*5.4.1 General overview*

general, BP external metrics deals with BP material inputs, while appropriate material costs play a role of principle importance. However, this is only one side of the coin, while the BP output products create an integral part of BP external metrics as well. The KPI indicator closely related to BP external metrics is postulated via formula (106). In order to express adequate numerical values the secondary KPI indicators concerned to output products and input materials should be applied.

KPIBP 1ð Þ¼ f g ½ � KPI\_BPemet ið Þ *;* j ¼ f½ � MATfincosts ið Þ *;* j , MROPselmatassets i ½ � ð Þ *;* j ,

However, any BP is represented by its internal metrics as well, while he KPI indicator closely related to BP external metrics is postulated via formula (107). In order to express adequate numerical values the secondary KPI indicators concerned

KPIBP 2ð Þ¼ f g ½ � KPI\_BPimet ið Þ *;* j ¼ f½ðDEV\_costs ið Þ *;* j11 ,ðDEV\_assets ið Þ� *;* j12 , ½ðTOOL\_costs ið Þ *;* j21 ,ðTOOL\_assets ið Þ� *;* j22 , f½ðHR\_costs ið Þ *;* j31 ,ðHR\_assets ið Þ�g *;* j32

The total business process KPI indicator value might be postulated with respect to formula (56). A detailed visualization of the above-mentioned KPI indicator

However, The sets [(DEV\_assets (i,j12)] and [(TOOL\_assets(i,j22)] are closely related to depreciation and amortization of devices and tools, as while, [(HR\_assets

(i,j32)] are closely related to extra contributions generated by employees.

(106)

(107)

½MROPselfinassets ið Þ *;* j �g

*Operations Management - Emerging Trend in the Digital Era*

to output products and input materials should be applied.

*A detailed visualization of BP KPI indicator components. Source: The authors.*

components are shown in **Figure 4**.

**Figure 4.**

**184**

The BPLM Strategy Creator should be implemented and operated like aim oriented knowledge based or expert system (ES), which consist of an appropriate knowledge base (KB) and inference engine (IE). Both of two subsystems consist of three components closely related to strategic, tactic and operational management levels. However, knowledge contained within KB are being represented via adequate reference databases (RDBs) and semantic networks (SNWs) as well, while IE should enable retrieval and presentation of knowledge contained in ES-KB and generation of new (primary) knowledge based on knowledge actually contained within ES-KB. An interaction between RDBs and SNWs provide transformations rules converted into appropriate transformation functions.

BPLM Strategy Creator is being implemented and operated via adequate knowledge based (expert system), which consists of two subsystems denoted as BPLM\_01\_06\_06\_01 ES Knowledge Base and BPLM\_01\_06\_06\_02 ES Inference Engine. The Knowledge Base subsystem operates over knowledge base, which contains adequate knowledge, while the Inference Engine subsystem provides retrieval and presentation of knowledge contained within knowledge base and new knowledge discovery based on existed one [25].

When considering the knowledge-based content, we have to talk about knowledge representation. The knowledge representation principle applied within that project is based on existence of reference databases (RDBs), transformation rules (TRrules), transformation tools (TRtools) and semantic networks (SNWs) and is closely related to an appropriate management level (strategic, tactic, operation.

#### *5.4.2 BPLM ES knowledge base*

The BPLM ES Knowledge Base functionality is being assured via four modules: (a) Data preparation (b) Reference Database (RDBs) (c) Creation, Semantic Network (SNWs) creation and (d) Import of SNWs to Knowledge Base. The Data preparation component is running within four subordinated steps and modules: (a-1) Data extraction, (a-2) Data transformation, (a-3) RDBs update, (a-4) SNWs creation.

*In the first step,* an appropriate data is extracted from various documents or they are prepared as a result of the document semantic analysis, while *in the second step* their structure should be transformed to adequate RDBs structure and stored to the RDBs and pre-defined SNWs pointers are being generated. Afterwards*, in the third step*, all the above –mentioned data are stored to linguistic sets and prepared RDBs subsequently. In the fourth step appropriate SNWs are being created and stored to BPLM ES Knowledge base.

#### *5.4.3 BPLM ES inference engine*

The BPLM ES Knowledge Base functionality is being assured via four modules: (a) KB content retrieval, (b) Knowledge discovery and (c) Presentation layer. The KB content retrieval operates based on Knowledge general and detailed requirement, which enables selection of appropriate knowledge records, while the selected knowledge record content is visualize via Presentation layer, which consist of the following modules: (c-1) Strategic layer, (c-2) Tactic layer, (c-3) Operational layer and (c-4) Analytical layer
