Last updated on November 25, 2016
The global operating systems, growing customer expectations, and pricing pressures have made traditional supply chains with multiple employees to look archaic. Critical supply chain decisions, such as forecast optimization, demand planning, inventory optimization, and deployment strategies, can fructify only with in-depth insights of production, sales, and logistics data. Supplier networks need contextual intelligence to compete with accuracy, speed, and quality.
Understanding the real value of data and its relation to customers, products, markets, competition, people, and performance helps create competitive edge. Advanced analytical tools enable a deeper analysis of supply chain data for timely action.
Predictive analytics comes in handy in supply chain operations. On the strength of computational mathematics, statistics, neural computing, robotics, machine learning, and artificial intelligence techniques, predictive analytics reveals meaningful relationships and patterns rooted in the large volumes of data to predict behavior and events.
Predictive analytics helps businesses better understand customer behavior and anticipate problems well in advance. It compels businesses to focus on what ‘will’ happen—not on what ‘has’ happened. No doubt, in this competitive age, sustainability of a business depends on its predictive capabilities.
Why predictive capabilities in supply chain planning?
Demand and inventory management is a big challenge for businesses due to the globally dispersed customers across different geographies. Product modifications to fulfill the demands of dispersed customers further adds to the problem. Customer-oriented industries, such as consumer goods, retail, and automotive, need real-time forecast to better serve customer expectations. Predictive capabilities present accurate, anticipated demand and monitor supply and replenishment status, thereby ensuring better inventory plans and flow of goods and services.
How predictive capabilities can pay dividends?
- As a result of measured individual efficiency, schedulers and dispatchers can predict the duration of a task. They can focus on high-risk tasks, taking into account potential fluctuation in traffic during the day.
- Planners can make necessary arrangements to cover the lacuna in capabilities/capacity.
- Real-time critical events and key performance indicators (KPIs) can be monitored through multiple touch points via advanced analytics-driven ‘control metrics’. Coupled with predictive analytics, those metrics can provide valuable savings in freight optimization of customers. Organizations can improve responsiveness, optimize cost, and minimize customer impact.
- As it enables analysis of internal and hybrid data, firms can weigh their strengths and weaknesses, and make adjustments accordingly in terms of their capabilities and inventories in real time. They can derive the benefits of the insights at all levels of the supply chain.
- It enables field staff to analyze situations and notify customers.
- Unseen patterns can be identified to target campaigns, promotions, and offers.
- Using churn models, you can determine customers most likely to churn next month and the reasons for their exit.
- You can measure the lifetime value of customers to improve sales forecasting and profitability.
- You can gauge customer sentiments to spot emerging trends.
By allowing businesses to take their services to the next level, predictive capabilities ensure superior customer experience. Better prediction ensures better returns on investment, increases the probability of additional sales, ensures more satisfied customers, and better-fit employees. The reality is that business processes gain incremental improvement with predictive capabilities.
Recollecting
Predictive analytics comes in handy in supply chain operations. On the strength of computational mathematics, statistics, neural computing, robotics, machine learning, and artificial intelligence techniques, predictive analytics reveals meaningful relationships and patterns imbibed in the large volumes of data to predict behavior and events.
Be First to Comment