Corporations worldwide spend billions of dollars each year purchasing electrical infrastructure equipment, software, and   digital solutions. Buyers (enterprise accounts) and sellers (electrical infrastructure vendors) negotiate prices and discounts for large ticket items such as transformers and switchgear on an ongoing basis.   In these traditional settings, equipment vendors rely on their salespeople to make pricing judgments based on experience, gut feel, and information about the customer and the competition. In most cases, the buyer does not accept the first quoted price. Negotiations ensue, and eventually both sides hopefully agree on a price. However, both sides are left not knowing whether they negotiated the best price. It is an inefficient process where up to 20 percent of a salesperson’s time is spent securing internal price discount approvals and customers can be frustrated by having to wait for a final price quotation.   For the power infrastructure vendor, the goal of the sales team is to optimize the win rate for each engagement opportunity. The salesperson wants to avoid quoting a price that is too high, in fear of losing the opportunity. On the other hand, the salesperson also wants to avoid a situation where the price discount is too deep, and the profitability of the deal is undermined.   AI Digital Solutions Drive Sales Teams to Generate Additional 5 Percent Revenue Growth New breakthroughs in Artificial Intelligence (AI) digital solutions now make it possible for electrical infrastructure equipment vendors to use data to structure more optimized product pricing in a much more efficient manner. In fact, over the past two years our company   Predictive Layer, a Schneider Electric   Technology Partner   specializing in AI tool development, has seen corporate sales teams who deploy such AI tools improve their revenues by up to 5 percent.       The new AI engines consider historical transactions for specific sets of products, including past win rates. The engine also factors in variables such as time of year (seasonality/cyclicality), levels of inventory, cost of goods sold, target margins, market growth trends, levels of demand, urgency of delivery dates, and purchase volumes.  The AI engine then accounts for these factors in its calculations when generating a recommended price range and explains which variables were given the most weight in the recommendation. In addition, the engine considers whether the vendor is choosing, in each particular transaction, to optimize for more volume and market share or for more profitability.   AI Digital Solutions Analyze Win and Loss Data Leading to Better Pricing Over time, the AI engine learns more about how to structure the best deal by analyzing opportunity win and loss data. The engine sharpens its assessment of the limits of what is acceptable in the eyes of the customer and pulls back from limits when it sees it has priced too aggressively. As the system continually improves over time, the percentage of profitable deals won continues to grow. The AI engine equips the sales team with a recommended price range. Working with a price range rather than a specific price provides the salesperson with control of the ultimate price to be proposed. Since the system also provides an explanation of the different dimensions that were considered in coming up with the price range and their relative importance, the salesperson can use these data-driven arguments to justify the quoted price to the customer, further strengthening the win rate.   AI Benefits Include Improved Sales Productivity In addition to generating up to a 5 percent improvement in top line revenues, sales teams that use such AI pricing tools are seeing a 15 to 20 percent improvement in sales productivity thanks to more streamlined internal pricing approval processes, resulting in quicker quotations. Sales management can also use data generated from such tools to engage in more fact-based discussions with sales teams when comparing performance across territories and regions.  And unlike the deployment of large enterprise IT systems, vendors can begin with a simple “Proof of Value” project that can be delivering benefits in just a few weeks.   For More Information To learn more about how innovative solutions such as AI pricing engines can help drive efficiencies and productivity across your business, read our blogs:   Changes in Energy Resources Marketplace Behaviors Usher in New Era of AI-driven Price Forecasting Why Utilities Are Using AI Digital Solutions to Reinvent the Science of Price Forecasting? Use Smart Digital Solutions forecasting Tools to manage Power Grids How AI engines boost Grid Energy consumption forecasting accuracy? How to improve Industrial maintenance and safety with AI predictive engines?   We use the   Schneider Electric Exchange  platform to foster and nurture new ideas related to energy solutions though its community of cross-industry experts. You can also have free access to Exchange to collaborate and co-innovate on trending topics like AI and innovative digital solutions by joining any of hundreds of Exchange communities, like   Power Distribution Excellence   and   Industrial Automation.   -----------------------   Originally posted on SE Blog & Authored by Olivier Cognet   About the author:  Oliver Cognet, CEO, Predictive Layer, is a business leader, corporate developer, and entrepreneur. He has 25+ years of experience in High Tech, Software Development, and Telecom markets focusing on enterprise and consumer markets. Mr. Cognet knows that technology and innovation can help businesses better understand the behavior and needs of consumers, thus helping businesses to grow. Predictive Layer brings the full power of machine learning artificial intelligence (AI) to businesses so they can simply and intuitively model and establish forecasts and predictive analytics of their key target indicators. ... View more

