Transforming Integrated Planning & Supply Chain Processes with Augmented Intelligence Capabilities

In conjunction with the announcement, o9 released an eBook titled, “Who Gets the Cheese?”

Aptly named after one of the greatest business books of all time (Who Moved My Cheese?), this resource details one of o9’s systems for optimally allocating resources across initiatives and brands at consumer goods companies.

Founded by executives, practitioners and technologists that have led supply chain innovations for nearly three decades, the o9 team has been quietly developing a game-changing Augmented Intelligence (AI) platform for transforming Integrated Planning and Supply Chain processes.

The team has deployed the AI platform with select clients, including:

  1. Bridgestone Tires
  2. Asian Paints
  3. Restoration Hardware
  4. Party City
  5. Del Monte
  6. Aditya Birla Group
  7. Caterpillar
  8. Ainsworth Pet Foods

Speaking on behalf of o9 Solutions, Co-founder and CEO Chakri Gottemukkala said, “While executives we work with hear the buzz around technologies for data sensing, analytics, high performance computing, artificial intelligence and automation, they are also living the reality of slow and siloed planning and decision making because the enterprise operates primarily on spreadsheets, email and PowerPoint.”

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One-Page Data Warehouse Development Steps

Data warehouse is the basis of Business Intelligence (BI). It not only provides the data storage of your production data but also provides the basis of the business intelligence you need. Almost all of the books today have very elaborated and detailed steps to develop a data warehouse. However, none of them is able to address the steps in a single page. Here, based on my experience in data warehouse and BI, I summarize these steps in a page. These steps give you a clear road map and a very easy plan to follow to develop your data warehouse.

Step 1. De-Normalization. Extract an area of your production data into a “staging” table containing all data you need for future reporting and analytics. This step includes the standard ETL (extraction, transformation, and loading) process.

Step 2. Normalization. Normalize the staging table into “dimension” and “fact” tables. The data in the staging table can be disposed after this step. The resulting “dimension” and “fact” tables would form the basis of the “star” schema in your data warehouse. These data would support your basic reporting and analytics.

Step 3. Aggregation. Aggregate the fact tables into advanced fact tables with statistics and summarized data for advanced reporting and analytics. The data in the basic fact table can then be purged, if they are older than a year.

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Why Supply Chains Need Business Intelligence

Companies that want to effectively manage their supply chain must invest in business intelligence (BI) software, according to a recent Aberdeen Group survey of supply chain professionals. Survey respondents reported the main issues that drive BI initiatives include increased global operations complexity; lack of visibility into the supply chain; a need to improve top-line revenue; and increased exposure to risk in the supply chain. Fluctuating fuel costs, import/export restrictions and challenges, and thin profit margins are driving the need for businesses to clearly understand all the factors that affect their bottom line.

Business Intelligence essentially means converting the sea of data into knowledge for effective business use. Organizations have huge operational data that can be used for trend analysis and business strategies. To operate more efficiently, increase revenues, and foster collaboration among trading partners companies should implement BI software that illuminates the meaning behind the data.

There is a vast amount of data to collect and track within a supply chain, such as transportation costs, repair costs, key performance indicators on suppliers and carriers, and maintenance trends. Being able to drill down into this information to perform analysis and observe historical trends gives companies the game-changing information they need to transform their business.

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Artificial Intelligence: The next big thing in Supply Chain Management

Imagine the endless possibilities of learning from 2.5 quintillion bytes of data generated every day. Artificial intelligence (AI), which began its journey 60 years ago is well on its course to make this implausible scenario a reality. Artificial Intelligence, is slowly taking over our lives.

From personal assistants like Siri in Apple products to stock trading to medical diagnosis, AI is able to learn from seemingly unstructured data, take decisions and perform actions in a way previously unimagined.

Businesses too are undergoing digitization rapidly. They are using AI – capable of performing tasks normally requiring human intelligence – to create a significant impact in the way businesses operate. In an increasingly dynamic environment comprising demanding customers and the need for speed, it was only a matter of time before the businesses embraced AI to obtain much needed agility. According to Accenture’s Technology Vision 2016 survey spanning 11 countries and 12 industries, 70 percent of corporate executives said they are significantly increasing investments in AI.

Artificial Intelligence in Supply Chain

Organizations are increasingly digitizing their supply chains to differentiate and drive revenue growth. According to Accenture’s digital operations survey 85 percent of organizations have adopted/ will adopt digital technologies in their supply chain within 1 year.

The key implication of this change is that the supply chains are generating massive amounts of data. AI is helping organizations analyze this data, gain a better understanding of the variables in the supply chain and helping them anticipate future scenarios. Thus, the use of AI in supply chains is helping businesses innovate rapidly by reducing the time to market and evolve by establishing an agile supply chain capable of foreseeing and dealing with uncertainties.

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How to Use Big Data to Enhance Employee Performance

Big Data has been one of the most significant and influential aspects of the Information Age as it relates to the enterprise world. Essentially, Big Data is the massive collection, indexing, mining, and implementation of information that emanates from just about any activity that can be monitored and managed electronically. Some of the uses of Big Data include: marketing intelligence, sales automation, strategizing, productivity improvement, and efficient management.

Enhancement of the workforce is one of the exciting and meaningful benefits of Big Data for the business sphere. Recently, human resource managers and analysts have been researching the implementation of Big Data as it relates to employees, and the following trends have emerged:

Employee Intelligence

For many decades, companies and organizations have tried various methods to gain knowledge about what their employees are really like. The productivity that workers can contribute to their employers is based on personal needs as they are balanced against the performance of their duties. With Big Data solutions, both personal needs and performance can be diluted into metrics for efficient analysis.

