Supply chain leaders are facing a wave of volatility and rising complexity. Supply chain networks are under strain and facing ever more risks. At the same time, companies are experiencing data fatigue and cannot keep up with the pace of change using traditional tools. In order to thrive in this fast-paced business environment and, "to build a successful bimodal supply chain, chief supply chain officers must balance operational excellence with disruptive innovation" (Gartner, Inc., Hype Cycle for Chief Supply Chain Officers, 2016, Noha Tohamy, July 11, 2016). Gartner's Hype Cycle for Chief Supply Chain Officers provides an overview of the critical technologies, business frameworks and competencies supply chain leaders need to achieve this. AIMMS can be leveraged to build several of these competencies, including Prescriptive Analytics capabilities and Network Design, as mentioned in the report. It also provides decision support for planning, manufacturing, S&OP/IBP, pricing and much more.
Gartner defines Prescriptive Analytics as "a set of analytical capabilities that finds a course of action to meet a predefined objective, such as maximizing revenue or minimizing costs" - the output of which is a recommended action. As stated in Gartner's Hype Cycle, the use of Prescriptive Analytics can improve decision making in several areas of the supply chain, including logistics, planning, and manufacturing.
AIMMS clients such as Nike, Air Liquide, BP, JBS and Shell leverage Prescriptive Analytics to support fact-based, real time decision making in critical supply chain planning processes. The flexible AIMMS platform can be used for network and inventory optimization, planning & scheduling, cost to serve segmentation, boosting ERP agility and much more.
Advances in data science are sparking more creative business opportunities. While much of the hype is for artificial intelligence and deep learning, this Hype Cycle shows the breadth and depth of excitement about data science, covering new technologies and improvements. AIMMS is mentioned in the report as a vendor for Optimization. Gartner's Lisa Kart defines Optimization as "a type of prescriptive analytics that finds a "best" solution from a set of "feasible" solutions, using a mathematical algorithm that maximizes or minimizes a specified objective function subject to constraints."
According to Gartner, companies are adopting Optimization due to a number of factors:
- "Overall awareness of the value of analytics and data-driven decision making means that more organizations are moving up the analytics maturity ladder and building analytics capabilities beyond descriptive analytics."
- "Companies' goals for analytics have moved from monitoring and reporting to improved decision making."
- "The availability of data sources and the increased compute power means we can solve increasingly complex types of problems more quickly, such as those in the optimization space."
The supply chain management technology market is evolving as organizations leverage advanced capabilities, and new delivery models and styles of implementation. This research provides supply chain leaders with an assessment of the maturity and commoditization of major solution asset classes. AIMMS is mentioned in the reports as a vendor for Advanced Analytics, a type of analytics that spans Predictive and Prescriptive Analytics. Gartner defines Predictive Analytics as "techniques to analyze data, identify patterns and anticipate future scenarios." Prescriptive Analytics, as explained above, results in a recommended action. Browse case studies to uncover how our customers use AIMMS to predict future scenarios and make better decisions.
Gartner's Hype Cycle for Manufacturing Strategy helps manufacturing leaders understand the maturity and viability of various core competencies and emerging technologies. AIMMS is mentioned as vendor for Manufacturing Network Design. Gartner defines Manufacturing Network Design as the "optimization of the location, function and ownership of the manufacturing network in support of overarching company strategy and customer requirements." Companies are increasingly developing this competency to gain supply chain resilience, lower costs and respond effectively to demand fluctuations.
Why Cool: AIMMS is a specialist in mathematical optimization techniques, and while it offers a product called AIMMS, it does not consider itself a tool vendor, but rather, a partner that provides a framework to enable companies to build optimization apps in their own ways. AIMMS brings the benefits of mathematical optimization techniques to the supply chain, thus enabling customers to apply mathematical optimization successfully in their supply chain organizations in various industries. It provides a suite to model and develop supply chain optimization apps, as well as an enterprise app store - on a cloud technology platform - to roll out those optimization apps.
AIMMS enables breaking down barriers to rapid response and innovation by providing a platform to develop, test, deploy and iterate new optimization apps with unprecedented speed, covering all areas of the supply chain, such as network optimization and end-to-end deployment optimization. It can be used to break new ground in optimizing new types of operations, or optimizing existing operations in an innovative manner, enabling the concept of "continuous optimization."
Supply chain modeling is an effective way for organizations to better understand current supply chain dynamics and pursue further efficiencies and growth. This research presents best practices for supply chain strategists looking to build and mature in this analytics competency. As mentioned in the report, companies can use AIMMS to develop an "in-house modeling tool."
This Gartner report offers best practices to democratize analytics using existing resources. Acquiring and retaining top data scientists has proved challenging for many supply chain organizations. But supply chain leaders responsible for analytics can successfully support their advanced analytics initiatives by casting a wider net for requisite technical resources.
“Simulation and optimization are two common techniques in predictive and prescriptive analytics. Using the two techniques together offers supply chain leaders the best of both worlds: a recommended course of action based on a more accurate understanding of uncertainty in the supply chain.”
“Leading companies are looking to break down barriers to speed and innovation in support of a competitive business advantage as they mature their supply chain capability. This can be achieved by utilizing configurable modeling platforms, and allowing end users to continuously develop, test, deploy and iterate new planning and optimization approaches with unprecedented speed across multiple areas and horizons of their supply chain.”