Machine learning in supply chain management
Recently, the use of machine learning in supply chain management has significantly grown around the world due to the higher demand for data transparency and traceability. And that’s not a surprise! Businesses get real-time information and key factors affecting service quality, demand forecasting, inventory volumes, logistics, and much more by implementing machine learning (ML) work for supply chain.
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Let’s dive deeper into the actual use cases of machine learning in supply chain management; the reasons to develop flexibility and agility of supply chains as well as the challenges the businesses should overcome towards implementation of cognitive IT platforms.
- Most popular challenges in the supply chain
- Top 9 use cases of machine learning in supply chain
- How to adopt machine learning in supply chain management
Most popular challenges in the supply chain
First, let’s define what is Supply Chain Management (SCM) since many people still equate the supply chain only with logistics, which is actually just one component of the SCM. Modern supply chain operations cover purchasing, product lifecycle management, supply chain planning (including maintenance of enterprise assets and production lines), logistics (including transportation and fleet management), and order issues.
To rely on obsolete interaction formats and manual data processing for planning and managing supplies is the past era. Especially, Covid-19 and the post-pandemic period with a fast-changing environment have proven that traditional methods are ineffective for the majority of businesses with deep supply chain addictions.
However, to switch quickly to ML work for supply chain management isn’t the one-day solution. The organization should analyze its existing supply chain applications and identify a step-by-step way for automation.
To illustrate, we can outline the next most popular challenges in the supply chain:
- forecasting demand across multiple product segments and geographic regions;
- availability of high quality, consistent real-time data;
- for managing the broader value chain, the integration of such solutions as processing optimization, predictive analytics, and mastering data quality;
- availability of supply chain data in different departments since usually marketing, inventory, purchasing, and other departments have their own databases;
- limited integration between systems and databases for accessing, cleaning, and analyzing data;
- ensuring that plans are completed on time and that they adapt to the effects of volatility (such as demand shocks, production interruptions, etc).
The way to solve these critical issues is to apply machine learning techniques. With them, a business has a complete picture of the entire ecosystem; can accurately predict supply and demand; optimally plan logistics and delivery, etc. Further, innovative methods allow organizations to accurately anticipate supply utilization and take preventive steps in advance.
Let’s explore in detail the use cases of ML work for supply chain.
Top 9 use cases of machine learning in supply chain
Decisions have to be made on the basis of most actual information to better meet the expectations of suppliers and consumers. ML can disrupt disparate structures to provide continuous visibility across the entire supply chain. This enables companies to operate their supply chains more efficiently and be resilient enough, even in the face of day-to-day volatility.
In fact, according to Gartner’s research, 50% of global product-oriented businesses will invest in real-time transportation monitoring platforms by the year 2023. Such an approach addresses the problem of visibility of the status of customers’ orders that provide significant value to users.
The predictive analytic algorithms, based on big data, provide clear information on where supply chain problems may arise in the future. The existing methods range from basic statistical analysis to advanced simulation. For example, the analysis of data such as weather forecasts and port congestion allows businesses to predict the impact on freight traffic in transit and determine which shipments might be delayed.
The automation of document processing
The robotization of document processing can include reconciliations, creating invoices, collecting statistics on inventory balances and sales. It allows businesses to remove the routine work from an employee and establish transparent communication with counterparties and customers. In our latest article, find more how by outsourcing FreySoft data scientists, our client could reduce the average handle time of the received invoices by up to 50% and strongly improved its operational efficiency at a lower cost.
Traditional methods of forecasting are solely based on historical data. On the contrary, ML solutions enable organizations to collect information from multiple contractors, customers, and their own suppliers, inventory, and products in real-time and then use it to make accurate planning. ML tools help to balance demand and supply gaps, efficiently plan production activities, and develop error-free SCM strategies.
Optimization of the logistics
ML work for supply chain management enables to analyze existing routes, identify bottlenecks and focus on the best logistic solutions. Because of ML data processing tools, the business captures the details of moving goods in real-time and correctly estimates delivery terms. This reduces both the time and the total cost of warehousing and transferring.
The ML work for supply chain management allows minimizing the risk of failures in work of warehouse management. It automates and optimizes all warehouse processes at an enterprise from receipt of goods to shipment. In particular, the system takes into account the expiration date, storage conditions, demand, and so on; as well as instantly updates the information about the location of goods, completed tasks, and planned manipulations.
Risk and fraud prevention
Unexpected payouts or risks negatively impact the ROI. With ML tools, inspections are automated, and results are uploaded in real-time to a secure cloud platform. Accordingly, machine learning algorithms provide information that immediately reduces the risk and likelihood of fraud. Moreover, businesses can minimize attack surfaces, improve auditing, and reduce the complexity, and cost of operating a modern enterprise.
