOT), planners and managers have the ability to put large amounts of data and reporting to work to leverage lean manufacturing principles for greater derived value.

The more chatter within a certain community about any given subject, the greater likelihood misinformation will creep into the conversation, thus distorting the truth or value about any given topic. This is especially true in global supply chain management when it comes to advanced analytics and how manufacturing view advanced analytics as part of their supply chain logistics.

As discussed in previous entries, today’s manufacturing insiders and analysts define advanced analytics as as the ability to glimpse into the future to cut the complexities of the global supply chain is key to unlocking the value of this reporting model. Operating in two distinct categories — predictive (risk management, planning) and prescriptive analytics (transportation routes, inventory management) — advanced analytics removes much of the guesswork that was at one time associated with making informed decisions about demand planning and production programs.

Today’s integrated supply chain is based on data. Big Data, advanced analytics, S&OE reporting, and other forms of information are the vehicle which drives planners and managers to execute planning strategies and platforms for the best overall results both in productivity and profitability. But heaps of data without any real concrete plan for analysis or evaluation is essentially like having a detailed map without any real sense of where you need to go. Without any sense of direction or end destination, even the most advanced map will be of no use in helping you complete your journey.

As such, one of the key differentiators for manufacturing companies in today’s global manufacturing landscape is data analysis and how companies leverage insights from this analysis to drive more efficient planning and production and create stronger links between various parts of the production sequence. Especially for manufacturing companies in variant-rich fields like the automotive industry, data analysis not only gives companies a 360 degree view of their overall production and supply situation, but it also helps foster three of the most important factors in global supply chain management: visibility, agility, and flexibility.

The introduction of advanced analytics into today’s global manufacturing pipeline has provided planners and managers with a number of critical value propositions in leveraging enhanced reporting and metrics for reaching business outcomes and goals. Yet one of the most valuable applications of advanced analytics exists not on the side of revenue and profitability, but in the realm of reducing the risks involved with administering a global supply network.

Because so much of today’s manufacturing industry exists in a variant-rich environment with network partners spread over disparate parts of the globe, the likelihood for significant disruptions or bottlenecks in the supply stream is ever-increasing. With a reliance on intelligent, integrated technology platforms and movements like Industry 4.0 and Big Data, advanced analytics gives manufacturing companies the capacity to review large pools of data in real-time to make critical adjustments to planning and production programs to avoid costly breakdowns across key areas of the value chain.

Fact or fiction. Trend or mindset. Fad or fixture. While Big Data has certainly permeated nearly every aspect of today’s manufacturing and supply pipeline, some industry analysts still question the validity, value proposition, and staying power of Big Data for companies as they strive to streamline their operational platforms and leverage lean manufacturing principles for optimal productivity and profitability.

First introduced to the manufacturing and supply chain landscape in the early 1990’s as a method of grouping, sorting, and analyzing large and complex data sets into executable actions. The sorting of these large, unstructured datasets gives manufacturing companies the capability to apply predictive analytics and other forward-looking logistic strategies to increase the efficacy, efficiency, and cost-effectiveness of planning and production programs.  

The metaphorical toolbox supply chain planners and managers have at their disposal is getting bigger and more diverse. This is due in large part to the complex nature of today’s global manufacturing and supply pipeline, but also because manufacturing companies must work tirelessly to remain efficient and growth-oriented in a competitive business landscape. Reducing operational costs and enhancing profitability is part and parcel to these efforts, and one of the most effective tools companies can use in this effort is advanced analytics.

With an endgame of helping manufacturing companies analyze and sort data, streamline processes, and increase the efficiency of planning and production programs, advanced analytics is a critical value proposition in a variant-rich industry where network partners operate at numerous disparate points across the globe. Because advanced analytics relies in large part on the adoption of intelligent technologies or solutions like Industry 4.0, Big Data, and The Internet of Things (IOT), planners and managers have the ability to put large amounts of data and reporting to work to leverage lean manufacturing principles for greater derived value.

How can organizations get the most out of their Sales and Operations Planning (S&OP) efforts? By establishing a separate Sales and Operations Execution (S&OE) process that focuses on short-term performance and supports the long-term goals of S&OP. While the S&OP team usually looks ahead by up to 18 months, the S&OE team concentrates on the next zero to three months. 

With all the talk around advanced analytics, it’s easy to see the concept as something of a buzzword rather than a key driver in pushing today’s automotive supply chain into tomorrow and beyond. This can be attributed to so many players across the global automotive industry failing to understand the wide-ranging applications of advanced analytics and the benefits thereof.

Understanding advanced analytics as the ability to glimpse into the future to cut the complexities of the global supply chain is key to unlocking the value of this reporting model. Operating in two distinct categories - predictive (risk management, planning) and prescriptive analytics (transportation routes, inventory management) - advanced analytics removes much of the guesswork that was at one time associated with making informed decisions about demand planning and production programs.

With this fundamental understanding in-hand, it becomes easier for planners and managers to realize the benefits of advanced analytics and the ways in which advanced analytics can be deployed for enhanced productivity and efficiency.

“Is a machine going to take my job?”

In today’s increasingly digital, automated world, it’s a sentiment you hear uttered throughout a number of global industries. While this is perhaps less the case in the automotive industry where robotics and automation dominate the manufacturing stage, it’s a question many planners and managers ask when it comes to advanced analytics and its ability to significantly reduce the amount of human intervention necessary across a number of touch points of the demand planning and manufacturing chain.