Mapping your value stream is a huge part of running an agile operation. Value streams reveal where product or service flow excels and where it breaks down, offering insight into how your organization can provide continuous value to customers and improve existing services or products for future deployment.
Traditionally, this is done on paper, and the map itself still works best with the physical presence of the mapper. Talking to your teams in person. Building the map as you go through the process yourself. Presence is key. When you’re done, it’s often the first time teams can see the full scope of the workflow. From here, improving the system takes over.
Improvements to that system are the new “X”-as-a-service. Leveraging the Internet of Things (IoT) is where businesses can gain an advantage over competitors and improve customer value through product and service iterations. Replace that “X” with just about any part of your value map and you’ve got Value-Stream-as-a-Service.
Defining Value Streams
Mapping value streams identifies areas where bottlenecks and miscommunications are undermining your efforts to provide continuous value to your customers. In an interesting exercise from this tutorial, for example, an initial value stream map revealed critical areas where communication just wasn’t happening and a significantly higher product deployment time to actual processing time.
There’s only so much your team can do. One of the most significant areas of AI could be the use of Value-Stream-as-a-Service models – mainly through IoT- to identify and streamline or possibly eliminate areas where you’re experiencing leaks.
Value-Stream-as-a-Service could be the next big thing for your organization because it could:
- significantly improve value streams without burdening your team
- provide better outcomes more quickly than training (or hiring) in-house
- automate actions so your human resources can get back to what they do best (innovate and problem solve).
Let’s take a look at a few ways you could soon deploy this newest style of service.
Ecosystem Driven Risk Management
Traditionally, risk management is a vital yet clunky part of a business’s workflow. Fraud detection was buggy during the first few years of IoT deployment because it was limited to localized aspects of the value stream. Risk Management can cut down on the headaches customers go through to verify identity, resolve fraud issues, and receive relevant info on security. Business side, IoT Risk Management streamlines fraud detection, builds automatic notifications and flags for potential risk factors, and shifts security to a centralized (better monitored) hub.
Downtime costs businesses dearly, so strategizing ways to reduce that downtime is critical to edging out the competition. With IoT, smart devices communicate directly to AI, reporting problems in fractions of seconds, scheduling the least intrusive times for repair or maintenance, and flagging potential downtimes far enough in advance to plan. In short, it turns downtime from a reactive process to a proactive process.
For example, large production requires the use of complex machinery. Scheduling regular maintenance means downtime and waiting for a machine to break down to schedule repairs causes massive downtime as well. You may or may not learn from the situation, causing unexpected downtime to repeat itself.
With connected machinery, AI can quickly assess the least intrusive time for scheduled maintenance. Machines on the verge of breaking could flag the system, or if that doesn’t happen, repairs can be initiated within seconds of the disruption, potentially saving thousands of dollars in lost revenue.
With successive iterations, organizations are looking to streamline the scaling goods and services. Traditionally, once organizations scale towards enterprise solutions, agile goes out the window. Although large companies do use Agile processes, it’s challenging to maintain lean operations when your organization is significantly larger.
Building smart processes such as AI-powered contracts or automated customer service helps reduce the bottleneck in your value stream. You could get alerts to potential upgrades, interact with customers through multiple social media channels, or digitize key pieces of your silo interactions.
Enterprises particularly benefit from that last one. Value streams using Digital Process Automation reduces the load on limited team resources and improves communication between the different parts of the Enterprise. Instead of manufacturing, warehousing, delivery, and management operating in relatively separate ecosystems, DPA improves communication flow and automates critical junctures in the map.
Value-Stream-as-a-Service is still somewhat challenging, particularly for older companies with decades of offline features built up. Brand new startups that operate solely in the cloud with no grandfathered capabilities could deploy Value-Stream-as-a-Service models relatively quickly. The same isn’t entirely true for things like utilities or commodities.
However, each new advance in deep learning and each further improvement in the IoT ecosystem gets us closer to field specific models that can upend what has always been bottlenecks, automating services where there may be labor shortages and easing communication between departments.
Meaningful collaboration could be possible between machines and their human counterparts. Sometime soon, we could see improvements in worker safety, reduce infuriating wait times on the customer side, and streamline industries traditionally considered too cumbersome to address. Value-Stream-as-a-Service models are coming. Is your business ready?