Big Data

Supply Chain

Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time. Our Data sciences solutions, using Machine learning, makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.

  • Spend Analysis: Analysis of spend data to identify cost saving opportunities. This is carried out at an aggregated/ vendor/ SKU level to create a sharper view. Bigger organizations that engage multiple vendors could also get a benchmarked view of the relative strengths and weaknesses of the vendors in addition to just looking at cost. As a secondary benefit analysis of this data along with simulations could help negotiation effort for new sourcing.
  • Active Supplier Management: As an ongoing effort analysis of supplier data on service levels (timeliness, quality, etc) in addition to cost help firms ensure tidiness in its supply chain. This kind of analysis package becomes a supporting package for review discussions and ensures risk arising out of supplier non-performance is prevented.
  • Demand forecasting: Accurate demand forecasting is in the core of the planning. Using a combination of statistical and judgmental techniques firms can make a reasonably accurate forecast of the demand (+- 5%) at an aggregated level as well as at SKU level. With “demand forecasting” being offered as an ongoing service, the chances of forecasting models losing its power or relevance never fades away.
  • Sourcing Optimization: With an eye on cost (price), control, and risk firms leverage internal as well as external (commodity price, external news, etc) data and information to effectively optimize sourcing. This involves aspects like supplier risk management, spend analysis, best time to buy, benchmarking, vendor rationalization and stratification to make it an ongoing practice.
  • Production Management: With an effective forecasting of demand and subsequently optimized sourcing, the next logical area is to focus on effective production management. This involves techniques like capacity planning, improved asset utilization and reduced idle time, quality assurance (descriptive and predictive error detection), and root cause analysis to uncover is something goes wrong. The analytical techniques involved on this could be simpler insightful visualization to predictive modelling.
  • Inventory Management: Identifying inefficiencies in inventory management could result in significant cost saving, both direct and indirect. Direct cost optimization would entail analysing storage cost/ capacity, time horizon, ageing and replenishment, and wastage to bring to management’s attention the areas of opportunity in managing cost. On the other hand, indirect cost would entail looking at lost revenue because of unavailability and locked in working capital to recommend more avenues to optimize the same.
  • Distribution and Logistics: Network optimization