My article used survey research as well as secondary data gathered from various known sources. Using descriptive statistics, I analyzed the data. I also did SWOT analysis and BCG matrix analysis to take better decisions.
2. Domain of artificial intelligence: In the figure, we can get an idea about all the different fields AI is performing
Some examples of AI in real life which we are experiencing daily basis are:
- Robo readers for grading
- Online endorsement system
- Navigation and travel
- Fraud detection
- Autonomous vehicles
- Image recognition
- Chatbots
- natural language generation
- sentimental analysis
- self-driving cars
- robotics
- computer vision and much more….
Now the next thing to be considered here is how we can use AI intelligently to make efficient supply chain. So, let us know a little bit about the supply chain then we will connect this to AI.
About Supply Chain
A supply chain is a grid between a firm/business and its suppliers to produce and distribute a specific product to the final buyer/consumer. Network consist of different activities, people, entities, information, and resources. The supply chain also represents the steps it takes to get the product or service from its original form to the end user. Companies developed the supply chain to reduce costs and remain competitive in market. Supply chain management optimized supply chain for faster production cycle in low cost.
supply chain management is essential to any business to be successful. There are lots of issues faced by companies while performing supply chain, some of them include:
Figure:2
Supply chain management is the process and it has so many elements involve in it, let us try to understand with this table.
On the left column of the table explains elements at each level and supply chain and the right column explains the corresponding activities of each element.
Supply chain mechanization:
As it is known, supply chain is a network that connects all the business apparatuses such as manufacturing, procurement, planning, sales, and marketing together.
The ongoing impact of pandemic effects demonstrated how the Supply chainrings down the companies. Many organizations, therefore, add digital solutions frequently to optimize the operations of the supply chain
The foremost pronouncement zones in supply chain management are location, production, distribution, and inventory. Managers are taking either strategic or operational decision accordingly.
The location decision relates to the choice of locations for both production and distribution facilities. Where production and transportation costs and delivery periods are vital.
Production and distribution decisions focus on what, when and how much customers need. Subcontracting can be a contemplation.
Because transportation costs are often a huge portion of total costs, distribution decisions are strongly influenced by transportation costs and delivery times
The operational decisions are based on schedule, maintaining equipment, and meeting customer demands. It is also important to consider quality control and workload balancing.
It involves determining inventory needs and coordinating production and stocking decisions across the supply chain. In inventory decision, logistic management plays a crucial role
Logistic management plays a crucial role in inventory decisions.
AI based Supply chain
- The use of artificial intelligence in supply chains can increase efficiencies and improve customer service. Any company's strategy must include these goals, as they help the company save money and keep customers satisfied.
- In an agricultural production set, AI is helping by collecting substantial amounts of data which can be extremely hard to analyze as a human being, and according to those data decision-making is easier now. Oracle and automation can help companies shift from completing repetitive and tedious tasks to considering the bigger picture, which saves both time, money, and human error.
- In response to a continual inflow of new customer orders, AI is helping by predicting when inventory shortages will occur.
- AI facilitates Environmental Scanning Supply Chain Scope (macro) Demand
Supply risk and AI
1 Data collection and Data analysis
I conducted in-depth interviews and contacted 250 supply chain and AI professionals via email and LinkedIn messages in June 2022 and provided a brief introduction to the research topic. After multiple reminders, 67 respondents replied and showed interest in sharing their views on the topic. However, of these, only 46 respondents finally agreed to be interviewed. Interviews were conducted in July, telephonically and over Internet-based calls to get the response fast. To maintain confidentiality, we have anonymized the respondents (A1–A35, A stands for Attendee, 1–46 are Interviewees numbers) and provide full details in Table: Details of interviewees
