Data abounds. Technologies have grown, but insight is limited.  Computing power capabilities double every two years (Moore's Law), but we are still using the definition of the traditional supply chain planning hierarchy defined by the Advanced Planning Solution (APS) vendors in the 1990s.  While companies have successfully implemented hardware and systems to meet the challenges of structured data in transactional systems, three opportunities remain:

Decisions that you need when you need it:  In-memory Processing. Computing power has increased 100X; yet, we are still using the traditional supply chain planning definitions of the 1990s.  This is changing.  The translation of tactical planning (demand and supply planning) into operational execution was traditionally defined by fixed and inflexible rules in and out of  Order Management to Distribution Requirements Planning (DRP) and from and to procurement into Materials Requirements Planning (MRP).  Historically, this was extended into order translation into Warehouse Management and Transportation Management.  SAP's launch of HANA for in-memory processing will start to redefine new approaches for bi-directional translation of tactical planning into operational execution.  In-memory processes will strengthen the move to define horizontal processes that can extend from customer's customer to suppliers' supplier.  This will be coupled by the evolution of natural language processes and rules-based ontologies. These technologies will converge to improve supply chain planning flexibility.

Flexibility:  Sense before Driving a Response. Market changes abound, but supply chain planning drives a response from historical - often distorted - data.  Big data supply chains - exabytes and zettabytes of supply chain data - fueled by the growth in supply chain sensing technologies of geo-location, weather, supply chain disruptions, product lifecycle, channel sales, supplier risk - coupled with the growth in unstructured data through social and channel networks can change this.  Instead of a fixed response based on historic data, the supply chains can sense before responding.  To improve supply chain flexibility, traditional supply chain planning structure and footprint will be redefined over the next five years by systems that sense before responding.  This new form of predictive analytics - rules-based ontologies - will evolve into learning systems with rules redefinition based on near real-time shifts (as opposed to one-to-one fixed rules mapping of traditional supply chain planning systems) enabling the redefinition of supply chain processes from inside-out to outside-in (from enterprise out to market channel in).

Harnessing the Power of Unstructured Data. Unstructured data is proliferating and is largely unused in today's supply chains.  It exists in customer service notes, channel and distributor networks, social technologies, mobile applications, warranty systems, and market-execution systems, but the traditional supply chain responds only to transactional or structured data, turning a deaf ear to the growing volume and richness of unstructured data.  In the next five years, the use of sentiment analysis in listening posts and the building of flexible rules-based ontologies to listen and then act will redefine supply chain processes.

                                      The Outlook

The use of sensing technologies and advanced analytics will redefine supply chain planning over the course of the next five years making current supply chain planning architectures obsolete.


Keywords: SC Planning & Optimization, Forecasting & Demand Planning, Business Process Management, Business Intelligence & Analytics, Asset Management, Technology, Business Strategy Alignment, Quality & Metrics, Supply Chain Analysis & Consulting, Global Supply Chain Management, Moore's Law, Traditional Supply Chain Planning Hierarchy, Advanced Planning Solution, APS, In-Memory Processing, Order Management, Distribution Requirements Planning, Materials Requirements Planning, SAP, HANA, Rules-Based Ontologies, Unstructured data