Deploying an Industry 4.0 Solution In-House, OutSourced or Jointly
The promise of the benefits that can be reaped from big data and predictive analytics through access to machine and operator data is compelling enough for most manufacturers to seriously direct their IT and OT departments to look into ways to enable such machine and operator data acquisition. Indeed, being able to optimize overall equipment effectiveness or OEE in terms of equipment availability, performance and quality of produced items by the equipment directly affects manufacturers’ bottom lines.
In this quest for big data and predictive analytics the question of rolling a solution in-house vs. outsourcing comes up inevitably. It’s therefore instructive to look at smart manufacturing examples, along with the pros and cons. However, rather than focusing on a laundry list of items to consider pros and cons for, it is more useful to boil this list down to the following six KPIs to measure and compare both approaches by:
- Flexibility: The ability to add-on new features or to modify existing features.
- Resources: The effectiveness and availability of human resources.
- Speed: The ability to meet project timeline goals.
- Know-How: The Domain expertise to ensure project success.
- Cost: The cost of human resources and cost impact on other projects.
- Ongoing Support: The resources needed to ensure post-project success.
The following presents an analysis on how each KPI may play out in both cases.
| KPI | In-House | Out-Sourced |
| Flexibility | Maximum control over resources, but impacted by effectiveness and availability of resources as well as cost in case of new skill sets and expertise and if resources must be shifted between projects. | Varies depending on the Ts & Cs agreed upon for ongoing technical support, whereas new add-ons and modifications are fulfilled through ramp up and ramp down of resources without impact to other projects. |
| Resources | Resources are almost always in short supply and may not have the range of expertise required to deal with specific project areas and dynamically shifting resources between projects almost always meets with significant resistance. | Projects are usually resourced with the right number of people, skill sets and expertise to deal with the full scope of the project and dynamically shifting resources between projects is restricted by contractual obligations to meet project goals. |
| Speed | Speed is directly impacted by resource effectiveness and availability, and therefore project timeline goals are directly impacted by resource limitations. | Projects are usually resourced with the right number of people, skill sets and expertise to deal with the full scope of the project and therefore impact on project timelines is minimal. |
| Know-how & future-proofing | Resources are usually behind on state-of-the-art due to a lack of project-specific specialization, which not only negatively impacts the implementation, but also risks that the solution will quickly become outdated. | Specialization is paired with know-how on the latest technologies, which enables efficient implementations and helps with future-proofing the solution, avoiding future costs of having to redo parts or the whole solution. |
| Cost | Cost of resources will increase if people with new skills and expertise have to be hired, and/or in case other projects are impacted by shifting resources. | Human resources are temporarily assigned and reduced as the project winds down to a minimum as required for ongoing technical support. |
| Ongoing support | Ongoing support is at risk since employee turnover can leave significant gaps in skill sets required to fix bugs, implement enhancements and/or add features. | Ongoing support is customarily part of the agreement, securing the resources necessary for bug fixes, enhancements and/or feature additions. |
Practical use of this table consists of assigning a ranking for each case for each KPI and computing the total. Among these KPIs perhaps the one that stands out the most is for the know-how and future-proofing of the solution. For example, the IPC – Association Connecting Electronics Industries® recently created the Connected Factory Exchange or IPC-CFX standardized data exchange protocol as an enabler of Industry 4.0, Smart Factory and Digital Factory solutions. Awareness and understanding of IPC-CFX is likely not the focus of in-house IT staff, but certainly within the scope of Aegis Software and GEM.
In our experience the ranking of the above KPIs may change a lot between the first project and subsequent ones. Usually the first project is the one with the highest risk and least chances to succeed if done entirely in-house. Therefore, the ideal approach may be a hybrid one, in which a joint team is formed, consisting of internal personnel and expert external resources, and where the first project heavily relies on the expertise, skill sets and full presence of the external experts, while for subsequent projects the internal resources take on a larger role.
Through the partnership of Aegis Software and GEM we are able to deliver end-to-end Industry 4.0 smart factory solutions to manufacturers, spanning the full scope from machine and operator data acquisition to OEE KPIs, predictive maintenance, as well as comprehensive MES functionality. Through our combined resources, skill sets and expertise in implementing and rolling out Industry 4.0 smart manufacturing solutions we are able to score consistently very high against each KPI in the above table.
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