Highly mature enterprise: Meet the challenges of enterprise information systems
Standard metrics and actionable rules are available to step up from Level 3 to Level 4 in the enterprise information system maturity model, where automated decision making creates a highly evolved and mature enterprise. The enterprise information system maturity model presents a general context for understanding enterprise systems and a set of criteria to evaluate enterprise system needs. It also helps determine next steps to increase decision efficiency (and the bottom line).
The diagram shows the maturity model, Levels 0-4.
What is a highly a mature enterprise?
A highly mature enterprise focuses on automating manual activities that are routinely performed in response to analytics results. The Level 4 maturity model goal is to eliminate repetitive decisions so that personnel can focus on more difficult and complex issues. As usual, the purpose for moving up the maturity model is to increase efficiency. Automated decisions can range from simple to complex. An organization must determine if the efficiency gains are sufficient for the automation costs.
It’s important to understand the type of automated decision making that occurs at this level. The goal is not to assist personnel in making a decision in which the actual decision making is a manual activity. Plant manufacturing line scheduling is a good example of this type of decision making. Typically, inventory and manufacturing line availability (data that is stored in a database or accessed in real time) are compared to upcoming order requirements. A software application determines the best schedule to accommodate the constraints. The scheduler makes the decision to order the line based on this information. Here, the software application is used as a tool to assist in making a manual decision.
Level 4 focuses on decision making that is fully automatic. In this case, manual approval may be required to execute the decision, but the decision and the decision making process are fully automated. A good example of this is automated integration with a computerized maintenance management system (CMMS). Analytics are used to determine if a CMMS request needs to be triggered for a plant floor machine (based on operational characteristics and run time values, perhaps).
An organization at Level 3 would deliver information to the appropriate personnel based on an actionable rule. In turn, a manual activity would take place to log the request into the CMMS. At Level 4, this activity occurs automatically with integration between the analytics system and the CMMS—possibly without any manual interaction. If manual interaction is required, the action is simply an approval activity. If approved, the request is automatically logged by system.
Plant floor adoption
Increasing an organization’s maturity comes with many benefits. Correspondingly, as discussed throughout these articles, a large amount of effort and dedication is needed to implement the model. Also, as noted previously, the number of systems is inversely proportional to their maturity level.
So, what does this mean for the plant floor? Plant floor operations are typically late in adopting new technologies and concepts compared to other industries. As a result, the plant floor is one of the last business operations to reach Level 4 of the maturity model. However, it can be argued that the plant floor is one of the best environments for automated decision making.
Many examples of Level 4 maturity are encountered daily. For example, credit card monitoring and analytics can automatically decide to deactivate a credit card to prohibit fraud. These same approaches can be applied to the plant floor to make automated decisions and receive the same efficiencies.
Getting started with automation
The best way to determine what to automate is to investigate Level 3 activities. Review the actions that are manually performed based on the actionable information provided by analytics. Identify and rank the most repetitive actions, especially ones that occur most often and in the same way.
From that list, examine the risks associated with automating each action. At first, look for the most highly repetitive actions (which should gain the most efficiency through automation) that have the lowest risk for implementation.
It is important to build credibility and confidence with initial implementations. Failures at the start (that result in poor decision making) can dissuade businesses from the continued use of automated decision making. This is one of the most impactful events that prohibit an organization from reaching Level 4 maturity.
Start with a few small implementations and keep them localized within the organization to reduce the impact of possible failure. Although the most effective automated decisions typically include many business operation streams and even external organizations, it’s important to build on small successes first. Don’t be tempted to begin with a massive undertaking that can only provide effective results with high risk.
3 keys to automated success
1. Collaboration: As with all other maturity levels, success requires collaboration among many parties. Plant floor personnel need to understand the impact of the automation and how it affects their daily operations. Controls personnel (which can sometimes be an external vendor or integrator) need to understand the various systems to make modifications and provide ongoing support. IT personnel are required to support network connectivity and other underlying system support. And management personnel must approve the process and understand implications of changes.
2. Incorporating failure: Failure needs to be included as part of the normal operation of an automated decision and its associated actions. For each decision, determine what failure means within the context of the decision. What is the necessary corrective action in the event an incorrect decision is made? What if the automated decision process is unable to execute the action based on the result? Sometimes a notification or failover mechanism is required. In other cases, production may be affected or modified as a result.
3. Continual review: Keeping statistics on the success (and failure) of automated decisions and their impact on efficiency helps in understanding the overall effectiveness. Continual and scheduled review of these statistics is necessary to sustain Level 4 maturity operation. This data should be shared with all members of the collaboration team and evaluated over time to identify any trends. Examine failures as they occur within the process and redevelop response plans accordingly.
The maturity model presents a context for understanding enterprise systems. It provides a set of criteria to evaluate enterprise system needs and help determine next steps to increase decision efficiency and the bottom line. It also helps owners communicate more clearly with integrators, vendors, and other providers. Even though it’s presented as well-defined steps, the maturity model is not a rigid process. An organization can be at different levels throughout its different business operations. The model provides a guide and presents a roadmap for organizations and their partners to increase their system maturity and, correspondingly, their overall success.
– Corey Stefanczak is senior system architect, Leidos. Edited by Mark T. Hoske, content manager, Control Engineering, firstname.lastname@example.org.
- A highly mature Level 4 enterprise meets the challenges of enterprise information systems.
- Standard metrics and actionable rules help organizations move from Level 3 to Level 4 in the enterprise information system maturity model.
- Automated decision creates a highly evolved and mature enterprise.
- See 3 keys to automated success.
What systems can you more effectively automate using the tools provided in this five-part series?
This article online contains links to each part of this full five-part series on meeting the challenges of enterprise information systems looking at the maturity model introduction, taking the first step, gaining a competitive advantage, optimizing resources, and a highly mature enterprise.
Part 1: Understand the maturity model to better manage, integrate plant floor, enterprise systems Control Engineering (CE), August issue, Inside Machines section, p. M1
Part 2: Migrating toward enterprise information systems from Applied Automation (supplement to CE and Plant Engineering), October issue, p. A13
Part 3: Gain a competitive advantage, meet the challenges of enterprise information systems CE Weekly News enewsletter, Nov. 25
Part 4: Optimizing the climb up the enterprise information systems maturity model CE November issue, Technology Update, p. 34
Part 5: Highly mature enterprise: Meet the challenges of enterprise information systems CE December issue, Inside Machine section, p. IM4 [this article]