10 Ways Machine Learning Is Revolutionizing Supply Chain Management_Featured-gradient

What is Supply Chain Management?

Supply chain management (SCM) manages the asset flow, services, resources, and many more parameters of an organization. Management of goods is done in several steps such as the storage of goods, goods in process, finished goods, transportation etc, i.e from the production stage to the final product delivery and selling stage. It involves, designing, planning, execution, controlling, and monitoring to improve and increase the net value. In this process, the marketing channel plays a vital role in increasing the customer value and to gain the advantage of the competition in the marketplace.

10 Ways Machine Learning Is Revolutionizing Supply Chain Management


What is Machine Learning?

Machine Learning (ML), is an application of artificial intelligence (AI) that learns and creates complex algorithms, which reduces the human interference by automating tasks. ML improves the ability of the machine, by sensing and learning from the environment without any prior program. ML makes the machine capable of learning itself and to predict the outcomes by analyzing data collected from various sources.

Many businesses can save time and money by implementing machine learning technology. All business deal with managing employees and schedules. Employee management software can streamline business operations using AI technology. By using AI technology, employee management software can accurately forecast labor demands and create the perfect schedule with a single click. Interested in learning more? Schedule a call with a Deputy rep and see the Deputy product in action before deciding if it’s right for your business:

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Why SCM?

SCM helps in optimizing the effectiveness and efficiency by increasing the operational speed. The best SCM would be capable of delivering the product at a faster rate, at less cost but without hampering the quality of the product. Generally, to achieve these type of targets, large-scale companies use complicated logistic tools for better delivery of goods, share databases so that the distant employee get real-time information and can easily coordinate etc.

Cost reduction is one of the easiest ways to stand a step ahead out of the competition. But, this may not be the best option as price dropping can lead to red flag in business. So, SCM provides the better and cost-effective way to take advantage of the market. In this way, the business does not need to drop the price of the product. For example, faster delivery of goods will result in developing more trust between the customer and hence, gives the advantage to the business.

SCM also reduces the unnecessary steps from the production lines. In this way, the time and extra effort of the employee will be reduced. An efficient supply chain helps in maximizing the negotiating power and the business stands firm only when the business is quick in its process. This is because of the support from the clients/partners and it is obvious they will take the side of a growing merchant. Hence, it will be beneficial for the business in gaining more favorable projects because of consistency in faster delivery.

10 Ways Machine Learning Is Revolutionizing Supply Chain Management

When ML collaborates with SCM it brings a revolutionary transformation across industries. Machine learning helps in generating the advanced and effective patterns of supply chain data. The algorithms are discovering new patterns that pinpoint the most prominent factor in the chain delivering continuous improvement and learning in the process.

Machine learning has the potential to form new patterns without the human interference. The ML algorithms compare the data with other constraints to recognize the root cause factor making accurate predictions.


How ML is revolutionizing supply chain management

Here are 10 ways machine learning is revolutionizing supply chain management:

  1. Accurate forecasting
  2. Cost reduction
  3. Consumer engagement
  4. Physical inspection
  5. Contextual intelligence
  6. Forecasting demand
  7. Increase in longevity
  8. Improvement in supplier management
  9. Improvement in planning
  10. End-to-end visibility

Accurate forecasting

The most challenging task faced by an industry is to make future predictions. Machine learning algorithm helps in analyzing a large amount of data such that accurate forecasting can be done. The pre-existing techniques like statistical analysis are moving toward the advanced simulation modeling. ML algorithms are proving the effectiveness on the traditional methods of analysis. It shows that the traditional methods can never track or quantify the progress ML has made. For example, ML is implemented by the Lennox in forecasting the demands of the product.

In order to maintain the status of shipped material always track the record of issued goods and received goods, such that the company can handle the upcoming possibilities of delay and issues even before they occur. In this way, the order is delivered/received on time, resulting in the reduction of a last-minute rush of the product during the shipment. It reduces the extra expenditure on logistics and payroll.

With the help of ML application on tablets, mobiles the supervising authorities can overview the company from different places on the basis of real-time data. As a result, the process is more streamlined, more machine-driven, and more accessible, which leads to a better plan of product and arranging the item to deliver on-time with better efficiency and reliability.


Cost reduction

Cost reduction does not mean to hamper the quality. Machine learning is helping in fulfilling the on-time demand and minimizing the risk with data analysis.

In manufacturing industries, the combination AI and ML has removed the human resource and has reduced the time taken to accomplish the project. It also eliminates risk from the supply chain. Thus, deducting the cost of the project. Also, machine learning removes the long process of monitoring and tracking the data.

Customers play a vital role in supply chain strategy. The basic rule of marketing is to fulfill the demand of a customer. Hence, the additional cost will be deducted which has no value. It is an important aspect, a customer only pays for the product which has value in their own terms. Customers pay the value in which they are happy and certainly, it will help in running the business with better flow. A fully automated customer service will help the organization in achieving an increased sale, profit, and an increase in trust of the customer on the product. Also, proper planning of the strategy has the potential to deduct the extra cost.


Consumer engagement

From the last few years, industries are collaborating with machine learning such that they get the valuable insight from the consumer’s data. Through the ML algorithm, companies are improvising solutions by automating customer services. These technologies are combining the browsed data from the customer with natural-language processors to give accurate solutions. The technology is capable of taking the questions from the customer, collecting data, and then responding to an accurate and best solution within the timeline. These platforms are capable of analyzing the sentiments of consumer along with predictions related to the stock.

Machine learning provides the insight of supply chain management to improve the performance of the goods. By collaborating the machine learning with SCM the technology is continuously evolving and finding new ways to improve the performance. Through the strengths of supervised learning and unsupervised learning, ML is finding the key factors which may affect the SCM.


