Why predictive analytics is critical to supply chains in Africa

Predictive analytics is one of the vital pillars of modern supply chains, even for those operating within Africa.

Technology has been great for modern supply chains. However, data analytics is one of the most underrated tech solutions in the supply chain. Less than 30% of supply chains are utilizing it. And this is on a global scale.

 

So, what is predictive analytics?

Predictive analytics is the analysis of data to determine the future. It is used to predict demands, market trends, and patterns, With predictive analytics, you can help the supply chain make better decisions and prepare for future events.

Decision-making is part of life and any business or supply chain. You must make a decision every day, if not multiple within a day. However, these decisions are usually mere guesses backed up with a lot of guts or trust.

With predictive analytics, it doesn’t have to be so. These decisions can be backed up with data-driven insights, giving you full control of the outcome.

 

Factors that impact the effectiveness of predictive analytics for supply chains in Africa

Some factors must be present when using predictive analytics in any supply chain operation. To a large extent, these factors will determine the solution’s success.

 

1. Data and its source

Not just any data but clean and trustworthy data. Using wrong data to predict the future will only throw your entire operation into chaos. Trusted data sources are vital here. For example, data from the customers over data from a consultant.

2. Skilled personnel

You need trained or skilled people to correctly use the predicted information if you will have any results from it. Prediction is not hard when everything is in place, but what to do with that information is where many supply chains fail.

3. Type of model

There are different models in predictive analytics. The use of any of its models will depend on the problem and nature of the data. You don’t want to use a classification model when you need a clustering model.

 

Why is predictive analytics vital to supply chains operating in Africa?

The solution is vital to African supply chains for several reasons, especially when considering the unique challenges supply chains face.

 

1. Demand forecasting

There is very little any supply chain can accomplish without demand forecasting. Demand forecasting is a critical aspect of any supply chain planning. It helps determine the resources needed and what it will take for the supply chain to serve the customers effectively.

With predictive analytics, demand forecasting no longer has to be so tedious for these supply chains. It is also way more accurate since it gets rid of human errors. The supply chain can save costs, improve its services, and ensure customer satisfaction.

Predictive analytics also factors in seasonal demands, political tensions, historical data, and other external issues that could impact demand forecasting. You have a more complete view of the future, which will help in preparation.

2. Route planning

Africa is a vast continent with beautiful topography. However, it is marred by poor infrastructure, which is a huge limit on transportation and logistics. There are also issues with security and traffic congestion on the better roads.

But the good news is that the roads are almost unlimited, and there is always more than one way to reach a destination. This is where predictive maintenance plays a huge role.

The solution helps supply chains and logistics businesses across Africa to identify the best routes for transporting goods and services to customers. This way, the supply chain can improve lead times and optimize the resources such as fuel used in the process.

3. Risk assessment

Because African supply chains face unique challenges, their risks are also unique compared to other supply chains. They include political tensions, high inflation rates, currency fluctuations, etc.

With predictive analytics, supply chains across Africa have a better handle on what to expect, and they can prepare or anticipate better with more insightful data. 

This could determine the type of suppliers, raw materials to source, city to source from, and many other important decisions. 

With predictive analytics, African supply chains can assess the risks better, empowering them to develop tangible solutions and contingency plans.

4. Innovation

Innovation is the spark that brightens any supply chain operation. However, it doesn’t just happen on its own. A lot of factors have to fall into place, including competition, customers, and so on. 

However, one of the ways a supply chain can bring about innovation is by identifying areas where it is lacking. And this is where predictive analytics comes into play. You can use it to identify weak links in the supply chain and then find possible solutions.

When applied well and in the right setup, predictive analytics can help bring innovations to the supply chain’s operations. It will also help optimize the operations for effectiveness and reliability.

5. Warehouse optimization

The warehouse stores the supply chain’s raw materials or finished goods. It plays a vital role in supply chain management. However, it can get very rowdy, especially with constant activity.

