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8 Ways machine learning can improve supply chain planning

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An efficient supply chain planning is the fundamental block for building a successful and well-organized supply chain mechanism. Many businesses are unable to achieve the desired operational excellence due to manual operative approaches, lack of visibility and poor supply chain planning.

This restricts brands from creating synchronized, smooth and responsive supply chains.

What is supply chain planning?

The most crucial activity in supply chain management is planning. Supply chain planning is the process of accurately planning a product flow from raw material sourcing to reaching the final consumer. This process includes demand planning, production planning, supply planning, sales and operations planning.

8 Ways Machine learning can improve supply chain planning

In order to develop an efficient supply chain plan, a reliable tool like machine learning can be helpful. Being a subset of Artificial Intelligence (AI), machine learning imparts learning abilities in a computer to function without human programming control. With its implementation, businesses can significantly optimize their supply decision-making processes.

Here are the eight ways machine learning can improve supply chain planning.

Forecasts future inventory issues

The biggest challenge in supply chain planning is predicting future demand for production. Traditional methods of forecasting like baseline statistical analysis, moving averages or advanced simulation cannot track or quantify some factors over a time period.

Machine Learning (ML) algorithms and models can easily identify patterns hidden in historical data. They are effective in tracking and quantifying factors that even traditional methods lack. This helps businesses create demand forecasting on metrics like optimum inventory levels, and decrease holding costs.

Machine learning also helps in  dealing with stockout issues and backorders during seasonal demand peaks. By analyzing patterns of seasonality and marketing spends, it helps align marketing spends for different products. It enables brands to identify, prepare and plan their inventory for future stocking issues that can result in frustrating customer experiences.

Highly responsive to supply chain disruptions

Since the COVID-19 pandemic, a majority of businesses have started to focus on supply chain resilience. Utilizing machine learning technologies helps businesses detect issues in the supply chain before it disrupts their operations.

Machine learning plays an instrumental role for businesses in building a robust supply chain forecasting system to respond to emerging issues and threats. By quickly responding to new problems and disruptions, it helps them improve the effectiveness of response.

Forecasts supply or supplier failures

Many businesses depend on external suppliers for the final quality of their products. Tracking and tracing every item or component used to build a final product is time-consuming. With machine learning algorithms, brands can quickly obtain real-time reports on supplier quality, thereby saving time and money.

Pairing up machine learning models with data science applications helps businesses extract meaningful supplier data from various sources. Its actionable insights on supplier quality after running noise reduction and relevance-based normalization calculates risk index/score for suppliers. These forecasted risk scores help organizations avoid potential supplier failures.

Reduces maintenance costs and improves asset longevity

Businesses that do not regularly track the health of supply chain assets risk losing their productivity. Knowing when supply chain assets will fail, degrade or depreciate is necessary for them to plan and schedule maintenance.

For instance, what happens when businesses don’t track their fleet assets? The quality of fleet assets degrade over time and results in a fall in productivity.

Preventive maintenance is the most effective strategic asset for any supply chain business. Combining machine learning capabilities with supply chain assets helps them avoid machine failures. It improves asset productivity and reduces the overall maintenance cost.

Machine learning also enables businesses to monitor, maintain and improve the life of supply chain equipment. These supply chain equipment may range from engines to different transporting equipment.

ML algorithms collect data on factors that influence the machine adversely. These datasets collected on a yearly basis help manufacturing industries measure and forecast their key metric – Overall Equipment Effectiveness (OEE). OEE is a metric used to measure the productivity, identify the loss and benchmark the progress of a manufacturing equipment.

With the use of machine learning, manufacturers can assess replacement and repair of their machines. It helps them identify worn out components and take preventive corrective measures for the maintenance of machines, thereby enhancing the longevity of their working assets.

 Improves on-time delivery scores

In the era of same-day delivery and next-day delivery, customers are increasingly anticipating faster deliveries. A few delayed deliveries and failed deliveries are enough to make them frustrated and exit a brand. To satisfy customers, it has become mandatory for businesses to consistently maintain their on-time delivery scores.

Machine learning helps businesses regularly track the status of shipped products, received goods and issues goods. It offers detailed analytical insights on orders to avoid possibility of delays and solve issues before they occur.  Its predictive ability enables them to identify the reasons for delay and avoid last-minute rush of orders.

