Contents:

How to Create a Culture of Quality Improvement

3 ways big data can improve your supply chain

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3 ways big data can improve your supply chain | World Economic Forum!

The Emergence of Factory Organization. The Age of Standardization. A Case Study in the Manufacturing.

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Military as Customer and Controller of Subcontractors. What Industry does for the Sake of On Registrars and Bureaucratic Power. In response, the company redesigned its planning process, dedicating more time to component planning and eliminating bottlenecks from data flows and IT processing. Furthermore, by improving the quality of the data for the component planners, the company was able to reduce the time wasted chasing data and fixing errors.

It could also promote governance issues in close cooperation with individual departments, as well as knowledge management and training. The origins of today's quality ideology and industry is traced, followed by a description of how the quality profession popularizes, promotes and ultimately benefits from the fads that come and go. This might mean making change within processes, organizational structures, performance metrics, incentive systems, or the distribution of roles and tasks. The quality-management department might, for example, be made responsible for central coordination and communication. Data about production reliability, adherence to schedules, and equipment breakdowns should be visible across functions. The supply chain function depends on up-to-date manufacturing data, but the manufacturing function may tightly guard valuable reliability data so that mistakes will be less visible. Another automotive OEM leveraged number 5, extending the shadow of the future.

And it developed more sophisticated analytical tools for measuring the accuracy of forecasts. On the basis of these and other organizational and process improvements, the company expects to improve forecast accuracy by up to 10 percentage points for components and 5 percentage points for systems, resulting in improved availability of parts and on-time delivery to customers.

The changes are expected to yield an increase in revenues, while lowering inventory levels, delivering better customer service, and reducing premium freight costs. Over time, many such fixed networks have trouble adapting to the shifting flows of supplies to factories and of finished goods to market. Some networks are also too broad, pushing up distribution costs.

How to Create a Culture of Quality Improvement

The tangled interrelationships among internal and external networks can defy the traditional network-optimization models that supply chain managers have used for years. Leaders can study more variables and more scenarios than ever before, and they can integrate their analyses with many other interconnected business systems. Companies that use big data and advanced analytics to simplify distribution networks typically produce savings that range from 10 to 20 percent of freight and warehousing costs, in addition to large savings in inventories.

A major European fast-moving-consumer-goods company faced these issues when it attempted to shift from a country-based distribution system to a more efficient network spanning the continent.

The company used advanced analytical tools and techniques to design a new distribution network that addressed these rising complexities. It modeled multiple long-term growth scenarios, simulating production configurations for 30 brands spread across more than ten plants, each with different patterns of demand and material flows. It crunched data on 50, to , delivery points per key country and looked at inventory factors across multiple stages.

Planners examined numerous scenarios for delivery, including full truck loads, direct-to-store delivery, and two-tier warehousing, as well as different transport-rate structures that were based on load size and delivery direction. Unlocking insights from this diverse data will help the company consolidate its warehouses from more than 80 to about As a result, the company expects to reduce operating expenses by as much as 8 percent.

As the number of warehouses gets smaller, each remaining warehouse will grow bigger and more efficient. And by pooling customer demand across a smaller network of bigger warehouses, the company can decrease the variability of demand and can, therefore, hold lower levels of inventory: Operations leaders who want to explore these opportunities should begin with the following steps. Connect the supply chain from end to end.

Many companies lack the ability to track details on materials in the supply chain, manufacturing equipment and process control reliability, and individual items being transported to customers. In order to have big data to analyze in the first place, companies must invest in the latest technologies, including state-of-the-art sensors and radio-frequency identification tags, that can build transparency and connections into the supply chain. At the same time, companies should be careful to invest in areas that add the highest business value. Many companies struggle to optimize inventory levels because lot sizes, lead times, product SKUs, and measurement units are entered differently into the various systems across the organization.

While big-data systems do not require absolutely perfect data quality and completeness, a solid consistency is necessary. That can change when leaders make the impact of poor data clear and measure and reward consistent standards. Build cross-functional data transparency. The supply chain function depends on up-to-date manufacturing data, but the manufacturing function may tightly guard valuable reliability data so that mistakes will be less visible.

The data could also help customer service, which might inform customers proactively of delayed orders when, for example, equipment breaks down. Data about production reliability, adherence to schedules, and equipment breakdowns should be visible across functions.

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To encourage people to be more transparent, management might assemble personnel from different functions to discuss the data they need to do their jobs better. Invest in the right capabilities. Hiring a team of top-shelf data scientists to do analytics for analytics sake is not the answer, however. Companies need to both partner with others and develop their own internal, diverse set of capabilities in order to put big data into a strategic business context.

Only then will they be able to focus on the right opportunities and get the maximum value from their investments. Companies that excel at big data and advanced analytics can unravel forecasting, logistics, distribution, and other problems that have long plagued operations. Those that do not will miss out on huge efficiency gains.

They will forfeit the chance to seize a major source of competitive advantage.

3 ways big data can improve your supply chain

This article was originally published by The Boston Consulting Group. Read more insights from BCG on bcg. Workers on the assembly line replace the back covers of inch television sets at Element Electronics in Winnsboro, South Carolina May 29, The views expressed in this article are those of the author alone and not the World Economic Forum.

Quality and Power in the Supply Chain What Industry does for the Sake of Quality

Workforce and Employment 3 ways big data can improve your supply chain. Want more climate action? Why are there so few entrepreneurs? Robert Gryn 17 Sep More on the agenda. Explore the latest strategic trends, research and analysis. Three High-Potential Opportunities But with so much available data and so many improvable processes, it can be challenging for executives to determine where they should focus their limited time and resources.

How to Begin Operations leaders who want to explore these opportunities should begin with the following steps. Workforce and Employment View all.