These activities and flow can be limiting for warehouse operations. However, with predictive analytics, there is a better arrangement of goods. The categorization will usually pay attention to the goods and their exit rate. For instance, the fastest moving goods will be closer to the door.

This way, there is a controlled flow in the warehouse, people do not get in the way of each other, and there is less risk of damage to any of the products. 

 

How does predictive analytics work?

The following process will help you make the most of predictive analytics in your supply chain.

 

1. Identify your needs

Before using predictive analytics, you have to know what you need. This, in turn, will determine how you will go about it. If you need demand forecasting, you will go about it differently than when you need risk assessment.

2. Data cleaning and processing

You want to make sure you have the right data in the right amount. This is where you vet the quality of the data and feed it to the algorithm or machine. A computer is still a garbage-in and garbage-out solution. So what you feed it will determine the results.

3. Model selection

There are different models of predictive analytics. As we mentioned earlier, the nature of the problem and data will determine what model you will use. Determine this correctly, and you will have a seamless time with the solution.

4. Communicate and collaborate

Data is ineffective if left on its own. When you have your results, you must share them with relevant stakeholders for the best impact. This could be suppliers, internal teams, and others. Your communication and collaboration effectiveness will dertemine how well the data is utilized.

5. Feedback

This is important because it allows you to learn from the process. You are able to determine the flaws in the system and points that could be improved. Effective feedback makes the predictive analytics solution more impactful in your supply chain.

 

FAQ’s on Predictive analytics and its impact on supply chains across Africa

 

𝗤𝟭: 𝗖𝗮𝗻 𝗹𝗮𝘀𝘁-𝗺𝗶𝗹𝗲 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗯𝗲 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝘂𝘀𝗶𝗻𝗴 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝗻 𝘂𝗿𝗯𝗮𝗻 𝗮𝗻𝗱 𝗿𝘂𝗿𝗮𝗹 𝗮𝗿𝗲𝗮𝘀?

Predictive analytics is useful in urban and rural settings because it can optimize last-mile delivery routes based on real-time data while considering local geography and traffic conditions.

 

𝗤𝟮: 𝗛𝗼𝘄 𝗰𝗮𝗻 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻𝘀 𝗶𝗻 𝗔𝗳𝗿𝗶𝗰𝗮 𝗶𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘄𝗶𝘁𝗵 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀?

To incorporate predictive models into different supply chain operations, businesses might invest in data collecting and analytics technologies, train their staff, and work with technological partners.

 

𝗤𝟯: 𝗖𝗮𝗻 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻𝘀 𝗶𝗻 𝗔𝗳𝗿𝗶𝗰𝗮 𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗳𝗿𝗼𝗺 𝗯𝗲𝘁𝘁𝗲𝗿 𝘀𝘂𝗽𝗽𝗹𝗶𝗲𝗿 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀?

Predictive analytics helps with effective supplier relationship management by evaluating supplier performance data, forecasting possible dangers, and suggesting strategic sourcing options.

 

𝗤𝟰: 𝗛𝗼𝘄 𝗰𝗮𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗵𝗲𝗹𝗽 𝗔𝗳𝗿𝗶𝗰𝗮𝗻 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗺𝗼𝗿𝗲 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲?

Through waste reduction, eco-friendly practice promotion, and fuel efficiency due to route optimization. Predictive analytics helps sustainability programs and contributes to ecologically responsible supply chain operations.

 

Conclusion

Predictive analytics helps supply chains across Africa tell the future. This way, they are not taken by surprise and can better naviagate challenges as they come. 

As more supply chains across the continent continue to embrace tech solutions and modernize their processes, predictive analytics will undoubtedly play a main role.

Tags: Nigeria, Ethiopia, Ghana, Sierre Leone, Benin, Burkina Faso, Cote d’Ivoire, Togo, South Africa, Mozambique, Egypt, Niger, Senegal, Tanzania, Madagascar, and Cameroun.