8 Predictions and Trends in Supply Chain and Logistics for 2021

Read the full article: http://bit.ly/Supply_Chain_Trends_2021

The aftermath of the pandemic, with its technological and economic adjustments, and the shift to a new normal, will drive operational processes in in-plant and e-commerce logistics and supply chains into a more agile form.

In 2020, supply chains experienced the biggest disruption since WWII. The COVID-19 pandemic has highlighted weak spots in material flows and a lack of resilience in global supply chains. The emergency situation confronted the logistics industry and supply chains with a new set of issues and challenges, which could have been prevented through timely access to accurate data and the implementation of more elastic operational processes.

Recent supply chain disruptions and shutdowns in logistics flows foreshadow more extensive changes to come in the global supply chain model. The transition to features and operating principles of digital supply network will be accelerated in order to adapt strategies ensuring business and operational continuity and sustainability even in the face of unprecedented events and emergencies.

(POST) PANDEMIC LOGISTICS
One of the most important findings since the outbreak of the pandemic has been that too much emphasis has been placed on streamlining logistics flows and accelerating "Just in Time" deliveries at the expense of their resilience to disruptions. Therefore, the challenge for the near future in the logistics industry will be to ensure the performance of lean supply deliveries without compromising its flexibility and continued operability.

One way to achieve supply chain flexibility is through more accurate sales planning and delivery projections. Businesses that want to better anticipate potential sudden changes on markets and be prepared for the rising volatility in customer orders should focus on solutions of predictive analytics.

Proper implementation of predictive analytics requires access to relevant data. The right data ensures that sales planning and scheduling take into account the strategic and operational objectives of the business. The outputs of the predictive analytics also serve as timely alerts and notifications for employees about expected shifts (or disruptions). On the basis of this information, authorized personnel can adequately and speedily respond to changing priorities, and operationally intervene to assure smooth deliveries.

DATA LOGISTICS AND DEMAND-DRIVEN LOGISTICS
Insufficient supply chain visibility has been frequently identified as a shortcoming in supply chain management during the pandemic. Businesses are not yet using their data to their full potential for making quick and informed decisions. Incorrect or missing data reduces the visibility of material flows, and means that material transfer and order movement cannot be adequately monitored. This significantly limits the flexibility of logistics operations and their ability to adapt to market changes.

The lack of data and the low supply chain visibility can be caused by:

-manual demand planning,
-inflexible ERP systems,
-WMS systems with significantly limited functionality in terms of monitoring, data analysis and overall performance.

The result is not only an inaccurate and outdated overview of the current state of inventory and what is happening in material flows, but also inefficient and slow supply delivery processes. However, enlarging data collection and analysis can contribute to:

-enhancing the visibility of logistics operations,
-more detailed status information on material flows,
-more accurate capturing of relevant transfers and movements of material and components in real time,
-gaining access to different demand patterns that can be used for efficient inventory and order fulfillment planning.

In order for companies and distribution center operators to multiply the added value of data analytics in inventory and warehouse management, they should process data from diverse sources such as sales forecasts, POS terminals and others. The interconnection of data sources should lead to the integration, collaboration and synchronization of data and logistics processes, including the breaking down of data silos between the shop floor, the warehouse, in-plant logistics and material flows from suppliers.

Advanced data analytics is part of the innovative concept of "Demand-Driven Adaptive Enterprise", which was created in response to the growing volatility of global supply chains. The demand-driven adaptive enterprise represents a management and operational model focused on the flow of relevant information and materials while taking into account the operational, tactical and strategic scope of the enterprise.

Read the full article: http://bit.ly/Supply_Chain_Trends_2021

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