Supply shortages, rising prices, and a strained supply chain ecosystem have forced supply chain leaders worldwide to innovate. Artificial intelligence is the device powering this breakthrough (AI). AI is being used by forward-thinking businesses to innovate across the supply chain (SCM). AI has the potential to revolutionize supply chain management procedures in a variety of areas, including pre-season demand planning, merchandising decisions, planning and allocation, inventory availability, product assortment, inventory fulfillment, route optimization, and last-mile events.
Despite the apparent benefits of implementing AI, only a few organizations manage to implement AI-based solutions.
Defined by SCM
The definition, purpose, and critical processes of SCM are summarized in the following paragraphs.
1. Definition/Purpose:
“The design, planning, execution, control, and monitoring of supply chain activities to produce net value, construct competitive infrastructure, leverage logistics, coordinate supply with demand, and measure performance” is how supply chain management is defined.
2. Typical Supply Chain:
An extensive supply chain may have a network consisting of dozens of sources, central warehouses, hundreds of storage locations, and thousands of POS endpoints with storage. The links in the network are referred to as shipping lanes and define how products will be transported and where they will be stored. In such multi-level supply chains, inventory management is very complex. Diagram of a typical supply chain:
3. Business Processes: Basic Business Processes in the SCM Solution are Briefly Described Below:
Demand Planning (DP):
The business process for capturing demand is implemented using a combination of simple statistical functions such as moving averages and manually entered demand numbers. RP is often done for multiple time horizons such as short term (month), medium term (quarter), and long term (year). Short-term demand figures are finalized based on multiple inputs, viz. statistical prediction, entered by the sales team and/or derived from long-term planning.
As the size of the time horizon (time segment) decreases (say to the daily level), the accuracy of the forecast decreases significantly.
Demand numbers finalized are released to the next module (Supply Planning) at the required time intervals (day, week, etc.).
After taking into account current inventory levels at all storage places, inventory standards, push-pull methods, production capacities, set limitations, and many other supply chain design factors, the module provides an ideal supply plan. A significant mathematical optimization problem must be created and solved to perform SNP using the combined integer linear programming (MILP) method from the operations research (OR) tool library. Using MILP, which has continuous or integer-defined variables, is a very efficient optimization method (take on binary values). The next production planning module receives the output of the SNP module, which is the ideal supply plan.
Production Planning and Scheduling:
In addition to capacities at each step, SNP output is used for production planning and detailed planning based on specific constraints in the production environment (e.g., product sequencing, other dependencies, batch processing, etc.).
Delivery / Load Builder / Integration with transactional ERP: Includes generation of deliveries prioritized to avoid stock out at endpoints, load generation, etc. It is also integrated with a transactional ERP system.
4. ERP Vendors:
Leading vendors include SAP and Oracle, among others.
5. Challenges/Issues You Face:
Below are a few commonly encountered issues when implementing SCM processes:
A large number of products / SKUs
High level of inventory
Fixed products / SKUs
Loss of sales in several areas (due to shortages) while surplus in others.
Reactive logistics
Non-compliance with the plan, i.e., disciplinary matters.
Departmental focus (e.g., the sales team would like to enter demand numbers in line with sales targets, generally on the higher side. Which often leads to a bullwhip effect throughout the supply chain).
6. SCM KPIs: Typical KPIs Used to Monitor SCM Improvements:
Demand fulfillment index
Days of inventory (average)
Forecast accuracy (weighted average)
Fulfillment of delivery/adherence to dispatch
Production compliance
Compliance with public contracts
End-to-end cycle time (buy to sell)
Machine Learning Cases In The Supply Chain
Machine Learning applications in the supply chain revolutionize how suppliers and retailers do business.Machine Learning, a subfield of artificial intelligence, trains computer models to change their behavior based solely on data.
1. Logistics and Transport
ML helps to understand where the package is in the entire logistics cycle. Enables supply chain professionals to track the location of goods in transit. It also provides an overview of the conditions under which the package is transported. Using sensors, sellers can monitor such parameters as humidity, vibration, temperature, etc.