Table:1
Interviewee profile no
|
Interviewee code
|
Job title
|
Years of experience
|
Respond in Favour of AI
|
Respond against
AI
|
1
|
A1
|
Supply chain analyst
|
7
|
Y
|
|
2
|
A2
|
Managers
|
<8
|
|
N
|
3
|
A3
|
Supply chain operation
|
10
|
Y
|
|
4
|
A4
|
Business analyst
|
10
|
Y
|
|
5
|
A5
|
Senior Manager
|
12
|
|
|
6
|
A6
|
Inventory Engineer
|
<9
|
Y
|
|
7
|
A7
|
Plant Manager
|
<6
|
|
N
|
8
|
A8
|
Distribution Manger
|
6-7
|
Y
|
|
9
|
A9
|
Executive consultant
|
8
|
|
N
|
10
|
A10
|
Supply operation Manager
|
9+
|
Y
|
|
11
|
A11
|
Executive Manager
|
14
|
|
N
|
12
|
A12
|
Associate Manager
|
12+
|
|
N
|
13
|
A13
|
Manager
|
<10
|
Y
|
|
14
|
A14
|
Supply chain Analyst
|
6
|
Y
|
|
15
|
A15
|
Business operation Manager
|
7
|
Y
|
|
16
|
A16
|
Inventory Manager
|
5
|
Y
|
|
27
|
A17
|
Vice president sales
|
8
|
|
N
|
18
|
A18
|
Engineer
|
5
|
Y
|
|
19
|
A19
|
Inventory Manager
|
<9
|
Y
|
|
20
|
A20
|
Inventory Manager
|
7+
|
Y
|
|
21
|
A21
|
Supply chain analyst
|
6
|
Y
|
|
22
|
A22
|
Managers
|
9
|
Y
|
|
23
|
A23
|
Executive consultant
|
12
|
|
N
|
24
|
A24
|
Supply planner
|
15
|
Y
|
|
25
|
A25
|
Managers
|
19
|
Y
|
|
26
|
A26
|
Director
|
17
|
Y
|
|
27
|
A27
|
Business operation Manager
|
6
|
Y
|
|
28
|
A28
|
Consultant
|
8
|
|
N
|
29
|
A29
|
Supply chain analyst
|
<9
|
Y
|
|
30
|
A30
|
Managers
|
6-7
|
Y
|
|
31
|
A31
|
Associate consultant
|
<8
|
|
N
|
32
|
A32
|
Supply operation Manager
|
6+
|
Y
|
|
33
|
A33
|
Director
|
12
|
Y
|
|
34
|
A34
|
Executive consultant
|
14
|
|
N
|
35
|
A35
|
Business operation Manager
|
10
|
Y
|
|
36
|
A36
|
General Manager
|
<8
|
Y
|
|
37
|
A37
|
Inventory Manager
|
<5
|
Y
|
|
38
|
A38A39
|
Supply planner
|
7+
|
Y
|
|
39
|
A40
|
Associate consultant
|
4-5
|
Y
|
|
40
|
A41
|
Supply chain operation Manager
|
6
|
Y
|
|
41
|
A42
|
Director
|
<6
|
Y
|
|
42
|
A43
|
Managers
|
9
|
Y
|
|
43
|
A44
|
Executive consultant
|
10
|
|
N
|
44
|
A45
|
Business Analyst
|
12
|
Y
|
|
45
|
A46
|
Supply operation Manager
|
10
|
Y
|
|
Total= 45 response
Number of people in Favour of AI and support that it is significance role in industry= 33
P (F)= 33/45= .73333/73%
Number of people not in Favour of AI and support that it is significance role in industry=11
P(NF)= 11/45=.24444/24%
some factors that can be considered in supply risk and after interviewing these 45 people I came to the below are the point to be considered for use of AI in this area.
- Often, suppliers struggle to find staff to run their facilities, and training new employees slows down production as well. Here AI helps and plays a key role, companies are using AI for time being or replacing the labor requirement, usually for repetitive kind of work and which can perform by AI in better way.
- Preventative maintenance (PM) planned to limit unscheduled down time. PM is known to decrease the number of unplanned machine breakdowns, which helps plants stick to the production schedules.
- Using artificial intelligence, we can monitor production delays, request additional parts because of quality issues or higher demand than forecasted, and decide on new and existing suppliers based on the best, most informed information.
- In many companies, filling in informational gaps after employees’ leave is a major challenge.
- When one of the members of the company leaves, the rest will find it extremely difficult to cope. At this point in time companies are using AI to fill in the gaps.
- In many companies, language barriers are a major issue. Many of these language barriers can be eliminated using artificial intelligence.
- It is important to manage supplier-customer relationships and ensure on-time delivery by working with customers. Companies are using chatbots, or automated communication systems that answer questions and provide customer service. AI also helps to reduce “... the amount of time everyone spends trying to make sure customer orders get shipped on time” (Green, “Artificial Intelligence for Real-Time Manufacturing Execution and Operations Management”).
- In production process, AI is helping to identify a depraved portion of produce by spotting these bad parts through cameras.
- In many cases, future sales can be predicted using data mining and predictive analysis, both of which can be assisted by artificial intelligence. Using artificial intelligence, companies can manage this data more efficiently, produce better products, and be more productive.
Global AI supply chain market from 2020 to 2026
Artificial Intelligence is a popular technology that is making every sector smarter and more resilient. The global supply chain AI market is projected to reach $13.5 billion (about $42 per person in the US) by 2026 (See Figure: 5
Table:2
Use-case of AI in food and beverages supply Chain planning and for casting.
