How to Leverage Intelligence and Gain Leadership in Global Markets

April 28, 2013
Today’s unprecedented shifts in markets, demands, technologies and opportunities require companies to respond intelligently to more frequent, drastic and faster changes.

Today’s unprecedented shifts in markets, demands, technologies and opportunities require companies to respond intelligently to more frequent, drastic and faster changes.

With new products, customers, markets and situations, manufacturers’ strategies that worked in the past will soon be obsolete. Further, customers have now grown to expect greater responsiveness. This means conventional process structures and business strategies have become increasingly risky. Additionally, essential Continuous Process Improvement initiatives have become significantly more challenging in digging out root cause issues due to the dynamic, constantly changing and sometimes unstable underlying technology platform.

According to the November, 2012 McKinsey study “Manufacturing the Future,” this strategy transformation will require companies to “match granular insights with granular operations strategy.” In other words, people need real-time situational insights they can act on right away to drive improvement.

Employees face many volatile situations that require quick decisions based on deep, complete data. For example, does it make sense to switch to making a different product when a material is not available, or is it better to use a substitute material? That depends on the current demand for all the products in question within the plant, on current inventory levels, on customer and quality and quality specifications, on scheduled supplier deliveries and other factors.

Employees making complex situational decisions are critical to:

·         Allow factories to be more responsive to variety, volatility and change without costly validation and consequence

·         Foster supply chain responsiveness and resiliency

·         Design products for manufacturing (DFM) and the supply chain (DFSC)

·         Provide feedback for process improvement and by being engaged in ongoing business model transformation to deliver finished goods cheaper, faster and of higher quality.

Since most large companies are outsourcing more than ever, these complex decisions involve a whole information network across the enterprise, into the supply chain and out into the marketplace. To gain effective insights, intelligence must be derived from data gathered across that network of disciplines, locations and partners.

Insights to Support Better Decision Making

The business question around big data is: “How do you generate, more than just gather, as much data and information as you can, and leverage this information by turning it into structured manufacturing intelligence for different roles in the company?”  This insight needs to be based on the full business context so each individual can respond quickly with the right action. By some estimates only 7 percent of big data captured is meaningful to businesses today.

Gathering information for information sake is costly not only in storage and IT costs but also in the cost for end users to navigate and make sense of the information. For example – giving a plant manager an excel report consisting of thirty worksheets and thousands of lines of information is not helpful. It needs to be gathered, cleaned, summarized and presented in a digestible level for the end user.

Manufacturing has significant big data issues and tight timelines, since in addition to usual market and business data there is a production process that changes moment-to-moment. Typically, production information systems serve just one site and are somewhat standalone. However, connecting more plants into the intelligence flow can have exponential benefits for a global manufacturer. That multi-plant intelligence MUST flow into the enterprise information view, and vice versa. Each provides critical context for the intelligence of the other to become insights.

Being able to monitor and analyze manufacturing activities across plants not only gives the people responsible for change the confidence to make these changes but also the validation that the changes are working or not.

With more products, variants and end user markets, manufacturers have more data than ever to sift through. Additionally, each department or discipline has its own data sets. These massive volumes of structured and unstructured data are challenging to use for decision making when processes and systems are designed for much simpler and more stable environments. With global operations and distribution, new data is coming in 24/7. Most companies are simply not capable or equipped to make good, data-supported decisions at this pace. In order for this to happen, companies must transform data to information, analyze it to build intelligence, and convert that to insights that enable profitable action. 

Manufacturing Intelligence: The Missing Link

Managing and converting big data into intelligence is typically the domain of Business Intelligence (BI) systems. However, in the case of global manufacturing enterprises, traditional BI is often not sufficient. With the critical position of manufacturing operations and the real-time nature of decisions that plant personnel as well as manufacturing executives must perform, a category of application called manufacturing intelligence (MI) has been growing rapidly in use and importance.

BI and MI have quite a bit in common. Both pull data from various sources to transform it into information suitable for analysis to then gain intelligence to support business decisions. However, BI systems are not intended to handle real-time production data nor support managers and factory-based employees in making minute-to-minute decisions. Traditional BI solutions also do not provide the low granular detail that manufactures need in today’s markets.  Companies using MI are twice as likely as all others to deliver real-time metrics to operators, line workers, supervisors and executives managing operations for their scope of control, this based on the findings in the MESA Pursuit of Performance Excellencestudy.

Clearly, companies are meticulously gathering and analyzing data, but not necessarily leveraging it to be available anytime, anywhere. Part of this shortfall is an inability to have ready access to the intelligence on mobile devices. Another factor is the challenge to obtain insights from MI that delivers multi-site or global intelligence to all situations that could benefit from this information, such as a comparison of performance across plants or continuous process improvement at a division or enterprise scope.

A Joining of Forces: MI+BI

So how can the full range of employees and partners gain actionable insights on a regular basis? The short answer is: by ensuring that business and manufacturing information support and provide context for each other. To gain the full advantage for a business, companies are integrating MI together with BI. This correlates real-time production data to shifting business realities and informs business decisions about operations and actions, thus yielding far greater insights for more informed decisions.

The challenge is often how to deliver it and keep it simple for each person’s scope of control but make it consistently available so as to support intelligent business process improvement. What many companies now lack is sufficient automation of all their processes and infrastructure to support people where and when they need to collaborate, make decisions and take action. With today’s cloud technologies, new systems can often be implemented in a matter of weeks or days – and later updated without delay or major disruption.

Sustaining Your Leadership Position

Sustaining a leadership position is an age-old problem. In this new era, the key is to ensure situational data quickly turns into information, then intelligence and then actionable insights. MI can foster timely processes to collect, analyze and display data to operations staff, supervisors, and plant managers.

Companies using MI are not only more likely to use best practices for line-level metrics, but also far more likely to improve operational and financial performance.

Sustained agility requires that people make sound decisions quickly and accurately, at every level across an organization, and often in completely new situations. Examples where BI and MI can be combined for more robust decision-making capabilities include:

·         Plant personnel deciding how to best cope with a materials shortage;

·         Enterprise executives making operational decisions about which product lines to emphasize based on total profitability;

·         Product design and innovation decisions based on issues in production and in suppliers’ facilities such as suitability to run on current equipment;

·         Traceability of materials and containment of problems that could cause a customer problem or recall across the global enterprise and supply chain;

·         Sales or customer service promises order due dates and/or quantities based on actual capacity and progress of in-process work and orders;

·         Crafting a new production plan when disaster such as a hurricane or tsunami strikes a supplier’s facility;

·         Reacting to unexpected demand patterns such as new products gaining traction in different markets or geographic regions than initially expected;

·         Marketing to spot seasonal trends;

·         Supplier performance and scorecarding.

All of these cases can also benefit from running in the cloud to ensure worldwide access and consistency, mobility to reach people wherever they are, and social to allow collaboration and knowledge sharing. All of this can further improve productivity for big data analysis and timely decision making that is the core of intelligence and insights.

About the Author

James Montgomery is a Product Manager at Apriso, and is focused on the company’s manufacturing and business intelligence offerings.

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