In addition, ML helps with real-time route optimization. It monitors the weather and road conditions and recommends optimizing the route and reducing driving time. Trucks can be diverted anytime en route if a more cost-effective way is possible.
2. Production
With ML, it is possible to identify quality problems in line production in time. For example, manufacturers can use computer vision to check whether the final appearance of products meets the required quality level. If the products have any defects, it is easy to detect them before they reach the customers.
Predictive equipment maintenance is another widespread use case for machine learning in the supply chain. ML provides reactive and preventive equipment maintenance based on real-time asset data rather than a pre-defined calendar. By improving asset maintenance, supply chain professionals can significantly reduce maintenance costs.
ML also helps to reduce the number of no-find cases (NFF). An NFF is a unit taken out of service based on a complaint of a detected equipment fault. If no anomaly is detected, the team will return to service without making the repair. The fewer such incidents, the more efficient the production process.
3. Chatbots
Intellectually independent chatbots based on machine learning technology are trained to understand specific keywords and phrases that trigger the bot’s response. They are widely used in supplier relationship management, sales, and procurement management, allowing employees to focus on value-added tasks instead of being frustrated by answering simple questions. Over time, they train themselves to understand more and more questions. They learn and prepare from experience.
For example, you write to the chatbot: “I have a problem sending the package”. The robot would understand the words “problem”, “shipment,” and “package” and provide a predefined response based on these phrases.
4. Security
Machine learning algorithms can analyze vast amounts of data and draw patterns for each business to protect it from fraud. For example, in the supply chain, ML helps identify fraudulent transactions, prevent credential abuse, accelerate fraud investigations, and automate anti-fraud processes. Additionally, with ML, supply chain professionals can automate monitoring of whether all parts and finished products meet quality or safety standards.
5. Business
Machine learning offers valuable insights that streamline and hasten decision-making from a business standpoint. Executives can evaluate best- and worst-case scenarios swiftly. Business executives may utilize machine learning to offer the best options using sophisticated algorithms.
How To Make ML Work For Supply Chain Management
You should take three essential steps to adopt machine learning in supply chain management. They are:
Understand The Structure Of Your Supply Chain
You should analyze the structure of your entire supply chain before incorporating machine learning:
Determine the essential elements of your activities.
Analyze the supplier network in depth, paying particular attention to Tier 1 and lower-tier providers.
Discover hidden connections and relationships.
Determine the supply chain’s relative fragility quantitatively.
Determine the supply chain’s risk factors and bottlenecks.
Make accurate comparisons with industry benchmarks and peers.
Check the security of the supply chain. Assess your functional maturity against the process, people, and technology.
Determination Of Transparent Business KPIs and Calculation Of ROI
To understand the circumstances under which machine learning use cases in your supply chain would benefit your business, you need to perform a discovery phase and calculate the ROI. You need to estimate the TCO and profitability you will get in the short and long term.
Preparing a detailed plan defining your goals and the requirements needed to achieve them is also essential. It is mandatory to align machine learning KPIs with business KPIs to eliminate inconsistencies. In other words, you should define the business problem in ML terms.
Ensuring An Efficient ML Engineering Process
The success of machine learning use cases in the supply chain largely depends on the following aspects:
Build a cross-functional team of professionals with experience in data science, DevOps, Python, Java, QA, business analytics, etc.
Start with a business problem statement.
Establish the right metrics for success.
Choose the right set of technologies.
Consider the readiness of your data: focus on data quality and quantity.
Develop, train, test, and optimize models.
Deploy and retrain models.
Monitor model performance.
Conclusion
The use cases for machine learning in supply chain management are versatile. Here, we’ve listed the ones that bring the most value to supply chain professionals. Managing your supply chain can be daunting if you manage an extensive network of suppliers, warehouses, and logistics service partners. However, supply chain management technologies like machine learning and AI can be helpful throughout the entire process. ML Algorithms benefit from demand forecasting accuracy, better logistics management, reduced paperwork, and automation of manual procedures. You acquire a comprehensive understanding of your supply chain. As a result, ensuring that it runs more effectively, consumes less energy, and is less susceptible to disruption.
