CHAPTER TWO Review of Literature
2.10 Strategic use of Knowledge Management in business and public sector in developed Countries developed Countries
23 Another specialised system that is designed to effectively deal with ‘Big Data’ is ‘Stream Computing’. This is an analytical processing system that is effectively designed to deal with frequently changing data (data in motion). It uses predictive analytics to promote real-time decisions. It also captures and analyses data at any given time whilst working on a just-in- time basis. One of the main benefits of this is the ability to store less and analyse more which in turn promotes better and faster decision making (IBM, 2014). Other systems that can satisfy the processing of ‘Big Data’ also include Content Management Systems which effectively manages documents and data contents allowing them to be properly controlled.
Advanced or high-performance databases and data warehouses are also used that function at high speed and have intense analytical capabilities to deal with large scale data (IBM, 2014).
These are among the main systems used in KM and BI. There is little evidence of them being used in HE in Africa. However, substantial evidence exists of its use in developed countries.
This will be covered in greater detail in section 2.13.
24 in turn supported and enhanced core organisational processes. Across all 6 organisations, KM was mainly used (ibid):
- as core support to Product Development Manufacturing (PDM) - to share knowledge through various team networks
- to enhance the capturing, storing and organising of best practices in the form of Standard Operating Procedures and this was done via specialised technological KM tools
- as part of process improvement and achievement of strategic objectives
- as part of policy deployment which in turn allowed KM to become a company-wide strategy for the management of knowledge resources
One of the companies had an interesting and also the largest KM system in place out the six surveyed companies. In context, the company’s best practices were documented, reviewed and refined and stored in a global systems database. This technology based system significantly improved the manufacturing process of the organisation and facilitated the sharing of best practices for process improvement activities within the company groups around the world (Ibrahim Edgar and Reid, 2009). The overall findings showed that KM was contributing to organisational performance and delivering benefits in various ways which related to performance, processes, quality and productivity. Cultural approaches also played a vital role when it came to team motivation towards adopting KM as a knowledge sharing tool and therefore staff participation was deemed a critical element in any KM initiative (Ibrahim, Edgar and Reid, 2009).
Based upon their findings, the authors derived the following framework of KM (Figure 2).
This framework is not just limited to the automotive sector, but can apply to almost any manufacturing type of organisation that opts to utilise KM strategically.
25 Figure 2: Conceptual framework of Findings
Source: Ibrahim, Edgar and Reid (2009)
This related well to Kamara, Anumba and Carrillo (2002) who developed the CLEVER (cross-sectoral learning in the virtual enterprise) framework that focuses on the organisational and cultural dimensions of KM. The author assessed the role of KM across fifteen companies in the manufacturing and construction industries and built the CLEVER framework based on the findings. The main findings that facilitated the development of CLEVER included the integration of Information Technology systems that ensured reliability and consistency in KM related activities across the organisation (Kamara, Anumba and Carrillo, 2002). Effective use of technology-based project management tools, documentation and revision-based systems (to revise project plans based on lessons learnt from past activities) was also incorporated in to the framework (Kamara, Anumba and Carrillo, 2002).
By utilising the CLEVER framework as a KM strategy platform, organisations could derive a strong KM strategy. The CLEVER framework is depicted in Figure 3.
Top Management Commitment IT Role
Organisational Culture People Involvement
Objectivist- based Perspective (IT based)
KM Measurement Objectivity Approach - Metrics
- Planning vs Target - Cause and Effects analysis Subjectivity Approach KM Applications
K-Based Tools Sharing Best Practices Process Improvement Policy Deployment Face-to-Face Organisaional Meetings
Added Value
Financial Value
Operational Benefits - Quality - Cost - Cycle time - Lead time - Product to market
Business Process Improvement - Decision making - Resolved complaints - No need to reinvent
wheel
Safety
- Reduce accident rates
Culture - Motivation - Team working Practice- based
Perspective (Human-based) Strategy
26 .
Figure 3: The CLEVER Framework Source: Kamara, Anumba and Carrillo (2002)
Knowledge Management is also being used as a strategy in the public sector such as in the conducting of police investigations in some countries. It was argued by Gottschalk (2006), that there was a need to identify stages of growth in KM systems and apply them to police investigations. The author referred to these stages as officer-to-technology systems, officer- to-officer systems, officer-to-information systems, and officer-to-application systems (Gottschalk, 2006). On the basis of this, a model was developed to strategically facilitate the planning of KM systems in police investigations and law enforcement. Each stage documented in Gottschalk (2006) provided its own advantages to how this model could be strategically applied to police investigations. In context, the study showed that harnessing information and converting that to knowledge through proper KM modelling can prove vital to the public sector. A visual description of this is shown in Figure 4.
Identify ‘To- Be’ Solution Define KM
Problem
Select
Appropriate KM Process(es) Identify Critical
Migration Paths
27
Stage 4 Investigator-to-Application
How- they-Think
Stage 3 Investigator-to-Information
What- they-know
Stage 2 Investigator-to-Investigator
Who-knows-what
Stage 1 Investigator-to-Technology
End-user-tools
Figure 4: The Knowledge Management Systems Stage Model Adapted from Gottschalk (2006)