Posted on July 15, 2019

How Big Data Will Create Leverage for Supply Chains

Ally Pulskamp
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Ally Pulskamp

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Fragmentation

The Big Impact of Big Data on Supply Chains

There is a tremendous amount of untapped information that can be leveraged as supply chains become more complex and fragmented. That ‘hidden’ information can be extracted from data that already exists within the OEM and/or resources outside its walls in the form of ‘Big Data’. Big data is used to understand current, and calculate future behaviors and preferences of customers. 

Companies who tap into available big data expand their traditional data sets with historical trends, browser logs, social media data, sensor data and outside data resources to get a more complete picture of customer past behaviors in order to gauge potential future trends.Construction equipment OEMs can realize tremendous opportunity in using big data in their parts supply chain to optimize processes and increase their ROI. Flash Global, an end-to-end global supply chain services company has outlined Seven Ways Supply Chains Can Benefit by Utilizing Big Data as summarized here:

Planning & Scheduling

Perhaps the most important part of any supply chain. So much money can be lost or expended without adequate scheduling and planning. With big data you can truly optimize this process by gaining end to end visibility so that you know where your items are at all times.


Improved Responsiveness
It helps manage the unpredictability in your supply chain. Big data can help in deciding how to manage new items and existing inventory. It allows procurement to best determine what items to buy and when, what items to forgo and what items in your inventory that are becoming redundant. This is vital in truly determining what items need to be included in the company’s supply chain.


Improved Demand Planning
Big data can play a key role in predicting and meeting demand. Big data provides predictability in determining what items are going to be needed as it pertains to current and forecasted demand.


Order Optimization
Having full optimization of the ordering process minimizes backorders, minimizes cost of vendor selection and helps to ensure product arrives on schedule and meets required quality.


Supply Chain Execution in Real Time
At major OEMs the ERP (enterprise resource planning) system is built from big data. Leveraging this data allows you to monitor all facets of the supply chain in real time. When used to its full potential, this means having the ability to monitor inventory and order status at your supplier, all the way to in-process controls at your facility and much more.


Inventory Planning & Development
Big data allows users to plan, forecast, and optimize inventory control to reduce wasted space, remove obsolete inventory and most importantly plan and schedule the flow of parts into and out of inventory. You can in effect look across networks to consider consumption rates, inventory levels, and other aspects to optimize resources and ROI from the supply chain.


Replenishment Planning
It allows you to see where and when parts and components are required, see levels of inventory on hand, look across the network of vendor partners and more. When leveraging big data there is no need to depend on antiquated automatic replenishment systems that may trigger order levels which are not in line with current needs. Instead, by properly managing the data you can develop a replenishment plan that ensures parts are received into inventory when they are required, not before or after.

All of these factors support the effective use of big data in construction equipment parts management by predicting future trends, plus measuring past successes and shortfalls. Operations at OEM manufacturers are so complex that remaining competitive requires the use of a vast amount of data integrated into every phase of the organization.

 

This blog is an excerpt from our latest whitepaper, Fragmentation in Construction Equipment Parts Procurement. Click here or on the link below to download your free whitepaper! 

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