big data in supply chain managementWhat is big data and how does a business harness it to optimize supply chain management? Big data is simply large volumes of information streaming in at high velocity and in varying formats from multiple internal touch points and external sources. 

This mega data provides valuable insights that help improve operational efficiency, accurate planning, and continuous innovation, especially for the manufacturing industry.

Having big data by itself is of little use unless subjected to proper analytics with defined objectives in focus. Plan adequately for it with superior data systems alongside cloud-based technologies for better collection, storage, analytics, and faster speeds as traditional ERP and SCM systems may not cope.

Data for contextual intelligence

With the Internet of Things taking root in industry 5.0, manufacturers are waking up to numerous amounts of data from external sources. Careful plotting of this big data in tandem with targeted analytics can open unprecedented opportunities for industrial companies. 

This data, when unified, provides contextual intelligence of both the market and your operations, allowing you to make accurate decisions, lay strategy, and preempt unfavorable situations. 

Contextual intelligence from big data provides real-time market trends, customer behavior, and efficiency of your supply logistics. With this vital information, a manufacturer can improve the quality of goods in response to market preferences. 

Using real-time feedback on delivery records, steps can be taken to optimize supply logistics affecting the delivery of both finished products and raw materials. On this same platform, a manufacturer can use GPS data gathered from external sources to plan for the most expedient route optimization to keep supplies on track.

Aggregate demand forecasting and optimization tools

Every business wants to know what the prospects are for proper planning. However, this has proved challenging due to significant margins of error relative to term and other variables like seasons, consumer habits, or macroeconomic factors. 

Janet Taylor works as the big data specialist for a large MNC and has written several academized  and  SameDayEssay papers while working part-time for Resume services and professional writing services that provide research papers to students and corporates. In her view, big data unlocks the potential for aggregate demand forecasting that is more accurate based on first-party data in the system. 

She further adds that subjecting the large volume of data at your disposal to objective analytics will help you make accurate demand forecasts that will drive supply optimization. In other words, you will supply for near actual demand avoiding losses through dead stock. 

An efficient supply chain functions well on reliable, accurate, and optimized tools. One of these is the shipping status tool that provides real-time information on all movements on an integrated platform. Big data learns the system and generates alerts when any shipment is due to arrive or off schedule. 

Other optimization tools that are based on big data comprise order processing, warehouse management, lean inventory, supplier management, specialized freight and many more. Big data makes it easy to collect information on all these tools for analytics and intervention.

Enhanced collaboration along the supply chain

Manufacturers will work better and plan well if they understand the entire supply chain down to the retailer and customer. Traditionally this is the preserve of the retailer leaving the manufacturer and suppliers in the dark. 

IoT, combined with big data, has changed all of that. It collects information across all sources back to the manufacturer, providing better insights for improvement and strategy review. This system promotes collaboration between players in the same supply line based on mutually shared information.

Collaboration optimizes supply chain management through collective action on a virtual network to get goods to a customer in the target market on time as needed. This can be achieved through transaction integration, sharing supply chain information, or strategic collaboration. 

All these are possible on the big data platform, and bring greater optimization across the supply chain. Each partner can analyze the shared information to use for faster decision-making, get a single view of their customer, improve time management, and logistics leading to greater profit. 

This exercise involves large volumes of information generated by partners that can overwhelm any ordinary system unless supported by big data and cloud-based technology. 

Mobile technology in supply management.

The use of big data platforms provides the capacity to use multiple technologies at the same time without compromising the efficiency and integrity of the system. Mobile technology is one of these and helps transform supply chain performance generating real-time information from the field and across departments. 

Mobile technology no longer requires purpose-made gadgets, as smartphones will do just fine with the added advantage of uploading information into the main system online. Functions such as barcode reading, videos, pictures, and messages are easily performed by smartphones optimizing supply chain operations at very little cost.

The whole concept of big data is to improve efficiency and especially real-time intervention along the supply chain. Adding mobile technology makes it easy to track shipments with precision using devices with GPS linked to the central system over the internet. Drivers can map shorter or less congested routes to make timely delivery. 

Asset tracking and warehouse management are made easier through the use of smartphones and tablets. Field and warehouse staff can file reports on these devices without the inconvenience of leaving their workstations for meetings with management.

Managing supplier inventory

When you are a global enterprise spanning multiple markets, you need standard procedures to harmonize supplier inventory management. Without a standard format, the reconciliation of these channels will pose a challenge at headquarters stifling growth and causing losses. 

An efficient and credible central coordination unit becomes necessary for such multinationals with suppliers and sales channels in thousands. This is where big data comes in for easy coordination and reconciliation owing to its unlimited cloud-based capacity. Processing system-based documentation can be achieved across all units globally with integrated reconciliation capacity.

With this data system in place, you do not have to worry about the correctness of supply records or availability, as every delivery is captured in real-time on the central system. This helps management monitor warehouses and branches overseas while making timely interventions where necessary. 

Analytics of individual warehouses done at headquarters can give a bird’s eye view of operations in these units regarding profitability and viability. It is equally convenient using big data for real-time communication between branches, including standard updates and bulletins from head office. 

Optimizing RFI, RFP, and RFQ through big data

You want to get the best deal from your suppliers, but to do so, you must have vital information on their pricing. You send out requests for information that your analysts can assess using big data to create a credible picture of your supplier. 

RFIs are survey tools that, when incorporated on big data, can help optimize supply chain management by assessing supplier capacity and reliability. By looking at their infrastructure and financial ability, you are in a position to know what quality and quantities they can supply and in what timelines. 

You can leverage data to your advantage to improve reverse auction results attracting more suppliers based on lowest bid characteristics. The RFI can also indicate what other products the supplier has that you can procure through bundled purchasing that is great at cost-saving. 

Big data can also produce spend analysis reports by collating information from all integrated supply chain solutions used by staff against procurement data for clarity. The fact is, in every sphere of e-commerce, big data gives you power in knowledge.

Advantages of big data in supply chain management

Big data is only useful if you leverage its many advantages to improve your supply chain operations. First off, is its immense predictive capacity if linked to credible touch points. You can avoid both extremes of stock volumes by projecting production to match expected demand, ensuring efficient use of resources. 

Cost reduction is key to every business and is the main reason companies go for big data. By analyzing readily available and current data, you gain insights into new ways you could optimize supply chain efficiency, revise standards, and reduce overall costs.

Another key component in the supply chain is the customer whose satisfaction is paramount. This means you must have real-time credible information on your customer to give better service that includes shipment updates and prompt delivery of quality products. 

Big data gives unparalleled support on information and tracking to make your logistics reliable. When faced with a product recall, big data provides tools for tracing goods with a global reach, saving you countless with a click of a button. There is not an aspect of supply chain operation that cannot be optimized on the big data platform. 

Conclusion

The Internet of Things has made great strides offering limitless opportunities and innovations in business management globally. Big data is the link between businesses and this limitless cloud-based capacity that supply chain managers can tap into to improve the operations immensely. There is no limit to the amount of data you can gather and store on this platform, and the benefits are great.

Author Bio: The post is by Kurt Walker, a freelance essay writer in London for 3 years now. He is also a professional content writer and journalist covering topics that include inspiration, productivity, education, management and technologies.