Big Data Integration with Supply Chains

Change is the new constant. The confluence of people and technology has given rise to a myriad of possibilities for addressing issues surrounding mankind’s sustainability. There is a tectonic shift in technology that the world is experiencing today with the emergence of the Internet of Things (IoT) and Big Data analytics that have realized some of the distinct asks that once existed only in science fiction. With the proliferation of data from people and processes, it has become imperative to analyze it and extract insights.

The manufacturing industry has been a forerunner in adopting new processes and technologies— from Kaizen, JIT, and lean principles to robotic automation—which have now become the essence of every organization. One of the key elements in ensuring long-term sustainability is continuous improvement. This has ushered in a new era of manufacturing called Industrie 4.0.

Industrie 4.0 or connected manufacturing calls for integration of cyber physical systems (currently working in silos) to form an enterprise where data acts as a cohesive force between processes to ensure optimization and maximize throughput. Today, data generated from such systems are analyzed independently to glean insights from a particular process or operation. Big Data and analytics will play a key role in the convergence of information technology (IT) and operational technology (OT) systems, highlighting the true value of Industrie 4.0. Removing factors hindering data transfer amongst different processes (for example, maintenance to supply chain to purchase to finance) can lead to better decision-making. Let us discuss one such critical enterprise process: the supply chain.

Traditionally, the aim of any supply chain was to get the right product in the right place at the right time. With the application of data analytics, the digital supply chain has taken a new definition. The objective now is to predict the right product, forecast time-to-order, and ensure availability at the right place before time.

One of the challenges impeding existing supply chains is the lack of strategic planning and forecasting due to the limited buyer-supplier relationship. Moreover, there are huge volumes of static data pertaining to materials, spare parts, and more, that remain unutilized in enterprise resource planning (ERP) systems. This needs to be combined with historical maintenance and part replacement records to provide valuable insights.

With the rise of digital technologies, newer systems are more agile, easy to use and configure, and dynamic, turning legacy ERP and supply chain systems into bottlenecks. Today, companies are moving towards Cloud-based ERP systems that can be globally accessed. Big Data technologies can be deployed to effectively use legacy data and derive real-time insights into day-to-day operations.  

Some of the ways in which digital technologies can optimize supply chain management (SCM) are:

Improved flexibility, scalability, and depth of data: There are ample number of variables that highlight the ever-increasing volume, variety, and velocity of various data sources in the SCM ecosystem. Analyzing these to gain actionable insights therefore becomes critical. Here’s where Big Data analytics will act as a catalyst in providing contextual intelligence through SCM data. This will lay the foundation for a more robust, collaborative supplier network on a single platform that can add value over and above regular transactions.

Reducing risk and increasing speed with accurate forecasting and prediction: Integrating Big Data analytics for business planning can optimize operations, demand forecasting, and risk mitigation, among others. This will act as a cornerstone for fueling long-term change management.

Consolidating and enhancing delivery networks using geoanalytics: Applying Big Data analytics to SCM across geographies can help organizations improve reaction time to issues, increase efficiency, and enable greater integration.

Be it a product or service, most enterprises are moving towards adopting outcome-based metrics and performance management systems to assess people and processes. With emerging technologies and expanding storage infrastructure capabilities, attributes like flexibility, agility, and rapid turnaround have become the default standard for SCM systems.

Companies that are hesitant to embrace change will become obsolete. Let us assess a real-world scenario that most manufacturing organizations currently face. It is associated with integrating certain systems within SCM with advanced features. To be more precise, it requires combining predictive maintenance with deep learning and artificial intelligence (AI) capabilities to enable efficient decision making.

Take the case of a leading technology company that developed a powerful cognitive analytics platform capable of thinking like a human brain. Integrating a platform with cognitive computing capabilities enables a transparent, intelligent, and predictive supply chain. After all, 65% of the value of a company’s products or services is derived from suppliers. Suppliers and the supply chain impact everything from the quality, delivery, and cost of a business’s products and services, to customer service and satisfaction, and ultimately profitability.

Another survey reveals that the greatest hurdle in meeting supply chain goals of an organization is the lack of visibility and transparency. Current manufacturing operations including predictive maintenance activities are carried out in silos, and predominantly focus on asset data (IT). In fact, almost all ERPs provide inbuilt business intelligence (BI) based on descriptive analytics. With such a solution that focuses on data from sensors and machines (OT), predictive maintenance is redefined, ensuring higher prediction accuracy.

I recall a statement from a TED Talk on data science, “Data is not the new oil, but it is the new soil.”  While I do believe data is the new soil, it needs to be harvested correctly to reap maximum outcomes and answer some of the toughest questions plaguing the industry.