Supply Chain Management How Analytics

Supply Chain Management

How Analytics Are Transforming

Supply Chains Into Demands Driven Supply Networks

How Analytics Are Transforming

Supply Chains Into Demands Driven Supply Networks

The pervasive use of analytics throughout supply chains are leading to more efficient decision-making frameworks, and the ability to define multi-attribute decision making models that ensure high accuracy and reduced costs (Wang, Huang, Dismukes, 2004). Greater reliance on analytics and real-time data updates are making it possible for supply chains to also streamline the more complex and highly synchronized strategies including the Sales & Operations Planning (S&OP) processes that ensure forecasts reach suppliers in time so they can react with correct production and order fulfillment (Schlegel, Murray, 2010). The real-time aspects of supply chain analytics are also serving at a much more aggregate level of create entire networks that can responds more quickly and with greater accuracy to customer demands. Gartner Inc., a leading Information Technologies (IT) research firm has called this a Demand Driven Supply Network (DDSN) in that all suppliers are synchronized to a common set of metrics and key performance indicators (KPIs) that indicate relative shifts in demand for products over time. A DDSN network can over time create a highly effective knowledge sharing network, which is exemplified in the accomplishments Toyota has had with their Toyota Production System (TPS) (Dyer, Nobeoka, 2000). What is so unique about this approach to defining supplier coordination is that the TPS stresses cross-supplier knowledge sharing so the entire base of suppliers globally share a common set of assumptions, data sets, information platforms and together, a strong assumption base on which to base decisions on. Toyota creates DDSNs capable of transforming product knowledge competitive advantage over time (Dyer, Nobeoka, 2000).

Analysis of How Analytics Are Transforming

Supply Chains Into Demands Driven Supply Networks

The ideal supply chain is so well integrated to the demand management aspects of an organization that when an order is forecast the entire chain of suppliers anticipated and responds to future requirements, often predicting far in advance what customers will need (Wang, Huang, Dismukes, 2004). The many frameworks in sue throughout supply chain management concentrate on this aspect of real-time data integration and pervasive use of metrics and key performance indicators to track supply chain performance. Over the last three years the process area getting the most focus from a measurement standpoint is Sales & Operations Planning (S&OP) (Schlegel, Murray, 2010). This process are is heavily relied on for ensuring the sales demand management, forecasting, support and services are all coordinated towards a common forecast and series of operating plans with suppliers (Schlegel, Murray, 2010). The S&OP process at a very strategic level is relatively easy to accomplish, yet once deconstructed to the individual product and process level, becomes inordinately more complex and requires the development of a network-based structure (Barrett, 2007). S&OP is also the most highly orchestrated process within supply chain management, often requiring metrics and key performance indicators (KPIs) presented on dashboards showing relative progress towards shared supply chain goals and objectives.

The reliance then on the DDSN framework necessitates that the S&OP process be one that is highly quantified (Schlegel, Murray, 2010). This explains why analytics has become so pervasive throughout supply chain management (SCM), supply chain planning (SCP) and the optimization of entire supplier networks (Wang, Huang, Dismukes, 2004). As studies also indicate what metrics get measured most within an organization most dictate its culture, it is entirely commonplace to find entire supplier networks becoming much more attuned to accuracy, agility and speed compared to their non-measured counterparts (Dyer, Nobeoka, 2000). There is also the added dynamic of supplier networks becoming learning platforms or organizations, where processes are created by suppliers and other members of the supply chain, to further enhance and accelerate knowledge transfer (Dyer, Nobeoka, 2000). This dynamic is more than an experience effect or network effect, as it is multiplicative across the many members of the supplier network, in effect creating an entirely new platform for sharing knowledge and information. The reliance on analytics for creating the necessary integration links and platforms for decision making also dominate this phase of maturity in any supply chain network (Wang, Huang, Dismukes, 2004).