Modern workplace analytics originates from tracking employee records as well as metrics on their performance, interactions and collaboration. The idea is to focus on the right metrics to create a climate of positive engagement.

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How visibility can drive supply chain performance

How visibility can drive supply chain performance

At its heart, supply chain management requires a balancing of operational efficiency, customer satisfaction and quality. Managing the true cost to serve each and every order is the aspiration to allow better negotiation and value creation across the supply chain. Customer and consumer centricity helps anticipate product and service requirements. But supply chains are becoming more extended and complex with a consequent increase in risk and the need for resilience. There are multiple data sources making it difficult to manage and measure end-to-end processes and metrics. Aligning priorities through integrated planning remains pivotal but there is an explosion of data available that needs to be incorporated and the value extracted to understand how supply-demand issues impact profit and revenue targets.

Organisations are looking to enable better and more consistent decision-making across complex processes with diverse systems and data. Many are leveraging business intelligence (BI) platforms to give them the capability to make decisions across the organisation, including environments where mobility and access to decision-critical information on the go is crucial. Putting the information in the hands of the people on the front line – those managing supply chain processes – is key to enabling decision making at the point of decision. But this requires synchronising an enormous amount of data that comes from many systems and sources in a way that it can be easily consumed by people who need to act on the insights.

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10 ways big data is revolutionising supply chain management

Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy enterprise resource planning (ERP) and supply chain management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.

Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.

The ten ways big data is revolutionising supply chain management include:

  1. The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence.
  2. Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.
  3. Big data and advanced analytics are being integrated into optimisation tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.
  4. Big data and advanced analytics are being integrated into optimisation tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.
  5. Using geoanalytics based on big data to merge and optimise delivery networks.
  6. Big data is having an impact on organizations’ reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%).
  7. Embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater.
  8. Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.
  9. Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant.
  10. Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data.

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External Insights Critical to Effective Supply Chain Performance

Traditional forecasting models that leverage historical data to predict future performance are the tools used by most supply chain executives to plan critical functions, yet these predictions are frequently inaccurate. In fact, research from KPMG International, in cooperation with the Economist Intelligence Unit, shows that most quarterly forecasts are off by 13 percent—meaning that supply chain managers are basing their decisions for ordering materials and scheduling distribution on erroneous projections. The result can mean surpluses or shortages, potentially costing companies millions either way.

There is a better way to anticipate supply chain demands—one that can vastly improve projections, and decrease the discrepancies between forecasting and reality, therefore helping supply chain executives perform their jobs more effectively. Few companies take into account macroeconomic factors, global manufacturing activity, consumer behavior, online traffic, weather data, etc. when making business projections. Yet companies that do identify leading performance indicators using such external data earn more than a 5 percent higher return on equity than those that use only internal metrics. Leveraging external factors, in addition to internal performance measures, is proven to result in more accurate, effective forecasts. Not to mention that improving forecast accuracy can represent huge bottom-line benefits. For a billion dollar manufacturing company, for example, improving forecast accuracy and overall return on equity even 1 percent can equal a $3 million increase in net income.

Forecasting accuracy, improved through external factors, benefits multiple business functions—from financial operations (shareholder value) to human resources (adequate staffing) to marketing (product innovation)—but is especially impactful on the supply chain management function.

Improves Inventory Management

Improved forecast accuracy using external drivers equates to reduced inventory management costs, ultimately improving bottom-line profit. By accounting for external factors, companies can see a 10 to 15 percent improvement in forecast accuracy, significantly decreasing the cost of excess inventory. By ordering raw materials based on correct projections, supply chain managers no longer have to worry about discounts necessary to move excess inventory or the cost of warehousing excess materials because they are ordering accurately from the start.

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Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

 Magic Quadrant for Business Intelligence and Analytics Platforms

Data discovery capabilities are dominating new purchasing  requirements, even for larger deployments, as alternatives to traditional BI tools. But “governed data discovery” — the ability to meet the dual demands of enterprise IT and business users — remains a challenge unmet by any one vendor.

The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. Because of the growing importance of advanced analytics for descriptive, prescriptive and predictive modeling, forecasting, simulation and optimization (see “Extend Your Portfolio of Analytics Capabilities”) in the BI and information management applications and infrastructure that companies are building — often with different buyers driving purchasing and different vendors offering solutions — this year Gartner has also published a Magic Quadrant exclusively on predictive and prescriptive analytics platforms (see Note 1). Vendors offering both sets of capabilities are featured in both Magic Quadrants.

For this Magic Quadrant, Gartner defines BI and analytics as a software platform that delivers 17 capabilities across three categories: information delivery, analysis and integration.

As a result of the market dynamics discussed above, the capability definitions in this year’s Magic Quadrant have been modified with the following additions and subtractions to reflect our current view of critical capabilities for BI and analytics platforms.
Capabilities dropped:

  1. Scorecard: Most companies do not implement true scorecard/strategy maps using BI platforms — they implement dashboards. Also, most BI vendors report limited sales activity for their scorecard products. Scorecards are primarily delivered by corporate performance management (CPM) vendors (see “Strategic CPM as a Driver for Organizational Performance Management”). Therefore, we have included scorecards as a type of dashboard, rather than as a separate category.
  2. Predictive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”
  3. Prescriptive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”

Capabilities added:

  1. Geospatial and location intelligence (see the Analysis section)
  2. Embedded advanced analytics (see the Analysis section)
  3. Business user data mashup and modeling (see the Integration section)
  4. Embeddable analytics (see the Integration section)
  5. Support for big data sources (see the Integration section)

Feel free to leave us your comments or send us a message.

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