By Application
|
- Food sorting
- Quality control and safety compliance
- Consumer Engagement
- Production and Packaging
- Maintenance
- Other Applications
|
By End User
|
- Food Processing Industries
- Hotel and Restaurants
- Beverage Industry
|
Geography
|
- North America
- Europe
- Asia -Pacific
- Rest of the World
|
AI improves supply chain planning by helping food and beverage companies to forecast demand against product supply and ingredient orders. The AI platform can take inputs from supply chain management professionals to develop algorithms that inform procurement decisions.[1]
Delivery of ingredients to the appropriate manufacturing facilities can also be optimized, as AI allows for highly specific predictions around when and where resources are needed.
Warehouse and inventory management in food supply chain
AI algorithms can also help forecast demand on warehouse resources and analyze inventory against shipments going out and new product coming in.
Automated procurement
In a far-off (or not so far-off) future, we could see the size of procurement teams shrink as the use of automation grows. Procurement teams may be focused more on finding the right vendors and establishing vendor connections than decisions to get what is needed from short supply chains.[i]
Quality assurance and protecting global food supply
Cornell University and IBM are partnering up to utilize AI to learn how to better protect the global food supply, so that when some amount of product is contaminated, thousands or millions of pounds do not have to needlessly go to waste.
Food and beverage manufacturers are utilizing AI through better predictions of what food has been contaminated, to make sure that these products do not reach consumers.
Remove language barriers that threaten auditing and compliance
Language barriers can be a huge problem when communicating with suppliers over product specs, agreements, certifications, and other nitty gritty details. This later creates a potential risk for auditing and compliance issues. In your view, the supplier agreed to something that they did not agree to. Or you interpreted their explanation in a separate way.
AI technology for supply chain management and procurement can alleviate the risk of foreign communication by reading foreign language data on your behalf and translating it into data that can be understood and utilized.[2]
Driverless vehicles and other logistics optimizations
Tesla is working on a driverless semi-truck that has the potential to revolution warehousing, logistics, distribution, and transportation in every physical product industry – not just food and beverage.
Consumer visibility
One of the key use cases for AI in the food and beverage industry is offering better visibility into what consumers need and how frequently. Currently, data silos between marketing and product creation can negatively impact sales.
Better consumer visibility should be able to come from a variety of various sources: federal or demand forecasts for certain products and trends, private demand forecasts that manufacturers and brands can purchase, sales data from retailers, demand statistics from real product orders, and more.
Without AI, it would be impossible for humans to integrate, clean, and utilize all these disparate data sources.
Difficulties to implement AI
As we all know an area influences machine learning and artificial intelligence to help enterprise retailer and organizations make better decisions across their supply chain.
AI being utilized in a couple diverse ways from kind of pre-season demand planning through to merchandising and buying decisions through planning and allocation and decision in the terms of in inventory availability and assortment. AI being utilized to decide what to make available across the supply chain throughout like where to fulfill the inventory from across distribution centers and now also to stores, also through the rout optimization and last mile. so, it is across industries. It is outside of retail also but retailer and brands are main utilizers, they are pure AI- players e-commerce and some PG also in the list of users of AI.
If we analyzed applicability of AI in current scenario, there is lots of opportunities to leverage machine learning and AI with obviously some challenges to implement it outside of the lab and bringing it market and commercialization it and productizing it.
Although there all lots of challenges but the biggest one the data itself as if one think about AI, it is really for identifying patterns making better decisions and predicting, making predictions about business outcomes based on data, so it difficult to collect data and curate, to cleans, to get in to format to use for business.
AI needs technologies and skill to engineer the data to build that pipeline to bring that data to organization and adjust in the way as its needed so that analytics runs it and make better decisions.
Blackbox – there is lots of concerns about black box and artificial intelligence when AI start to enter in the business, there was lots of concerns about how can machines take a decision for human?
So, to clarify that for solving very discrete problems and isolating the problem space organization have more visibility into how decision is being made, transparency about the computation and the decision-making process the software is working that the model is running and that the transect in all the organization and all the units is essential. Visibility of metrics is important to looking at the actual business impact and sharing its cross functionality is critical.
The benefits that the company is going to receive after AI investment is to solve very discrete problem so that you quantify the current stage to so company can understand how decision can be today which track those decisions and documents.
Another thing is comparing, the ways that AI model is running the machine and is making those decisions. So, comparison is important and having a closed loop in terms of what was the decision-making process before and what is today.
In supply chain, this type of technology operative in every chain of process in next five year if we see the picture for the supply chain as different retailers and manufacturer and even CPG (Consumer Packaged Goods) as they make decisions in terms of where to make investments in their supply chain, they are going to see impact on customer service, customer promise state. AI will make competent to compete in the market, and to know better the customer requirement for further decision.
Digital platforms help in tracking the discarded packaging material and products. It also helps customers to get salvage value for the discarded product and feel motivated to cause and loyal to the company.
[1] https://www.gartner.com/en/information-technology/glossary/scp-supply-chain-planning
[2] https://www.cips.org/supply-management/news/2018/may/the-future-of-procurement-four-scenarios/