Industrial organizations are subjected to a higher level of business risk than ever before. Distribution disruptions, global competition, physical infrastructure vulnerabilities, and safety issues all present growing challenges to production predictability and resilience. According to analyst firm   Aberdeen Research, 82% of companies experience unplanned downtime and, depending on the industry, these interruptions can cost a company as much as $260,000 an hour. For these reasons, many manufacturers are searching for new ways to minimize the impact of disruptions through the use of   digital solutions   such as   AI engines.   Predictive AI engines consist of software with imbedded algorithms that use data from multiple sources to steadily build prediction accuracy. Such predictive engines apply themselves to the operation of industrial equipment, to service fulfillment, to physical infrastructure analysis within buildings (i.e., monitoring of power, ventilation and heating and cooling systems), and to human safety.   As organizations grow, their level of operational risk increases (more can go wrong with a larger installed base of equipment, for instance). By analyzing the historical experience of multiple sites surrounding equipment failure and safety incidences, an AI engine can automatically generate a profile of the level of risk that an organization can expect moving forward, and can also generate the probability that a future incident will occur.   Equipped with such information, an organization can pursue multiple paths. First, it can alert workers to the risk and, by making people aware of the risk, can instruct them how to avoid the danger or disruption. Second, it can plan for resource and staff to respond to the failure threat (such as procuring the necessary inventory of spare parts prior to the equipment failure). This type of foresight helps organizations to get back to normal service as quickly as possible, should an incident occur.       How an AI Engine Digital Solution Drives Business Continuity Our company,   Predictive Layer, is a Schneider Electric Technology Partner that supports process and discrete manufacturers who are seeking to forecast equipment performance and safety with higher accuracy and speed. We explain to our clients that generating an accurate profile of an organization’s facilities plays an important role when populating an   AI engine   with appropriate data. Elements such as geographic uniqueness, the functional mix of employees (i.e., ratio of blue collar to white collar), the number of employees, the physical setup of the site (e.g. number of floors, types of equipment on those floors), business hours (e.g. 24/7 operation), and the types of activities that occur at the sites are all critical. The engine will combine those elements to calculate risks and will incorporate data from other, similar sites and learn from that data. In addition to site profile information, the history of previous equipment failure and safety incidents is also factored in. By processing this historical data, the AI engine can then start to build a model which generates alerts when the potential risk of critical equipment failure gets too high. This allows the facility to properly pre-provision and pre-staff to quickly respond to the risk in question.   Company size often determines whether external anonymous data needs to be added to the data pool for AI analysis. Very large organizations, for instance, with multiple global sites, can generate enough data within their own walls to sufficiently drive the accuracy of the engine. Smaller organizations, by agreeing to both share and use anonymous data from other similar organizations, can pool enough data to drive value from the AI engine’s predictive capabilities. AI engines provide organizations with the probability of events such as equipment failures or potential safety breaches. They also provide a view of the nature of the issue and the degree of severity of that issue.  The AI engine even has the ability to perform a self-assessment of its predictive capabilities and, in cases where it finds itself lacking a high enough degree of certainty, will communicate whether it thinks a human should be brought in to provide a judgement. This “confidence indicator” helps to further reduce risks to business continuity by allowing humans to join forces with the AI engine to help generate the quickest, most accurate decisions.   When connecting to new industrial clients with AI predictive engine needs, our company uses the   Schneider Electric Exchange   platform to foster and nurture new ideas related to AI predictive analysis tools though its community of cross-industry experts. Both end users and technology providers have free access to Schneider Electric Exchange to collaborate and co-innovate on trending topics like AI by joining any of hundreds of Exchange communities.   For More Information About AI Digital Solutions We use the Schneider Electric Exchange platform to foster and nurture new ideas related to energy solutions though its community of cross-industry experts. You can also have free access to Exchange to collaborate and co-innovate on trending topics like AI and innovative solutions by joining any of hundreds of Exchange communities like Power Distribution Excellence and Industrial Automation.   Want to see how  Predictive Layer , a  Technology Partner  of Schneider Electric, is helping to optimize energy consumption with the use of  AI-based software , then visit the  Schneider Electric Exchange Shop. To learn more about accurate forecasting data models for the Energy Industry, try these  publications in Exchange .      --------------------   Originally posted on SE Blog ... View more