Reduction of forecast errors
According to McKinsey Digital, through using ML work for SCM systems, the business reduces by up to 65% of the sales losses based on product lack and 20-50% reduction in inventory today. That is to say, the solutions, that ML techniques offer, speed up the decision-making and carry out the performance more accurately.
Interaction with consumers
Machine learning technologies can combine human speech recognition and purchase history data. Thus, the system itself can provide a quick response to a consumer’s request. In these terms, your customer feels connected and valuable and is more likely to come back for your services again. If Fintech is your niche, find more about ways of enhancing customer experience in our previous article.
Also, retailers, food and beverage manufacturers, restaurants, and online deliveries are using ML in the supply chain to analyze social media content. Likewise, companies are already getting benefits by introducing voice-assisted chatbots. That is to say, consumer sentiment, combined with other data, is a huge key to a business’s retention rate.
How to adopt machine learning in supply chain management
We define the following phases of adoption ML work for supply chain management:
- Analyzing the existing components of SCM
To understand the value ML can bring to your SCM, first you need to evaluate your existing supply chain structure. In particular, this process includes a detailed analysis of your operations, security, and functional maturity in terms of people and technology.
- Identifying weak points and barriers
Compare your SCM analyses and researches with market benchmarks and your direct competitors. In such a way, you can draw up a clear picture of what needs to be the current priority and figure out the particular ML use cases that will bring actual value.
- Determining business goals in ML terms
Prioritize the functional areas of supply chain management and then develop target values for KPIs to improve them. At this stage, also define the processes and mechanisms that affect the KPI. It should be noted that the identified processes can relate to different divisions of the company.
- Integration of technologies
After ensuring alignment of ML capabilities with your prior business goals, define which areas of supply chain management you will optimize and proceed to the piloting stage. It would be advantageous to turn to experts in this expertise. Why? Read in our latest article.
- Make the effective ML process
At this stage, your team or outsourcing vendor considers data readiness, its quality, and quantity. Read more about it in an interview with FreySoft Data Scientist and Machine Learning engineer. Also, at this phase, you establish the tech stack, success metrics and accordingly, develop, train, and optimize models.
- Development through continuous improvement
After deployment, continuously assess the model’s performance and carry out monitoring of KPI implementation. Such an assessment of KPI provides information on the further directions of development and improvements towards the digital supply chain.
To summarize, the integration of ML development services is a splendid solution for supply chain management if you are looking to:
- hand off repetitive/ routine processes to complete with speed and accuracy;
- build end-to-end visibility, predictive maintenance, and analytics, thanks to better processing of large data volumes;
- control logistics and warehouse management in real-time to prevent interruptions and oversupply;
- streamlined production and workforce planning due to the automatic computation of better solutions;
- improve customer service, and several other business functions (ex. with the help of conversational AI platforms);
- integrate already designed and trained neural networks into machine learning solutions to increase efficiency at a lower cost;
- prevent anomaly and detect fraud actions, etc.
No doubt, machine learning has become one of the most promising technologies that bring actual value to supply chain efficiency. However, to reap the full benefits of its tools in the future, businesses should invest in this expertise today.
In fact, it is the right concerted action between business and IT vendors that leads to effective and optimal supply chain management. We at FreySoft help our clients to more correctly build the paths of development and transformation of processes through the introduction of innovative tools. In practice, this can be both businesses consulting on the effectiveness of processes and our teams’ involvement in the developing of future solutions.
Each case is specific and requires initial custom research. That is why, at FreySoft, our experts first conduct research, gather the requirements, and further advise the best approach towards your business needs. Contact us today to get a personal consultation on your ML work in supply chain management.
Machine learning tools revolutionize supply chain management without the need for manual intervention or even defining a taxonomy for diagnosis. With ML tools, the supply chain applications evaluate large and varied collections of data, improving the accuracy of demand forecasting; identify the key variables that most affect the functionality of the distribution chain; reduce delivery time and costs; and take over customer support functions.
Artificial Intelligence (AI) is used in the supply chain to efficiently solve in real-time various issues of management, procurement, customer service, logistics, etc. This can be from route optimization to forecasting demand. With AI, businesses can automate repetitive tasks and processes across supply chain functions. In addition, it can provide the implementation of alternative forms of strategic decision-making and cooperation.
Businesses are turning to innovative tools to run their warehouses and distribution systems since traditional supply chain models are prone to disruption and human errors. In turn, supply chain automation takes a central role, making it possible for businesses today to actually simulate the future demand, production, and form orders for a supplier, transfer, purchase, etc. That is, to reach a qualitatively new level of growth.