How Prescriptive Analytics Solves Multiple Supply Chain Issues

Supply chain systems received the hardest hit due to the ongoing Covid-19 pandemic, even for countries where the spread of the virus was under control. The time has now come for supply chain managers to plan ahead and keep the flow of goods moving so that the supply chain systems continue to work smoothly at all crucial times. 

Data analytics plays a vital role in understanding the working of your supply chain department and forecasting the trends that impact the inflow and outflow of goods. Therefore, a robust data analytics strategy is needed to resolve the different challenges that the supply chain teams face.

Prescriptive analytics is the future of business decision-making. It plays a crucial role in transforming the working of the supply chain systems on all four components, namely products, vehicles, facilities, and routes. 

Let us first understand the complexities of the supply chain, and then we will discuss how prescriptive analytics can help solve them.

Supply Chain Complexities 

As we saw in 2020, the supply chain is a volatile aspect of any business, and several factors come into play for its proper functioning. Here are the challenges faced by supply chain teams worldwide:

• Increasing fuel prices that raise the cost of transporting goods. 
• The constant fluctuation in prices, especially in industries such as the oil and gas sector. 
• Natural disasters, pandemics, and trade wars might partially shut down the supplies from affected countries.
• The increasing cost of raw materials raises the market price of products.
• Demand for faster “last mile” logistics for eCommerce stores catering to the needs of local customers.
• An experienced and talented workforce who can handle work pressures and work in different time zones.
• Change in consumer demands or political agendas affecting the supply of goods.
• Dropshipping services enable consumers to receive goods quickly. 
• Customer service can adapt itself to the changing needs of consumers.
• Good supplier relationships for timely delivery of products.
• Adaptability to technological advancements and making way for innovations to improve the flow of output.

To drive growth, supply chains need to enter new markets and manage supplier/delivery partner relationships. The need to maintain an inventory with different partners is also rising. Supply chain complexity is inevitable, but if you take the help of prescriptive data analytics solutions, you can certainly manage them with ease.

What is Prescriptive Analytics?

Prescriptive analytics is a kind of data analysis on raw data to make better business decisions. It uses advanced analysis techniques to determine the best solution among different choices. 

For instance:

• Descriptive analytics tells, "What happened?"
• Diagnostic analytics tells, "Why did it happen?"
• Predictive analytics tells, "What will happen?"
• Prescriptive analytics tells "How can we make it happen?"

Prescriptive analytics uses historical data and estimates outcomes based on different variables. It uses sets of algorithms, artificial intelligence, and machine learning to offer suggestions based on outcomes. 

How Does Prescriptive Analytics Work?

Prescriptive analytics leverages the power of artificial intelligence and machine learning to understand the data and recommend the future course of action. It combines with predictive analytics and uses advanced statistical techniques to analyze historical data.

For instance, a manufacturer can model prices on various factors and plan for changes in demand, the flow of production, and storage needs.

Prescriptive analytics works on two algorithms:

• Heuristic: It uses a shortcut method to find a solution to the problem by sacrificing accuracy, precision, and optimality.
• Exact: It always finds the best answer to the problem with great accuracy. However, the time taken to solve the problem increases exponentially compared to the problem size.

How Prescriptive Analytics Solves Supply Chain Problems

Prescriptive analytics is a better alternative to the traditional ERP systems that does not predict the next course of action. Most business analytics solutions offer great insights into what happened in the past but do not predict what will happen in the future.

To determine future supply chain challenges, prescriptive analytics uses various statistical modeling techniques to create a perfect supply chain model. After that, it uses different forms of structured and unstructured data to evaluate different situations under which the supply chain cycles might get affected.

Advanced analytics and artificial intelligence decide the next course of action to overcome different supply chain challenges and achieve all the business goals.

For example, logistics data solutions such as Logmore can store shipment information to the cloud as “missions.” A Logmore data logger can scan temperature, shocks, lights, humidity, tilt, and location and upload the measurement history to a secured cloud. Anyone can scan the QR code from the logger's screen to examine the data at the arrival. It solves the route-related issues and delivery partner performance besides optimizing speed. 

Over time, you can accumulate enough data from all of your company’s shipments to start applying predictive and prescriptive models on a macro level. Logmore’s API makes it easy to connect mission condition data to the data-crunching platform of your choice.

Final Thoughts

You can use prescriptive analytics for capacity planning, demand shaping, scenario analysis, logistics optimization, and operations planning.

Today's analytics solution should use all three analytics, namely descriptive, predictive, and prescriptive, to optimize their inventory policies based on future demands.