Supply Chain

Data Analytics is Redefining Strategic Visions of Supply Chains

Suppliers have become strategic partners in the success of their customers, which means technology must play a key role in value creation. Advanced analytics can close the gap between data and decision-making, by providing critical and otherwise unavailable insights. — By Cecil Parang

Supply chains have moved to a strategic and integral position in business, and the move has been largely driven by technology. For the supply chain to be strategic, especially on a global basis, the suppliers in the supply chain must step up their utilization of digital technologies. Once purely focused on production and shipment, the strategic supplier is increasingly focused on generating value by closing the gap between data collection and decision-making.

Between these two efforts are insights based on data analytics, but even producing analytics is not enough to maximize the ROI in the technologies. Suppliers must identify those types of analytics that present the most valuable information for making decisions intended to grow the business. Investing in technologies that produce data analytics which can be turned into business value is the key to accelerating business value.

Unless data analytics are meaningful and can be leveraged in decision-making, their production is more of a proforma exercise than a strategic process. Digital supply chain solutions that maximize ROI are focused on helping businesses make decisions based on numerous factors, including customer needs, global risks, partner networks and internal cross-function needs.

Focusing on Holistic and Advanced for the Dynamic Supply Chain
Thanks to technology, it is not difficult to produce advanced supply chain analytics today. The keyword is “advanced” because these are analytics that are forward-looking and actionable. They can predict what will happen and assist with decision-making about things like future needs in production, inventory and logistics. However, it is prescriptive analytics that enable optimal supply chain decisions.

Suppliers who use predictive analytics can perform scenario analysis of what-if situations, capacity planning, operational planning, and what the tech company Riverlogic calls “digital planning twin.” This concept begins with constructing a mathematical business model that presents an accurate picture of how the organization functions and uploading organizational data to solve various scenarios. A digital planning twin creates a dynamic model, where the model is continuously updated with real-world data. This enables reacting in real-time to unexpected situations, minimizing business risks and enabling more rapid decision-making.

Technologies can close the gap between data and identifying/responding to optimization opportunities.
SDI is a supply chain as-a-service provider for the MRO (maintenance, repair, operations) supply chain. The company offers a new digital-based holistic approach to MRO that connects typically fragmented departments and functions with no central accountability, enterprise-wide visibility or consistency. To compete on a global scale, companies must ensure their direct, indirect and MRO supply chains are coordinated, and that is only possible via expert use of technology to manage supply chain operations end-to-end, rather than as a category procurement event (which usually excludes MRO). This applies to suppliers in the supply chain and corporations.

Lost value from excluding MRO in a fragmented supply chain analytical system includes unplanned downtown, stock outs, lack of control over spend, obsolete inventory and no spend visibility, to name a few. The SDI supply chain SaaS improves data quality, which leads to impacts like improved management decisions, greater control over end-to-end processes, and cost reductions. However, it also broadens the global capability of procurement, provides market-oriented insights, and brings innovations to operations. The SDI technology platform is plug-and-play and, as with most modern technology, continues to evolve.

Supply Chain Optimization via Advanced Analytics
Another type of advanced analytics is cognitive analytics, in which natural language is used to answer complex questions. Artificial intelligence (AI) is a cognitive technology that can understand, reason, learn and interact like humans. Cognitive technologies are increasingly used for supply chain optimization because of its ability, per IBM, to use enormous amounts of “data to forecast, identify inefficiencies, respond better to customer needs, drive innovation and pursue breakthrough ideas.”

As the tech giant IBM explains, when blockchain is combined with AI and Internet of Things (IoT), the supply chain optimization process delivers value by eliminating silos through end-to-end visibility, meeting customer expectations, providing real-time decision support, increasing supply chain agility to respond to change, and improving environmental and social impacts.

The digital supply chain has blockchain that brings together data across partners, adds AI to derive a meaningful context from the data and to generate insights, and uses IoT as an interface between the virtual and physical environments. The optimized supply chain offers visibility into inventory and inventory tracking, real-time intelligence, order management, and reporting and analytics that evaluate patterns in processes to forecast future demand and sales. The value is also found in improving price performance and procurement supplier relationships.

Path to Implementing Advanced Digital Technologies
Technologies can close the gap between data and identifying/responding to optimization opportunities. However, the complexity of the technologies can be overwhelming for suppliers.

Global software product developer Altexsoft offers a path for implementing a system for supply chain analytics that closes the gap between data and decision-making. The steps begin with identifying the business problem and specific objectives, followed by developing KPIs and a method for tracking and measuring them. Carefully defining the data sources is crucial because analytics can only be meaningful when the data is coming from the right sources. For example, if meeting customer expectations is an objective, the data sources will need to include eCommerce platforms, customer support and a customer relationship management system, often left out of supplier systems focused on production and shipping.

There are internal and external data sources. Internal data includes customer-focused data and operational data like logistics, warehouse management, manufacturing data, and so on. External data comes from outside research, B2B integration platforms, performance of partners and competitors, customer feedback, and so on.

The next steps are assembling the data team, creating a data-driven culture that includes data sharing and data-based decision-making, and using existing analytical capabilities. Next is identifying how the supplier moves forward in the use of data analytics for decision-making. The final step is creating the customized data architecture solution needed to produce granular actionable insights from the data, developing a system for accurate forecasts and predictive scenarios, and making data-driven decisions designed to grow the business.

Fully utilizing digital technologies in supplier operations and supply chains is a complex process, but there is no doubt it offers large business value. Done right, closing the gap between data and decision-making is mandatory for domestic and global competitiveness.