Sunday, January 26, 2014
Unleashing analytics collaboratively to optimize inventory
This week's reading show us the importance of matching supply with demand and optimizing your inventory levels to maximize competitive advantage. Often organizations are faced with problem of determining the right levels of inventory and the getting the right mix among raw material, work in progress and finished goods inventories. Predictive analytics, a powerful mechanism, comes to the rescue.
As highlighted in the readings, the production, finance, sourcing and marketing teams need to work together; a good analytics solution takes in raw data from all these departmental systems/ERPs and predicts demand. This collaborative approach provides a single source of truth for users (forecasters) to base their numbers and rules out any chances of data inconsistency. The predictive model looks at historical data, seasonal conditions and other factors to adjust the predicted demand. The below video describes how IBM implemented an analytics solution for Columbus Foods, a company dealing with perishable goods, to increase their revenue and optimize inventory:
The complete case study can be found here: http://public.dhe.ibm.com/common/ssi/ecm/en/ytc03438usen/YTC03438USEN.PDF
The performance of the analytics platform and the frequency of data update depends on the industry. For example, a cement company may not worry about real time data; however, for a garment company real time updates are more significant. The latter may also source its data from social media and other public forum to understand the customers' sentiments.
Recently, Guess worked with HP Vertica to empower its designers, store manager, buyers and planners to serve their customers better. Click here, to see how analytics played a key role in inventory management.
Procter & Gamble (P&G), often rated as the company having one of the best five supply chains, seems to have mastered the art of leveraging analytics. Their analytics platform factors in an optimized distribution network while setting the targeted inventory levels across various touch-points in the network (distribution centers). This is improving P&G's bottom line by multi-million dollars since 2006.
Many more case studies where companies have implemented an analytics solution for better inventory management can be found at IBM's website