Analytics in supply chain and logistics

Supply chain and logistical activities are relying on analytics to gain competitive advantage. Previously, supply chain configuration was focused on dedicated markets serving loyal customers. The transition has occurred in recent years from buyers market to sellers market. Henry Ford on production of famous T-model said that he made “any color so long as it is black”. The mere suggestion indicated that people have dearth of options for cars and color is not something that they are concerned about. Well, that thought would not stand in today’s world which is characterized by competitive environment encompassing several players. Supply chain has traversed a long journey in the last century and the growth has increased multi-fold in last decade. There are several factors that are responsible for growth such as technology, customer awareness, internet, diversification, globalization etc.

Google search for analytics would yield several areas of the domain ranging from Google analytics, business analytics, web analytics, data analytics, supply chain analytics and so on. Supply chain uses analytics in forms suited for decision making at different phase. Strategic decision making for Greenfield projects with focus on identifying site location and future growth predictions. It would use qualitative inputs that would govern potential locations by evaluating future tariffs, logistics service providers (LSPs)/3PLs, geopolitical stability, supplier proximity etc. It would also heavily depend on product attributes, distribution network, future demand (with uncertainty) and target market. Commonly, data analytics is heavily used for analyzing past data and identifying growth opportunities which may not be limited to shipment pattern, load optimization, heavy lanes, service level, fill rates and others for base scenario and To-be scenario. Product attributes classifying them under volume and weight criteria under following categories:

·         High volume – Low weight (e.g. cigarettes)

·         High volume – High weight (e.g. heavy machinery)

·         Low volume – Low weight (e.g. plastic pins)

·         Low volume – High weight (e.g. metal screws)

Primarily the product categories is based on density, but can be elaborated based on above attributes. Further, life cycle and usage of product classifies them into fast moving, medium moving and slow moving products. FMCG are fast moving products with shelf life limitation, while high annual usage value products are not stored in the inventory, to smooth cash flow.

Finally analytics is also used in other areas such as route planning, inventory planning, supply planning, demand planning and network design. This is not an exhaustive list, but it is impossible to cover all areas in supply chain domain that utilize analytics. Analytics is evolving with the impetus of technological innovation, such as IoT, Big Data, cloud storage and offers complementary benefit in utilizing them for continuous improvement of supply chain.