6 Applications of Artificial Intelligence for your Supply Chain.
Once thought to be a concept only sci-fi movies could produce, Artificial Intelligence (AI) has become a topic of our mainstreams and every day.
The potential of AI enhancing everyday business activities and strategies hasn’t just sparked the interest of people and organizations globally but has initiated rapid implementation.
But, what is AI?
Artificial Intelligence is intelligence displayed by machines, in which, learning and action-based capabilities mimic autonomy rather than process-oriented intelligence.
The simplest way to understand the potential application of AI is to clearly define its potential value added.
Introduced by Gartner Analyst, Noha Tohamy, at Gartner’s Supply Chain Executive Conference, AI was broken down into two categories:
- “Augmentation: AI, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such Artificial Intelligence is used in Virtual Assistant, Data analysis, software solutions; where they are mainly used to reduce errors due to human bias.
- Automation: AI, which works completely autonomously in any field without the need for any human intervention. For example, robots performing key process steps in manufacturing plants” (arkieva.com 2017).
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Understanding these two categories of AI capacities is important for future implementation of AI into business work tools. In particular, the application of AI into Supply Chain related-tasks holds high potential for boosting top-line and bottom-line value.
Previous studies, by the Tungsten Network, have suggested that valuable time and money is wasted on trivial supply chain related-tasks that are conducted operationally by humans.
“Businesses estimate they spend on average per week around 55 hours doing manual, paper-based processes and checks; 39 hours chasing invoice exceptions, discrepancies and errors and 23 hours responding to supplier inquiries” (mhlnews.com 2017).
This loss- has been equated to around 6500 hours, during the work year, that businesses are throwing away by processing papers, fixing purchase orders, and replying to suppliers.
Imagine if a business could automate such tasks that are (more or less) ‘wasting time’.
Well… There’s not really any need to imagine anymore. Companies, even at that enterprise level, have already begun the implementation of AI tech into everyday supply chain tasks. Tech vendors such as IBM, Google, and Amazon have released products that utilize artificial intelligence.
“McKinsey estimated that tech giants such as Google and Baidu spent some $20 billion to $30 billion on AI last year, of which 90% was on research and development and the rest on acquisitions of intellectual properties or companies” (asq.org 2017).
The graphic below shows a breakdown of the applications of AI in 835 different companies in the past year.
While the potential for AI application and implementation is quickly visualized in this graph, it lacks a percentage designated to supply chain management (SCM). That’s because, of the companies surveyed, the application of AI into SCM-related activities hasn’t been actualized on a wide scale.
“Only 2% are using artificial intelligence to monitor internal legal compliance, and only 3% to detect procurement fraud (e.g., bribes and kickbacks). […] Only 7% of manufacturing and service companies are using AI to automate production activities. Similarly, only 8% are using AI to allocate budgets across the company. Just 6% are using AI in pricing” (hbr.org 2017).
How can AI be applied within SCM activities?
- Chatbots for Operational Procurement:
Streamlining procurement related tasks through the automation and augmentation of Chabot capability requires access to robust and intelligent data sets, in which, the ‘procuebot’ would be able to access as a frame of reference; or its ‘brains’
As for daily tasks, Chatbots could be utilized to:
· Speak to suppliers during trivial conversations.
· Set and send actions to suppliers regarding governance and compliance materials.
· Place purchasing requests.
· Research and answer internal questions regarding procurement functionalities or a supplier/supplier set.
· Receiving/filing/documentation of invoices and payments/order requests (Smith 2016).
2. Machine Learning (ML) for Supply Chain Planning (SCP)
Supply chain planning is a crucial activity within SCM strategy. Having intelligent work tools for building concrete plans is a must in today’s business world.
ML, applied within SCP could help with forecasting within inventory, demand, and supply. If applied correctly through SCM work tools, ML could revolutionize the agility and optimization of supply chain decision-making.
By utilizing ML technology, SCM professionals — responsible for SCP — would be giving the best possible scenarios based upon intelligent algorithms and machine-to-machine analysis of big data sets. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather action setting for parameters of success.
3. Machine Learning for Warehouse Management
Taking a closer look at the domain of SCP, its success is heavily reliant on proper warehouse and inventory-based management. Regardless of demand forecasting, supply flaws (overstocking or understocking) can be a disaster for just about any consumer-based company/retailer.
“A forecasting engine with machine learning just keeps looking to see which combinations of algorithms and data streams have the most predictive power for the different forecasting hierarchies” (forbes.com 2017).
ML provides an endless loop of forecasting, which bears a constantly self-improving output. This kind of capability could reshape warehouse management as we know it today.
4. Autonomous Vehicles for Logistics and Shipping
Intelligence in logistics and shipping has become a center-stage kind of focus within supply chain management in recent years. Faster and more accurate shipping reduces lead times and transportation expenses, adds elements of environmentally friendly operations, reduces labor costs, and — most important of all — widens the gap between competitors.
If autonomous vehicles were developed to the potential — that certain business analysts and tech gurus have hypothesized — the impact on logistics optimization would be astronomical.
“Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost” (techcrunch.com 2016).
5. Natural Language Processing (NLP) for Data Cleansing and Building Data Robustness
NLP is an element of AI and Machine Learning, which has staggering potential for deciphering large amounts of foreign language data in a streamlined manner.
NLP, applied through the correct work taken, could build data sets regarding suppliers, and decipher untapped information, due to the language barriers. From a CSR or Sustainability & Governance perspective, NLP technology could streamline auditing and compliance actions previously unable because of existing language barriers between buyer-supplier bodies (greenbiz 2017).
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6. ML and Predictive Analytics for Supplier Selection and Supplier Relationship Management (SRM)
Supplier selection and sourcing from the right suppliers is an increasing concern for enhancing supply chain sustainability, CSR, and supply chain ethics. Supplier-related risks have become the ball and chain for globally visible brands. One slip-up in the operations of a supplier body, and bad PR is heading right towards your company.
But, what if you had the best possible scenario for supplier selection and risk management, during every single supplier interaction?
Data sets, generated from SRM actions, such as supplier assessments, audits, and credit scoring provide an important basis for further decisions regarding a supplier.
With the help of Machine Learning and intelligible algorithms, this (otherwise) passive data gathering could be made active.
Supplier selection would be more predictive and intelligible than ever before; creating a platform for success from the very first collaborations. All of this information would be easily available for human inspections but generated through machine-to-machine automation; providing multiple ‘best supplier scenarios’ based on whatever parameters, in which, the user desires.
What’s the catch?
One could hypothesize that SCM is a part of the value chain that would be heavily impacted by AI implementation, for the better and the worst. Of course, augmentation and automation raise security and safety concerns for IT infrastructure and human life.
But, something that is potentially even more threatening to business: AI implementation will begin replacing jobs.
“Four years ago, an Oxford University study predicted 47% of jobs could be automated by 2033. Even the near-term outlook has been quite negative: A 2016 report by the Organization for Economic Cooperation and Development (OECD) said 9% of jobs in the 21 countries that make up its membership could be automated. And in January 2017, McKinsey’s research arm estimated AI-driven job losses at 5%” (hbr.org).
These numbers are tough for most to accept, but the true future of business lies within machine-to-machine work; the automation of — currently — human manned positions.
Until next week.
This publication is brought to you by author Sam Jenks, but also in part by Kodiak Rating — A Supplier Relationship Management SaaS functioning out of Stockholm, Sweden. Kodiak Community intends to challenge traditional business practices with innovative thinking and creation.