Savvy industry and manufacturing professionals are increasingly using big data management tools to provide real-time feedback on decision making and planning.

While historically focused on marketing and sales dynamics, business information (BI) software is being embraced across a variety of manufacturing and service industries to provide a rapid, accurate and high-level visual snapshot of complex dynamics. This information is used to enable data-based allocations of essential resources and facilities.

When assessing whether a BI tool could help save time and money, consider these screening questions:

  • Does your manufacturing line make more than one product or version?
  • Do you expect your business to grow substantially?
  • Are you now or in the future looking at capacity limitations?
  • Do you sell to multiple customers, either locally or globally?
  • Does your selling price vary by application, customer, volume and so on?
  • Do you need a methodology to provide more than a “gut feel” that you are investing resources, line time, raw materials and development efforts in the most optimal way to maximize profits?

Answering yes to one or more of these questions may justify investigating BI solutions as a means to streamline production planning, improve customer focus, reduce variable costs and optimize other critical variables.

BI analysis tools can provide a product manager, operations specialist or strategic analyst with a real-time view of complex multi-variable historical or forecast data. This information can be presented in many different graphical or statistical formats to track progress, plan business responses and provide concise reports on key variables and path-forward directions.

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Here are some real-world scenarios that show how BI was used to improve manufacturing operations.

Capacity Allocations

Imagine that your company has finite line outputs of—say—5,000 widgets, pounds, units, etc./per hour. The business has grown to produce four different part numbers on a fixed line, each with a variable impact on line speed, maintenance demands and inspection needs. Although 70% of historical volume was Part #1, sales forecasts show potential growth of Part #3 that will double its demand and restrict other parts capacity on the line.

Do you pursue Part#3 volumes and scale back other demands? Implement line speed improvement programs? Run volume campaigns at the risk of inventory growth? Implement other solutions?

For this situation, BI analysis showed that Part #3 growth would increase total variable margins by 26% annually due to strong customer demand and consequent pricing power. Part #1 was evaluated for lifecycle status and pricing actions to offset potential volume restrictions, and the decision was made to produce less of this item.

Pricing Strategies

Now imagine that your company is an established supplier to the electronics industry, and has provided a growing portfolio of products to both core and new customers. After 15 years of regional customer management and customized annual contracts, the larger picture of price/volume versus customer segmentation has become unbalanced and must be revisited for strategic direction.

What are the pricing variables among products and customers? Is pricing consistent with a volume model by global region? Are you losing opportunities, and if so where?

Using 24-month BI pricing data, the product manager charted actual global pricing by customer, volume and top five part numbers. Unexpected variations in pricing inverse to volumes were seen, as well as significant pricing differentials by country and region. Cross-functional data summaries were reviewed with sales and marketing teams to evaluate, propose and execute options to improve bottom-line returns, and to re-establish pricing models based on customer volume and business priorities.

Profitability and Service Execution

And consider this leading IT company in a technology growth area that has been experiencing the impacts of resource competition, escalating cost of services and various management issues. Boosting overall profitability by selecting the right projects to pursue and increasing the efficiency of IT resource management are crucial.

Planning teams used BI data to integrate information sources, compare work-to-cash cycles, preview operating costs and assess overall firm and project risk. BI data views allowed multiple “what if” scenarios to be analyzed and compared to operational efficiency and growth goals. The analysis pointed to the benefits of a new and targeted campaign to focus on key technology customers with high-margin potential.

These examples illustrate how BI can be used to use existing data to improve efficiency and profitability. But how do you get started, who does the work, and how long before you can begin using BI tools to improve decision making?

Implementing BI

The BI project has three main implementation steps: discovery, design and development.

During discovery, the BI team focuses on customized business metrics and performance management strategies. All stakeholders and owners provide input to questions that ask what, how, when, where and why. The quality of this discovery effort is crucial to the project’s ultimate success.

Phase two is design, also known as creating the “cube” where all internal and external data sources are identified as fundamental inputs to the data matrix. Conceptual and logical data models are explored, and different ways to consider the required outputs are proposed and reviewed with cross-functional teams. It is here that the data warehouse is populated and can become quite large.

The third stage is development, and it is here that the database is configured with naming and metric conventions. The database is partitioned to provide efficient generation of the critical tables, data sets and business outputs needed for real-time tracking of key metrics. The teams must also consider external data sources that will be ongoing inputs to the data warehouse, and establish workable links to maintain ongoing accuracy. Finally, user training and sign-off protocols are created to run mock deployments outside of the actual manufacturing or business streams for testing and troubleshooting.

Cloud on the Horizon?

Larger companies may depend on database administrators to set up and load a new BI database. Smaller organizations may take advantage of the BI vendor’s service options to review an organization’s needs and complete initial set-up and training, and to provide ongoing data management and updates.

The core business, manufacturing and operational teams must be intimately involved with establishing the business critical variables as this will guide creation of the initial data base. Data may include such things as variable costs to manufacture, hourly production rates, number of global customers, pricing, sales volumes, yields, amount of different part numbers and many other factors needed to process, calculate, and display end-goal information.

Most recently, BI systems are evolving to cloud-based platforms, using software-as-a-service technology to build and maintain a core database. Moving IT off premises in this manner can provide benefits in terms of reduced internal data center and IT management costs, faster system deployments and increased flexibility to address changing business needs.

As with any cloud-based decisions, however, security concerns are a primary consideration when sending sensitive data beyond firewalls. Security is thus a primary focus of many BI suppliers, and a point to consider when comparing on-premises to cloud-based solutions. Although security concerns exist, cloud-based storage firms may provide advantages in terms of a structured backup regime, a staff of full-time cyber security experts, and duplicate data storage across multiple geographical locations.

BI technologies are expanding to provide customized solutions for many businesses and enterprises. Understanding how this technology can be used will enable firms to support their organization’s next generation business improvement initiatives. Once properly implemented, BI solutions make tough problems much easier to solve by providing data-driven responses to expedite and improve the decision making process.

Editor’s note: Art Creidler has most recently been a product development specialist and senior product manager at E.I. DuPont for its global flexible circuit materials business. He has been a leader in various DuPont product launches and commercialization growth in the military, aerospace and technology industries. He holds a Bachelor of Science in Mechanical Engineering degree from the University of Delaware.

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