In recent years, patterns of energy production and consumption have changed radically. New variables such as renewable power generation, community microgrids, and rapidly changing energy prices make grids infinitely more complex to manage. To address this challenge, our company Predictive Layer, developed an artificial intelligence (AI) digital solution that is designed to better forecast energy supply and demand variations across an electrical  grid .      How to Improve Forecasting While Managing a Variable Grid My clients are concerned with how to improve their forecasting while managing a much more variable grid. Many have similar questions, so here are my responses to some of the most common questions.   Question:   How can I maintain my grid stability given the intermittent nature of renewables like wind and solar?   Answer:   Renewables make managing the grid more complex because they add an element of unpredictability. It is more difficult to understand where and when energy will be produced and consumed. In order to successfully integrate wind and solar into your grid, the local utility has to tune, program, and schedule your grid in such a way as to be able to adapt, in real time, to these sometime unpredictable changes. Your prediction model cannot afford to just wait for new supply and demand patterns to emerge. It must predict and anticipate the reconfiguration of the grid topology, in order to optimize performance.   To develop this ability, your grid will require two fundamental pillars: First, make sure that the grid infrastructure has the capacity to deliver the energy required and to redirect supplemental flows of energy back to those points in the grid that need it (e.g., when solar or wind power plants are overproducing). It is a matter of optimizing the grid topology to the right capacity at the right moment. Second, controlling the energy flow across the grid is important. Improper tuning of energy flow can degrade the efficiency of the grid by a factor five, 10, or even 20 percent.     Question:   Can I scale my predictions?  How can I generate enough data touchpoints to make my grid-wide forecasting more accurate than it already is?   Answer:   Not only local grids, but also enterprises and microgrids require more data to deliver an automated, accurate forecasting of the short-term production and consumption. Scalable automated forecasting capabilities are critical. It is not just about having an intelligent artificial intelligence system that will automatically learn the local context and then issue a forecast. It is also about the ability to absorb and evaluate thousands of touchpoints across the grid.   This is almost impossible to implement with only human mathematicians. You cannot send a mathematician out each time you need to readjust your predictive model. You want to implement an automated model, with artificial intelligence and machine learning, to scale with fine precision. If a community microgrid, for example, is doubling its volume of solar panels, the system should learn automatically that seven new panels have been added and instantly factor that into its calculations and predictions. Thanks to new technologies like cloud computing, such scalability is now available at an affordable price. You can rent 100 computers for a couple of hours and accomplish in three hours what an expert human mathematician previously accomplished in three weeks. If we imagine scaling the predictions by a factor of a thousand to meet required accuracy parameters, we can see how a human, using a manual model, would be quickly overwhelmed.     Question:    How do clients who have already installed your AI forecasting solution adjust to placing their trust in a machine software algorithm?   Answer:  Initially, they are cautious and look over the machine’s recommendations to see if the human needs to override the decision. Then, over time, as the results begin to roll in with more accuracy and precision, the trust builds. Since there is a shortage of data scientists, these experts can let the AI engine take over. Now they are free to tackle bigger problems, like those that require more human thinking than machine thinking.   Find More Information About AI Tools to Manage Grid Forecasting Predictive Layer  is a Schneider Electric   Technology Partner   that supports electrical utility and microgrid managers who are seeking to forecast energy supply and demand across their grids with higher accuracy and speed.   To learn more about how AI tools can better manage grid consumption forecasting, read our blogs: Changes in Energy Resources Marketplace Behavior Usher in New Era of AI-Driven Price Forecasting Why Utilities are Using AI Digital Solutions to Reinvent the Since of Price Forecasting Use Smart Digital Solutions Forecasting Tools to Manage Power Grids   Learn More about Energy Management Solutions Want to see how Predictive Layer, a Technology Partner   of Schneider Electric, is helping to optimize energy consumption with the use of   AI-based software, then visit the Schneider Electric Exchange   SHOP. To learn more about accurate forecasting data models for the Energy Industry, try these   publications in Exchange.  ... View more

