Business & Analytics | How Supply Chain Analytics is changing the face of Logistics and Supply Chain Management.

Business & Analytics | How Supply Chain Analytics is changing the face of Logistics and Supply Chain Management.

Supply Chain Analytics, Evolution of Supply Chain Analytics, Manufacturing, Warehousing, Packaging, Sales, Inventory and Operations Planning

Supply Chain Analytics focuses on improving operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational, and tactical levels. It persists virtually the complete value chain: sourcing, manufacturing, distribution, and logistics.

Evolution of Supply Chain Analytics:

In the past, supply chain analytics were mostly used for statistical analysis for demand planning and forecasting. Data were stored in spreadsheets that came from different participants in the supply chain.

By the 1990s, companies started adopting Electronic Data Interchange (EDI) and Enterprise Resource Planning (ERP) systems to connect and exchange information among the supply chain partners. These systems provided easier access to data information for easier analysis, designing, planning, and forecasting.

In the 2000s, businesses began shifting to business intelligence and predictive analytics software solutions. These solutions drove more companies to gain more in-depth knowledge about their supply chain network performance, how to make better decisions and how to optimize their networks.

As recently in 2017, a typical supply chain has accesses 50 times more data in just 5 years. However, 80 percent of supply chain data is unstructured and just 20 percent were only structured. Therefore, organizations were looking for ways to best analyze this dark data.

Studies are pointing to cognitive technologies or artificial intelligence as the next technologies. AI technology can think, reason, and learn in a human-like manner. Artificial Intelligence can also process tremendous amounts of data and information, both structured and unstructured data, and provide summaries and analyses of that information.

Today, companies are investing a lot of time in allocating resources to improve supply chain efficiency and enhance the speed of operations.

Manufacturing:

Big data and analytics are driving manufacturing on a huge scale. For example, Data on manufacturing parameters, like the forces used in assembly operations or dimensional differences between parts, can be archived and analyzed to support the root-cause analysis of defects, even if they occur years later. Agricultural seed processors and manufacturers analyze the quality of their products with different types of cameras in real-time to get the quality assessments for each individual seed.

The Internet of things technology is also booming with the help of networks of cameras and sensors on millions of devices, giving the live information on a machine's condition could trigger the production of a 3D-printed spare part that is then shipped by a drone to the plant to meet an engineer, who may use augmented reality glasses for guidance while replacing the part.

Warehousing:

Warehousing plays a fundamental role in a logistics system. Inbound functions assist to prepare for storage as well as outbound functions pack and ship orders, resulting in both advantages to customers and business.

Storage Facility:

This brings higher returns for your business. Manufacturing or the purchasing of goods in bulk are basic requirements.

Distribution:

Distribution is a central part of business, where "Out-of-stocks" should be minimum. It stores all the goods to smooth the distribution process to have constant control of the stock available as well as future requirements. This is called safety stocking, ensuring your business doesn’t run into unexpected problems such as faulty stock or shipment delays.

Convenience:

Warehousing can directly impact the distribution of any company. The distance away your storage facility or warehousing is situated from your suppliers or manufacturers, the more your distribution costs will rise. The farther distance warehousing from suppliers or manufacturers, the more your distribution costs will rise. Strategic placement of the warehousing facility can dramatically affect your transportation costs, in turn, influencing the product. Alternatively, some countries such as Germany use warehouses as their storage and retail facility.

Packaging:

Vendors are looking for innovative ways to stand out in the market and extend their customer reach online/offline both. Customer-facing and smart packaging elements are only two of the most promising innovation areas which are identified for the packaging industry and among them, blockchain technology is a highly advanced technology in more efficient and fast supply.

Each year, the automotive industry loses between 16 and 18 percent of reusable packaging assets, which amounts to an enormous "multi-billion-dollar problem.

Blockchain as a Tool for Supply Chain Innovation:

A blockchain is a tool that should be leveraged to give access to instant and transparent information. Blockchain is the new payback tool to explore and exploit.

For instance, Company named "Surgere '' which is a cloud-based software solution adopted the Blockchain for their Supply chain management. That will provide the visibility which can remove the artificially created demand patterns and make visible smooth and continuous demand for tires near real-time.

Sales, Inventory and Operations Planning:

Planning is the most data-driven process in the supply chain which is using the Enterprise Resource Planning (ERP) and SCM planning tools.

Retailers are now using the new data sourcing to improve the planning process. For instance, Blue Yonder has created a model that uses the data-intensive forecasting methods now deployed into retailing where 130,000 SKUs and 200 influencing variables generate 150,000,000 probability distributions every day. This has dramatically increased the forecast accuracy of the company's logistics capacity needs, reduced obsolescence, inventory levels, and stockouts.

Similarly, IBM has also developed the links between production planning and weather forecasts for bakeries. By analyzing the temperature and sunshine data, now the baking companies are more accurately predict demand for different product categories based on factors that influence consumer preferences.

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