Over this period there have been numerous examples where risks have materialised, impacted, and been seemingly absorbed by some organisations, while other organisations have never really recovered. What differentiates the overall effect of the impact is how prepared these organisations were to deal with the risk once it materialised. With the growth of outsourcing and the evolution of multi-tiered global supply networks many organisations now face direct and growing operational and legislative risk from disruption or malpractice in their supply chains.
The combination of large-scale IoT infrastructures, deeper analytical processing and machine learning tools has created platforms dedicated to reforming business intelligence processes. If done right, companies can take this string of technology innovation and create a system providing not just end-to-end visibility, but also recommendations and the ability to implement actions autonomously in real time.
Introducing the cognitive operating system Intelligent supply chains boast the ability to perform continuous predictive analytics on enormous amounts of data and use machine learning models to review historical information to plan for current and future needs. Companies already have the needed data from their transactional systems.
|A Risk Intelligent approach to managing global supply chains | Deloitte | ERM, Services||Zobel, and John R. Macdonald When Boeing announced plans to assemble the Dreamliner in lateit introduced a new concept to the assembly of a commercial aircraft.|
In some cases, their system has to crawl 54 different ERPs at one company, which is not unusual. Even if the company has standardized their ERPs, they have many different modules. The system has to connect directly or indirectly to all the signals, and that affects performance.
Is it better to scale systems up, or out? One of the big decisions a company will make when choosing the architecture for their intelligent supply chain is what cognitive operating system to use, which means deciding whether to scale up or out.
Zweben said his company uses distributed architecture and the power of the cloud to scale out. Instead they string together numerous inexpensive computers to scale larger data sets, with fast parallel computing.
It can be connected within minutes, Zweben said. The brain does the job, and the data feeds the brain. Frederic Laluyaux President and CEO, Aera Technology Splice uses different computing engines depending on the function, like machine learning and streaming.
They link the machines together behind the scenes, giving the customer one interface, one integrated package.
Data risk is something to consider when implementing any new IT system, especially one that connects all the parts to create end-to-end visibility, make recommendations and take autonomous action.
The most obvious risk is data access. Building trust in those machine learning recommendations is something society is already getting used to. But you must know that the data is accurate, secure and tested. With intelligent supply chains, businesses are asking systems to make real time recommendations, so there must be trust in the system outcomes.
Who is paving the path to intelligent supply chains? The implementation decision comes from the C-suite. Most of the time is spent on the user interface and testing. The applicability of machine learning has gone from it being a black art for a bunch of PhDs, to being a tool that is available for everyone to use.
While the cost can be significant, said Laluyaux, the ROI is strong. The decision to scale up or scale out also impacts cost, said Zweben.
The other part of paving the way is change management. Changing to an intelligent supply chain requires human behavioral changes. Companies are, however, digitizing their manual processes, and digitizing their physical assets as well as they can.
Inventory is tracked electronically using sensors, versus people scanning it. Fleets of trucks and containers are constantly reporting in their location. Using all this data effectively requires change.
Are we there yet? Even if a company is ready for the future, is the technology ready? What makes it possible now, instead of five years ago when the data lakes were filling up, is the ability to store, memorize and compute vast amounts of transactional data, using required business logic and machine learning in real time.
It might get two out of three. An intelligent supply chain, he says, has four levels — wherein the first three are currently feasible, and the fourth is in pilot testing.
Level 1 — real time end-to-end supply chain visibility. Level 2 — recommendations. These are real time insights, where the artificial intelligence can learn from past decisions and make recommendations.Artificial intelligence and automation are bringing new levels of efficiency and better planning capabilities to the supply chain.
While it’s improving business outcomes, there’s . Supply Chain Resilience 1 Preface This publication is the 25th whitepaper in Deloitte’s series on Risk Intelligence.
The concepts and viewpoints. A Strategy for Supply Chain Security and Resilience in Response to the Changing Character of War Chris Nissen, John Gronager, Ph.D., intelligence perspective and act on it. Risk quan-tification and mitigation, as a mission, receive Form a Whole-of-Government National Supply Chain Intelligence Center (NSIC) (ST).
Ultimately, companies seek to translate climate risk intelligence into practical, operational strategies to build supply chain resilience.
This guide offers food and beverage companies a. Supply chain resilience A Risk Intelligent approach to managing global supply chains Managing risk has always been an important part of supply chain management.
Supply chain resilience: A Risk Intelligent approach to managing global supply chains Global Vice Chairman Risk Advisory. [email protected] Adel Melek is Deloitte's Global Vice Chairman of Risk Advisory and Global Lead Client Service Partner for Royal Bank of Canada. As part of the Deloitte Canada member firm, Adel has over.