The stability of power grids supplying energy to our homes and businesses is emerging as a critical success factor for maintaining both physical and economic security. Regardless of the electric grid size—a microgrid with several hundred users or a regional grid with millions of users—grid complexity is on the rise. Trends such as the sharp increase in Internet of Things (IoT) devices and digital solutions   that tax large and small grid capacities, a growing installed base of renewable energy generation, and the proliferation of distributed energy resources (DER) behind the meter at the grid’s edge, are all contributing to this complexity.   Unfortunately, the electric design of existing grids has not been optimized to accommodate these new behaviors. Local and national grids are experiencing significant changes in energy flows. In the past, the energy consumption pattern was easy to interpret. At around 6:00 p.m. rates of energy consumption were high and not much energy was consumed during the night. Now, businesses, hospitals, and homeowners, for example, may be producing energy at midday with solar panels. An increasing quantity of electric cars are also consuming energy in the middle of the night to recharge their batteries. Energy prices that sometimes change minute-by-minute are also altering where and when enterprises choose to consume the most energy. This makes the flow of energy within grids more volatile. Instead of traditional top down energy distribution, grids must deal with bidirectional energy flows which change throughout the day and night.   To accommodate the changing nature of the grid, stakeholders are investing in both technology and manpower to better forecast short-term evolution of electricity in their operation. Without accurate consumption forecasting, nodes within the grid can become saturated with energy demand that is too high which, in turn, can lead to grid failures and blackouts.     Why AI Digital Solutions Can Improve Forecasting Accuracy   This is where we at Predictive Layer, a Schneider Electric Technology Partner, offer support.  Electrical utilities and microgrid managers need to forecast, with high accuracy and speed, short-term grid energy consumption across the geography of their grids. We have developed an artificial intelligence (AI) engine   to address the new layers of grid network complexity.   Today most large grids deal with the challenge of energy consumption forecasting by assigning teams of mathematicians to automate and scale the forecast. Should the parameters of energy consumption change—an extreme weather event, or a societal event where many people are forced to work from home—those complex mathematical models have to be rapidly reworked in order to align with the new reality.  When thousands of grid network nodes are involved, the task becomes too complex to execute in a rapid fashion—even for the best mathematicians. The task is also challenging for microgrids and smaller local grids who typically have few human resources to dedicate to forecasting.   An AI-driven forecasting engine learns from both past energy consumption patterns and from real time live situations. For example, the changes that are taking place within a grid (like the addition of new transformers in a location) and the intricate nuances in energy consumption demand automatically are fed into the model on an ongoing basis. This includes data from transformers, information about the behavior of subscribers connected to the network, and information surrounding weather-related and calendar date-based patterns of consumption.     Benefits Include Less Downtime and Lower Operational Costs   The AI engine learns how to better forecast what is happening every day, everywhere within the confines of the grid, and continues to get smarter as it processes more data. At each point of the day or night it can generate, on behalf of grid operators, local schedules of how much energy should be delivered to designated locations and expected volumes of energy coming in or out. The AI forecasting tool also automatically reconfigures itself as changes occur across the grid. If a new enterprise suddenly turns the lights on in a new facility within a geography, the engine can recalculate that the energy consumption peak will now occur at 4:00 p.m. rather than at 6:00 p.m. in that area.   Instead of a team of people generating 10 forecasts of what is happening in each of their regions, the software can generate thousands of such forecasts in minutes. These forecasts can account for every locality within the grid. (In New York state, for example, there may be 10,000 transformers in the entire network that distribute energy to users.)   The benefits of having access to such an automated system include less network downtime, better operational management and cost control, and higher efficiency of electricity distribution. The tool also makes it easier to manage a crisis where rapid changes in consumption require immediate actions. The goal is to sustain the level of customer expected quality and uptime regardless of the changes that are taking place across the grid.   -----------------------   Originally posted on   SE Blog & Authored by Olivier Cognet   About the author:  Oliver Cognet, CEO, Predictive Layer, is a business leader, corporate developer, and entrepreneur. He has 25+ years of experience in High Tech, Software Development, and Telecom markets focusing on enterprise and consumer markets. Mr. Cognet knows that technology and innovation can help businesses better understand the behavior and needs of consumers, thus helping businesses to grow. Predictive Layer brings the full power of machine learning artificial intelligence (AI) to businesses so they can simply and intuitively model and establish forecasts and predictive analytics of their key target indicators.   ... View more

