Lesson from KFC: Logistics is not simply a cost center
This has reference to news item in Forbes online article about issues with KFC logistics system shutting off supply of chickens to two-thirds of its UK stores. Excerpt is quotes below:
“Logistics is Not a Cost Center: Too many business leaders still think of logistics and transportation simply in terms of cost. While shipping expense is obviously something to control, it is also inexorably tied to service. The customer experience, which in the case of KFC includes both consumers and franchise owners, depends on logistics nearly as heavily as it does on the quality and value of the product.”
Full article can be read at the reference provided below.
Artificial Intelligence in Supply Chain Management
What’s Artificial Intelligence?
Artificial intelligence can be defined as the use of computers to simulate human intelligence, specifically including learning – the acquisition and classification of information, and reasoning – finding insights into the data. At the core of artificial intelligence is the ability to recognize patterns across the 3Vs of big data (volume, velocity and variety) and find correlations among diverse data.
Today, the term artificial intelligence encompasses everything from speech recognition to machine vision and from chatbots to collaborative robotics. The benefits of this technology lie in speed and accuracy beyond the reach of human capabilities.
Artificial Intelligence and Future Supply Chains
“Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
These were the words used in 1955 to launch the very first research project that coined the term ‘artificial intelligence’.
Fast forward 60 years and artificial intelligence – or machine learning as many call it – is emerging as the next big technology. 2016 has seen a race for artificial intelligence, with a number of acquisitions and large technology vendors – of the likes of IBM, Google and Amazon – launching new artificial intelligence-enabled products.
In SCM World’s 2016 Future of Supply Chain Survey, we found big jumps in importance for a series of disruptive technologies with respect to supply chain strategies, some of which were considered largely irrelevant just a couple of years ago.One of these is machine learning, which in 2016 cemented its place in the technology mainstream. 47% of supply chain leaders from our larger community believe that artificial intelligence is disruptive and important with respect to supply chain strategies. The technology grew so rapidly in importance over the last couple of years that in 2014 it wasn’t even included in the research!
Artificial intelligence can be defined as the use of computers to simulate human intelligence, specifically including learning – the acquisition and classification of information, and reasoning – finding insights into the data. Today, the term artificial intelligence encompasses everything from speech recognition to machine vision and from chatbots to collaborative robotics. The benefits of this technology lie in speed and accuracy beyond the reach of human capabilities, which is also feeding a debate about its implications in the future of work.
Business activities that require to collect and analyze lots of structured and unstructured data can benefit from artificial intelligence and its ability to support faster and smarter decision making. Supply chain is therefore a natural fit for artificial intelligence.
Artificial Intelligence and Supply Chain
An interesting 2010 research paper from Dr. Hokey Min from the College of Business at Bowling Green State University, predicted a number of applications of artificial intelligence in supply chain management. These include setting inventory safety levels, transportation network design, purchasing and supply management, and demand planning and forecasting.
Today’s artificial intelligence is mature enough to make some of those applications possible:
- Capitalizing on the machine-learning capabilities of IBM’s Watson, IBM has recently launched Watson Supply Chain aimed at creating supply chain visibility and gaining supply risk insights. The system uses cognitive technology to track and predict supply chain disruptions based on gathering and correlating external data from disparate sources such as social media, newsfeeds, weather forecasts and historical data.
- ToolsGroup’s supply chain optimization software is rooted in machine-learning technology. One area of application is new product introduction. The software begins with creating a baseline forecast for the new product. As the algorithm learns from early sell-in and sell-out demand signals, it layers this output to determine more accurate demand behavior, which feeds through to optimized inventory levels and replenishment plans.
- The machine-learning technology of TransVoyant is able to collect and analyze one trillion events each day from sensors, satellites, radar, video cameras and smartphones. In logistics applications, its algorithm tracks the real-time movement of shipments and calculates their estimated time of arrival, factoring the impact of weather conditions, port congestion and natural disasters.
- The technology firm Sentient uses machine learning to deliver purchasing recommendations to e-commerce shoppers based on image recognition. Rather than only using text searches and attributes like color or brand, the software find visual correlations with the items that the shopper is currently browsing through visual pattern matching.
- At the core of Rethink Robotics’ collaborative robots is an artificial intelligent software that allows the robot to perceive the environment around it and behave in a way that’s safe, smart and collaborative for humans working alongside production lines.
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 it’s ‘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.
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 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 under stocking) 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 capabilities could reshape warehouse management as we know 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 the recent years. Faster and more accurate shipping reduces lead times and transportation expenses, adds elements of environmental 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.
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 took, could build data sets regarding suppliers, and decipher untapped information, due to language barrier. 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).
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.
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