With ML applications in tablets and mobile devices, supervisors can track real-time data from anywhere in the world. This makes the real-time tracking process more accessible, and streamlined. This machine-driven process helps them design a better product plan and deliver them on-time, with improved efficiency and reliability.

Improves factory scheduling accuracy and production planning

Many manufacturing companies depend on a production approach named build-to-order production workflow. In this workflow, a product is created once a confirmed order is received. It is the customer who determines the number of produced products and the time required for it.

Job shops are manufacturing units that specialize in making small batches of tailor-made or custom-built goods specifically for one customer. This customer may be an end user or another company. Businesses that use this job shop process work with a Just-In-Time (JIT) workflow to fulfill customer orders.

Modern production systems are filled with shorter delivery times and fluctuating customer demands. Hence, they require a fast-paced scheduling and production planning that is flexible to changing conditions.

The latency for components that are used to assemble customized products is the major reason for supply chain delays. Machine learning helps manufacturers balance multiple constraints and optimize their production. It enables them to improve production planning and factory scheduling accuracy, thereby reducing latency by manifold.

Using machine learning algorithms on production data, manufacturers can identify areas that are exposed to waste and inefficiency. It helps production plants involved in job shop production environments to accurately determine optimal job shop schedules. It enables them to automatically adjust their production planning and scheduling to changing conditions.

Facilitates fraud prevention

A few decades back a majority of manufacturers did face an inevitable fall in product quality. There used to be a team that inspects the product quality manually. But since the advent of machine learning, things have changed completely.

Machine learning algorithms analyze a humongous amount of past data of frauds, recognize patterns and build a model from it. These models help businesses find fraudulent transactions, avoid credential abuse, automate anti-fraud processes and speed up fraud investigations. Its automated monitoring process helps supply chain professionals verify, if the finished products meet the quality or safety standards.

With its automated inspections, auditing processes and real-time analysis of results, machine learning algorithms easily detect anomalies, exceptions and outliers in product quality. This helps businesses prevent privileged credential abuse, thereby instantaneously reducing the risk of fraud and enhancing product quality.

Minimizes forecast errors and improves forecasting accuracy

In this information age, businesses are dealing with abundant data like never before. Machine learning works like a robust analytical tool to process large sets of data from innumerable sources. Beyond processing, it deals with vast amounts of data with the greatest variety and variability.

With IOT devices, intelligent transportation systems and other technologies, machine learning enables companies to obtain better insights and achieve accurate forecasts. Also, it eliminates even a negligible human intervention unlike traditional modelling methods.

Conclusion

With machine learning technologies gaining momentum, manufacturers definitely stand to benefit from it, in terms of supply chain planning. It aids them in reducing supply chain costs, eliminating potential bottlenecks, boosting productivity and optimizing resource utilization. It assists them in building a flexible, agile and profitable supply chain that meets customer expectations.

Many businesses lose their credibility and profitability due to their last-mile delivery performance. Planning your last-mile delivery with a powerful tool like a delivery logistics software maximizes the operational efficiency of your fleet.

With its machine learning algorithms, logistics optimization software helps plan your last-mile logistics factoring in customer expectations and delivery constraints. Its optimal route recommendations and on-ground real-time tracking helps fleet drivers maintain a consistent on-time delivery performance. It helps fleet managers make their logistics operations more efficient, profitable, sustainable, agile and flexible, without burdening their resources.

Content Outline : Supply chain planning is an essential foundation for building a well-orchestrated supply chain. It lays the pathway for businesses to achieve operational efficiency, eliminate potential bottlenecks and optimize resource utilization. Machine Learning has become pivotal in creating a smooth, synchronized and responsive supply chain for your business. Find out how machine learning algorithms are highly beneficial for supply chain planning.

Author Bio :

Author Name : Elsa

I am an author specializing in content related to e-commerce, business, finance, supply chain and logistics. I write highly-researched, customized and reader-friendly articles that suit my target audience. Singing, writing poems, and sculpting short stories are some of my hobbies.

 

We are a team of writers passionate about innovation and entrepreneur lifestyle. We are devoted to providing you the best insight into innovation trends and startups.

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