As the energy marketplace has grown more complex, energy producers, grid operators, and consumers require more intelligence and faster data analysis to interpret pricing patterns. Producers need to know how and when to maximize their energy output during times of the day and week when pricing will work the most to their advantage. Consumers need to accurately perform short-term forecasts of both their consumption and pricing to optimize their purchasing power.   Part 1 of this two-part blog series “Changes in Energy Resources Marketplace Behaviors Usher in New Era of AI-driven Price Forecasting” reviews the reasons why the marketplace has evolved and explains how new energy trends are impacting pricing complexity across the industry.   This blog post (Part 2) highlights some of the advantages utilities and other energy stakeholders can accrue when they deploy an AI-driven forecasting digital solutions   model.   When compared to classic mathematical forecasting models, automated AI models can forecast consumption and production with 20 percent to 30 percent more precision – which leads to direct bottom line savings. In addition, field studies also determine that AI models prevent more large errors than do classic models.     Unique Abilities of AI Digital Solutions Tools Act as Accurate Differentiators   We at Predictive Layer, a Schneider Electric Energy Management Technology Partner, have developed an AI system to help large enterprises, traditional utilities, and producers of renewable energy resources to better forecast energy consumption and pricing so that customers can save money through smarter energy choices.   Listed below are some of the reasons why energy stakeholders are investing in such AI-driven forecasting digital solutions:   Global application Many countries today have created their own specific energy demand, supply, and pricing rules. Besides providing energy supply and demand forecasting, AI tools also can learn the unique characteristics of the pricing models of each of these local markets. People who understand these local pricing models are very difficult to replace when they retire. The AI model provides an insurance policy that pricing knowledge is retained and put to good use.   Access to a richer data pool AI models directly access multiple sources of information, including Transmission System Operator (TSO) energy balancing reference information, multiple weather forecasts, pricing alerts, market prices published in real time, published client activity plans (e.g., vacations, holidays, major events) and any published signals on open platform forums like Schneider Electric Exchange. This collection of data enables unique precision and provides stakeholders with enough response time to adjust to marketplace changes.   Increased savings AI models consider all the variables at play and propose the best purchase or trade plan for the upcoming weeks, months, and quarters. This enables savings of 5% to 10% when compared to exercising a standard purchase plan approach. These gains are possible because the machine learning aspects of the AI tool recompute models and automatically adapt to new patterns in the marketplace much faster and much more accurately than a human remodeling, especially, if the individual has to manage multiple forecasts simultaneously.   Self-monitoring abilities AI models are constantly self-monitoring the health and accuracy of their own forecasting model. They also suggest the integration of additional inputs that can continue to sharpen the accuracy of the model thereby further reducing risk for error as time goes on. Organizations that have deployed our AI solution in the field over the last 18 months have developed enough confidence in the tool that they now rely on it as their main resource for pricing-related decisions. Only automated alerts surrounding anomalies are sent to a human manager. Such automation allows the forecasting team to be freed up to both further scale their forecasting systems and to build in more resilience to enable the servicing of new opportunities.   Resources on Artificial Intelligence Tools To learn more about how artificial intelligence tools can better manage grid consumption forecasting, read Part 1 of this blog series ”Changes in Energy Resources Marketplace Behaviors Usher in New Era of AI-driven Price Forecasting”. You can also find our offers on the Schneider Electric Exchange Shop.   -----------------------   Originally posted on   SE Blog & Authored by   Olivier Cognet   About the author:  Oliver Cognet, CEO, Predictive Layer, is a business leader, corporate developer, and entrepreneur. He has 25+ years of experience in High Tech, Software Development, and Telecom markets focusing on enterprise and consumer markets. Mr. Cognet knows that technology and innovation can help businesses better understand the behavior and needs of consumers, thus helping businesses to grow. Predictive Layer brings the full power of machine learning artificial intelligence (AI) to businesses so they can simply and intuitively model and establish forecasts and predictive analytics of their key target indicators. ... View more

