## 5 Analysis  
The initial model for ZDM outlined in Section 4 is intended to be generic and adaptable primarily for manufacturing but also for process industry companies. As stated in the introduction, our view on ZDM is based on our previous research [3; 7; 8], and combines and integrates seven areas analyzed towards the initial model:  
* Monitoring of process parameters--the model in (1) provides an estimate for measured process parameters and in turn can be used to monitor the deviation between actual and desired values. * Collaborative manufacturing (which includes sharing of manufacturing data without media breaks along the supply chain)--the system equations and system descriptions given in (3), (5) and (6) can be a combination of several interconnected sub-systems that reflect the individual unit operation and the interconnections. In that way, it is even possible to reflect supply chains in an overarching system model that contains the bi- or multilateral information flow between individual unit operations or manufacturing units. * Continuous quality control (which includes measurement of input to the process and measurement of output from the process with automatic sorting)--quality control depends on the sampling and assessment of the produced components, which are not directly related to the system model. However, quality variations can be related to changes in the process and potential faults needing to be addressed. It is therefore of importance to not only represent the process in terms of its operational characteristics and degradation, but also how the process nominally leads to product qualities on the basis of input material properties. * On-line predictive maintenance--the system equations in (3) do contain states reflecting both the condition and operation of a process and are used to optimize process operation as stated in (7). Predictive maintenance relies on the proper prediction of the degradation of a process in relation to the operation of a process. It is thereby possible to provide an estimate of the remaining useful life of components in a process and to act proactively. * Data storage, analytics and visualization--the modeling approach described does not directly provide specific methodologies for storage or visualization, but is an integral part of the analytics by providing the needed predictions and estimates for process variables and process parameters. Analytics is usually an important component of any decision support solution for operation and maintenance. * Re-configuration and re-organization of production--a needed insight for these operational decisions is the condition of the process. Early detection of incipient faults is an essential understanding for production decision making. For this end, different production scenarios and their impact on both operation and degradation are integral aspects when tailoring the methodology to a specific case. * Re-scheduling of production--a basic requirement to have re-scheduling capability is the availability of additional production resources or timely flexibility. In conjunction with a predictive maintenance scheme, flexibility enables alignment of maintenance needs, operational needs and order flows. Ideally, this enables increased production system utilization and overall equipment efficiency. The reason for combining and integrating the seven areas is that when they are combined and integrated, a better result can be achieved [8]. Based on the analysis above, we posit that this can be done using our initial model for ZDM. In a practical manufacturing perspective at Gestamp HardTech AB (cf. [8]), the first five of the seven areas above are currently being addressed and the last two will soon be taken into consideration. A complete system model, i.e., digital twin, needs to be further developed. The development of systems for inline and high-accuracy geometry measurements of all output quality is progressing well. The continuous and accurate measurements of the output are key to the whole ZDM approach and model.