In recent years, data has been touted as the latest economic asset to give businesses an edge over the competition. Here’s the reality: Data is valuable, but many manufacturers struggle to obtain and maintain the level of quality required to extract knowledge that allows them to make smart decisions. Bad data ― and by extension, bad knowledge ― results in unworkable plans, lost opportunities and high costs. A staggering 91 percent of businesses make common errors that result in inaccurate, incomplete and outdated data. 81 percent struggle to generate meaningful insights because of data inaccuracies. Inaccurate knowledge derived from bad data causes a negative impact on the bottom line. 91 percent of businesses report wasted budgets because of inaccuracies. 43 percent cannot even account for how much budget is wasted every year. A report estimates that it can cost a company up to 25 percent of its revenue to rectify and prevent recurrences in knowledge inaccuracies. And this estimate can only rise as enterprise data volume grows by 40 percent yearly in tandem with the increasing number of consumers and enterprises performing online transactions.
Experts recognize accurate knowledge as the basis for productivity, innovation and competition. 99 percent of manufacturers have a data strategy in place along with improvement plans to increase the quality of their knowledge. Yet, only the right solution will enable manufacturers to overcome data quality issues, extract insights that matter and start making smarter operational decisions in real time. The solution is the Self-Learning Supply Chain ― an advanced analytics capability embedded into the supply chain.
Real Data, Real Plans, Real Results
The Self-Learning Supply Chain turns supply chain planning and optimization into a self-learning process. This process is divided into five stages:
- Data: Plans created with advanced analytics technology are only as good as the data that goes into them. In the first stage, machine learning captures real-world data flowing into the supply chain through various channels ― for example, order characteristics, process setup times and processing times from the production floor. The solution identifies and filters outliers in the data to ensure consistent quality. It then checks the data against the constraints of your resources and updates it to ensure the highest level of accuracy at all times.
- Knowledge: Every process and transaction automatically generates data. It is difficult, near impossible, for humans to identify useful patterns within the deluge. Take, for example, bottlenecks in the production line that shift in different conditions or operational rigidities that prevent you from responding quickly to disruptions. Knowledge that human experts derive from data and interpret based on their own experiences can be fallible. Conversely, the Self-Learning Supply Chain’s data-driven knowledge extraction uses algorithms to identify and condense patterns in data into useful information for planning. It then analyzes the data continuously to keep it up-to-date even as conditions change.
- Planning and optimization: The Self-Learning Supply Chain’s self-learning capability, which learns from actual data and captures patterns of logic and judgment, often surpasses the accuracy of human experts. It replicates these learnings throughout the supply chain as the basis for effective planning and optimization. It identifies and optimizes factors proven to have the most positive impact on a company’s KPIs and reduces factors proven to have the most negative impact.
- Optimized plan: The plan is optimized for execution in the real world ― with minimal deviations. Actual execution data (e.g. setup times, processing times and waiting times) is captured and fed back into the solution for further planning and optimization. This continuous improvement via a closed feedback loop generates knowledge that reflects the reality of your operations. As knowledge is continuously refined and accuracy improved, you can expect your plans to become increasingly precise and your operations to stabilize over time.
- Business value: As manufacturers execute the plans created by the Self-Learning Supply Chain, they begin to move from merely cutting cost to increasing revenue. Operational efficiency improves as adherence to plans increases. Planners have the support they need to improve the speed and accuracy of their decision-making. The result? Maximized revenue, increased customer satisfaction levels and optimized use of assets and resources.
Support for Your Knowledge Management Journey
Incorporating a next-generation planning system into your business may seem overwhelming. The right support from an experienced solutions provider is crucial in making sure that the data you collect is accurate and, more importantly, functional. This ensures that the baseline knowledge used by the Self-Learning Supply Chain is adequate and can assist your planners in their planning. Then, there are the three stages of the knowledge management journey:
- The solution tracks and monitors your adherence-to-plan KPI. This enables your planners to measure how well their plans were realized or whether deviations occurred during execution. It also indicates whether the knowledge used in your planning process is sufficiently accurate and up to date.
- Once the solution finds the structure of knowledge to be sound, it moves on to propose appropriate values based on existing data. Because the structure of the knowledge is maintained, the impact on existing processes and procedures is minimal.
- The final stage involves full-fledged, data-driven knowledge discovery. The Self-Learning Supply Chain applies its machine learning capability to historical data to derive accurate knowledge and determine the structure and values of this knowledge. Because this process is fully automated and requires minimal human involvement, it can be repeated as regularly as you need (i.e. weekly, monthly or quarterly) to ensure that knowledge is up to date at all times.
Configured to fit your business reality and powered by world-class optimization technology, the self-learning supply chain has the ability to intuitively learn and replicate the logic and reasoning of the best decision-makers in your company. It prescribes actions and generates optimal plans that work in the real world.
Markus Malinen is VP of Sales, EMEA at Quintiq.