The energy provider marketplace is rapidly changing. Ongoing deregulation has pushed many utilities to develop new commercial offerings that provide both enterprises and households with increased contractual flexibility. In most European and Asian countries and in the United States, clients can now potentially receive proposals from 10 or more utility providers. The ongoing energy transition also opens the market to new producers who generate their energy from very different sources. Renewable energies such as solar, wind, and hydro complement existing fossil fuel energy resources and now produce up to 25 percent of the total production during certain periods of the year. Renewables address a marketplace need for low carbon power generation and sustainable energy development.   These types of marketplace variables significantly add to the complexity of forecasting energy consumption behaviors across grids. As a result, the ability to forecast energy prices using traditional methods becomes less accurate, and grid operators have a more difficult time remaining profitable. To address this issue, grid operators can now turn to AI-driven   technologies to forecast energy prices.       Energy Consumption Pattern Changes Impact Pricing Energy consumption patterns have also changed. Enterprises and households now have more control over how and when they consume energy. This new-found flexibility allows them to lower their cost of consumption.  In some cases, they may even act as temporary energy producers when using their own solar panels. They are also shifting their energy consumption to periods of the day when energy prices and overall energy demand is lower. Retailers and offices, for example, control their lighting, and channel the intensity of their heating or cooling to times of the day or night when prices are lower. Residential homes are also starting to use devices like smart thermostats to better control their heating and cooling and can schedule the recharging of their electric vehicle (EV) batteries at night when rates are low. For the energy producers who support these customers, the traditional top/down model of managing a grid is no longer a sustainable energy business model.   Market and Lifestyle Changes Alter Electricity Pricing Mechanisms In this context of changing regulations, power sources, and consumer preferences, the way energy is priced has evolved. In fact, many regions of the world have developed their own specific demand/supply/pricing rules. Energy is now traded in public market forums like the   European Energy Exchange (EEX). In addition to being linked to primary energy prices (i.e., prices for Brent crude and gas), electricity contracts are also priced for periods representing future days, weeks, months and even into the next year–and these prices fluctuate on a daily basis. Such prices may evolve by a factor of 1 to 2 during their trading periods. In fact, intraday prices regularly evolve by a factor of 1 to 5 on an average basis. Very high price peaks are observed during certain days–for example, when a large industrial asset unexpectedly requires maintenance in winter and is taken off-line. Negative prices are also becoming more and more of a market reality as renewable production sometimes results in an unexpectedly high volume (high supply) of available energy due to misinterpreted weather forecasts.   Digital Solutions Enable Forecasting and Increase Accuracy As a result of these conditions, it has become much more difficult for both utilities and major energy consumers to predict both energy production and portfolio consumption. Therefore, they find themselves in a situation where they cannot access an optimally low energy price. The solution is to enable a level of forecasting that increases the accuracy of supply, demand and prices. Only then can electrical utilities and distributors maintain and/or increase their profitability. This is where   new artificial intelligence   (AI) digital solutions play an integral role. We at   Predictive Layer, a Schneider Electric Energy Management   Technology Partner, have developed such a system to help large enterprises, traditional utilities, and producers of renewable energy to better forecast energy consumption and pricing.   AI and Machine Learning Provide Higher Accuracy and Adaptability Forecasting technologies, statistics, and models over the last 50 years were developed by small teams of expert energy forecasters. Their approach worked well in managing the previous generation of grid operations. Now, accelerating levels of complexity across grids create limits on the effectiveness of these human forecasting teams. However, new generations of distributed IT and infrastructure tools allow very affordable and resilient high-performance computing that enables the creation of more accurate forecasting models. These models now supplement forecasting on a daily basis. In fact, technology evolution is such that it is no longer necessary to manually build new forecasting models. New AI and machine learning tools automatically evolve the forecasting models by incorporating new trending and network supply and demand behaviors on a daily basis. With the proper AI tools, forecasting teams can produce and manage forecasts with 10x more accuracy than before.   -----------------------   Originally posted on   SE Blog & Authored by   Olivier Cognet   About the author:  Oliver Cognet, CEO, Predictive Layer, is a business leader, corporate developer, and entrepreneur. He has 25+ years of experience in High Tech, Software Development, and Telecom markets focusing on enterprise and consumer markets. Mr. Cognet knows that technology and innovation can help businesses better understand the behavior and needs of consumers, thus helping businesses to grow. Predictive Layer brings the full power of machine learning artificial intelligence (AI) to businesses so they can simply and intuitively model and establish forecasts and predictive analytics of their key target indicators.   ... View more