The Truth About Value Stream Mapping
Value stream mapping involves “mapping” the set of all specific actions required to bring a specific product through the three critical management tasks of any business:
1.Problem Solving (e.g., design, new product development).
2.Information Management (e.g., order processing and other non-production activities).
3.Physical Transformation (e.g., converting raw materials to finished product).
What Value Stream Mapping Is
In their book about value stream mapping, Learning to See, Mike Rother and John Shook define a value stream as “all of the actions (both value added and non-value added) currently required to bring a product flow from raw material into the arms of a customer.” In a sense, value stream mapping is a powerful tool that helps you truly see the flow of your product. Many organizations leverage value stream mapping to continuously improve and pursue operational excellence. Leaders of these organizations recognize the need to walk the process in order to know the process. They also recognize that performance can be subjective, so value stream mapping helps a group come to a consensus and measure if necessary.
What Value Stream Mapping Is Not
It’s an important reminder that value stream mapping is not a flow analysis, which uses a comprehensive set of calculations and algorithms to design optimum product flows. Rather, it’s a tool to help teams develop a consensus-based view of value stream flow, not discrete operations.
Value stream mapping is also not just corporate “wall paper” that hangs on the wall and is ignored. Rather, it’s the basis for a prioritized action plan within a value stream context.
Data Collection in Value Stream Mapping
Data are the most important aspects of problem solving, as bad data can lead to bad decisions, which will lead to bad results.
Too much data can:
- Waste valuable resources, time and money during data collection
- Distort the purpose (scope) of the intended project
- Take too much time to analyze and crash the analysis tools
- Cloud the overall intent
While too few data can:
- Create wrong or poor decisions which lead to poor results
- Duplicate work once having to gather more data
When collecting data for value stream mapping, you first want to consider what analysis tools will be used. The analysis tool will dictate the amount and type of data required, as well as what results to expect from the data. Tip: try to run fake “simulation” data in your analysis tool before gathering actual data. This process will iron out some of the common mistakes made before data collection. Other considerations should include:
- Gather as many classification identification variables as possible with the data: Time, machine, auditor, operator, gauge, lab, material, target, process change, conditions, shift, etc.
- If data occur in time sequence, record the order of data capture
- For changes over time, record the measurement before and after the stabilization period
- Screen or filter data to detect and remove data entry errors such as digital transposition and magnitude shifts due to a misplaced decimal point
If embracing lean supply chain management, it would only make sense to measure attributes of the lean supply chain in data collection. But, some organizations continue to measure transportation cost, cases per hour, inventory turns, and a host of other “output” measures. Effective measurement in value stream mapping should include “inputs” such as:
- Lead-time reduction
- Eliminating all non-value
- Process discipline
- Inventory reduction
- First-time quality
- Highest value
- Total cost
LeanCor provides value stream mapping and other supply chain consulting services for companies in many different industries. Visit our website to learn more about these services.Share