Physical inspection

Through ML algorithms, visual inspection and monitoring can be done easily which can reduce the threat to the supply chain. ML collects multiple data sets and then compare them to provide the accurate predictions. As we know machine learning is best in recognizing the visual patterns. Thus, physical inspection of the goods in the whole supply-chain is done easily and quickly.

It has the ability to quickly identify the damage, wear and tear throughout the logistic hubs and product shipment. For example, IBM Watson uses Machine learning algorithm to identify if the shipping container is damaged. It can also classify the damage time and suggest the best alternative to repair the goods. The algorithm used by the Watson is used to combine the data taken visually or through the system and based on that track, report and suggest alternative in real-time.

10 Ways Machine Learning Is Revolutionizing Supply Chain Management


Contextual intelligence

Machine learning is combined with advanced technologies such as logistics control tower to lower the inventory for precise operation cost and to build relationships with the customer quickly. Machine learning provides the insight of supply chain management to improve logistics, warehouse management, etc.

In manufacturing, contextual intelligence has brought the need of customer in the center. By fulfilling the customer requirement, the industries are earning huge revenue. By ERP, SCM, and CRM both the legacies came together, companies will be able to complete the requirements accurately, on-time, and fast responses to the questionnaires perfectly.


Forecasting demand

Machine learning provides the predictions on product demand by analyzing indirect and direct sales. Machine learning is proving its value among the causal factors that may affect the demand.

Nowadays companies are focussing on machine learning to forecast the demands, the inclusion of ML is much easier in demand planning than in production planning. The global supply chain planning (SCP) market of 2 billion USD is moving to software as a service model (SaaS) from software license model lowering the upfront cost. Users can enroll in Cloudera Certifications Training Course to understand the basics and advance training on ML, including IoT and AI.

This certification course helps in demonstrating your expertise with technical skills. As many companies require professionals who are able to solve the big data problems through analytical tools. However, experts are predicting in upcoming years the number of skilled professionals in analytic tools will be less.

Algorithms like SVM(Support vector machines), RNN(Recurrent neural networks) and NN(Neural networks) are providing the demand insights. These processors are analyzing from Big data to forecast accurate data. Truly, machine learning forecasting is integrating the data collected in a more beneficial way. Machine learning forecasting is more automated, self-correcting and powerful in comparison to the average traditional methods.


Increase in longevity

Machine learning is increasing the life of supply chain equipment such as machines, engines, warehouse, and transportation equipment. By implementing ML with IoT sensor new patterns can be identified. For example, in manufacturing industries, the data is collected on the yearly basis to increase the life. Machine learning helps in analyzing the factor through machine derived data which influences the machine adversely. Also, machine learning helps in measuring the overall equipment effectiveness(OEE), which is a key metric for most of the manufacturing industries.

By implementing Artificial intelligence, manufacturers will be able to do inventory management. It is an important part of an industry through which they can spot the theft, spoilage, need for replacement and repair. With the help of machine learning sensors, items misplaced, worn out components, parts can be identified such that corrective measures can be taken. In this way, supervisors of the department can take control of the stock related issues such as restocking, maintenance of machines, organizing so that the demand is met.

10 Ways Machine Learning Is Revolutionizing Supply Chain Management

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Improvement in supplier management

Machine learning helps in defining the product hierarchies and also trace and track the data. On average, 80% of components of a product comes from the external suppliers of an industry. ML is more prominent in Industries like Aerospace, Defense, Medical, Foods, and beverages. A typical manufacturer invests thousands of hours and money in product hierarchies and in tracking and tracing.

Supply chain efficiency can be improved by using a statistical model for uncertainty and deviations. There are multiple numbers of uncertainties in supply chain management and through statistical model disruption, market outliers can be considered. Statistical model also helps in taking an account on deviations and exceptions. In order to overcome these deviations and uncertainties, machine learning has helped in finding the gateway. ML helped in handling the challenges during forecasting the demand and variations. The algorithms help in refining the model from the data in a more precise way to predict the chances.


Improvement in planning

Machine learning calculates different constraints and optimizes them before giving any type of result. In this way, it improves the production planning of a product. A few years back, manufacturers were completely dependent on BTO and MTS but after the arrival of ML, these constraints are balanced. As a result, manufacturers reduced the discontinuation in the supply chain of products.

Analytics is used as fuel to move the business at a faster rate, by attaining the goals on time. It is also behind the increase in the expectation of the customer. Analytics is working as a base from order fulfillment to the quality timelines. Every industry is working on customer-driven analytics to fulfill the need with quality on real-time. For example- enosiX, has done real-time integration in a different and unique style.


End-to-end visibility

With the help of IoT sensors and ML, monitoring can be done easily which helps in providing end to end visibility. Industries just need a platform which can help them to predict architecture on real-time which was not possible a few years back with analytic tools.

Every step of an item such as contract purchasing, development dates, requirements, the rate of current consumption, etc. is visible. These all are an important part of building supplier relationship which enables a long-term business. Monitoring and alignment are more needed in the case of contractual trust such that the compliance does not fail.


Conclusion

By implementing Enterprise Resource Planning (ERP) with Customer Relationship Management (CRM) provides the detail of the product such as price, product catalog, etc such that the relative information can be transmitted to the customer. With fine details by material requirement planning (MRP) the planner can optimize the product schedules in real-time. In this way, the areas of production can be improved and increased customer satisfaction results in higher revenue.

Being able to effectively manage performance and staff is crucial in transportation and logistics. Deputy makes it possible to streamline the review process so that business owners can quickly and efficiently evaluate employee performance. Give Deputy a try by signing up for a free trial below:

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