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Predictive analytics for supply chain

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Predictive analytics for supply chain: Future Roadmap


The Supply Chain of any company is an incredible source of data, data about your customer, business and operations involved. By putting data in proper use can exponentially help the enterprise to increase profit and conclude exactly what a customer wants.

Big Data Predictive Analytics (BDPA) in Supply Chain Management (SCM) is counted as significant technology which helps to extract information from company’s historical data, analyze it, and identify the issues and opportunity hence leading the enterprise to form a predictive approach towards a decision. Google, Netflix & Amazon are some companies using predictive analytics and gained a significant shift in their overall process optimization. Research conducted by Gartner shows that companies embracing predictive supply chains achieve a high return on their investment. Additionally, they can cut inventory by between 20-30% thanks to more accurate demand predictions. As per CIO magazine, the market is expected to reach a total value of $10.95 billion in the next few years.

Predictive Analysis answers some of the basic questions like:

  • Are the company’s forecast tracking with actual sales.
  • Which product needs to be produced more.
  • What stock should I hold and where should I position it.
  • What, when, and where should I ship.
  • Which supplier can achieve lowest landed cost and with shortest lead time.
  • Which vendors can be trusted for internal growth.
Importance of predictive analytics in Supply Chain Manageemnt:

  • Price & demand forecast:Machine learning algorithms dig through data collected from different resources to make more accurate price predictions. Most businesses depend on the pricing approach “seems the right price” and hence making some major decision mistakes. Data helps you to know easily which consumers are more likely to purchase and how much value they have created around the product, thus impacting the demand. Demand and supply laws interact to determine the actual market prices and volume of goods that are traded on a market, with predictive analytics combining company data and economic information, a firm will know what and why of the occurrence.
  • Procurement planning:Predictive analytics can be used to forecast the needs of particular order, department and vendor. The benefits of analysis allow the procurement team to plan ahead to consolidate similar requirements and communicate the vendor on purchase cost hence making spend analytics more predictive. Predictive analysis studies global events and shield procurement against any type of risk.
  • Inventory optimization:Predictive analytics assess demand and plan better, the useful information gathered helps in taking further merchandising decisions, it predicts to combine all sales channels and locations so that every product, order and consumer can be managed in a single place. Predictive analytics helps to create buffer variability in demand and supply hence reducing the forecast error or any deviation of demand/supply, optimizing safety stock.
Here are a few examples of how organizations are making use of predictive analytics:

  • Automotive industry:Big data analysis is used to strengthen customer satisfaction, quality control and safety management. Getting hold of big data, predictive analytics applications can accurately identify people most likely to purchase a vehicle in future based on colour, design and features.
  • Mining and metal industry manufacturing:Predictive analytics plays a significant role in optimizing and controlling the process variables to manufacture products with minimum rejection, high quality, low throughput time, and better efficiency.
  • Chemical industry:Equipment of the chemical industry is connected with sensors which carry a continuous data stream hence providing as relevant information, by analytics, chemical industry companies can precisely forecast the load of raw materials such as oil, natural gas, water, metals, minerals, to help them meet power and energy demand/supply.

Conclusion:

The next big thing in supply chain business intelligence is the proactive nature of the predictive analytics strategy. The use of Artificial intelligence (AI) and predictive analytics will dramatically improve supply chain operations, optimize pricing strategies, manage inventories and improve overall operations and enhance supply chain management. The firm uses predictive software to increase profitability and also to prevent procurement fraud.

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