Collaboration Tools Knowledge Managers Need in the Enterprise

The Collaborative Enterprise

Over the last decade, business processes have grown significantly more complex and interdependent, requiring a high level of collaboration within and between departments, divisions, and teams. The growth of collaborative platforms and tools designed for knowledge managers, however, continues to lag in terms of functionality and depth of integrative process support compared to what is needed by many enterprises and organizations (Huberman & Wilkinson, 2010). The collaboration tools managers really need include a highly scalable and customizable series of integrated analytics, data integration, knowledge workflows, and business performance management modules that can be selectively applied to a given strategy or initiative.

One of the most pressing areas of this need is in supply chain collaboration (Ramesh, Banwet, & Shankar, 2010), where the level of coordination and synchronization has a direct impact on profitability and overall company performance. Another area in which the need for enterprise-wide collaboration has become particularly urgent is enterprise project management (Harley, 2011). Ultimately, collaboration directly influences the profitability and long-term growth of enterprises, yet to date the field lacks the requisite platform of analytics, integration, and application support needed to meet the escalating needs of knowledge managers (Cross, Gray, Cunningham, Showers, & Thomas, 2010). The intent of this analysis is to evaluate what collaboration tools knowledge managers really need and to assess the factors that impede their adoption and use over time.

Designing More Effective Collaborative Systems and Tools

While collaboration platforms — including applications, systems, and processes — continue to become more sophisticated and capable of tracking performance over time, they lack the ability to attain a high level of knowledge sharing and value creation (Harley, 2011). This is especially true in the areas of supply chain collaboration (Ramesh, Banwet, & Shankar, 2010) and collaborative processes within complex manufacturing environments (Rosenzweig, 2009). These two areas have the greatest potential for revolutionizing how companies achieve their most difficult strategies through the combination of data, analytics, and collaborative workflows (Huner, Otto, & Osterle, 2011).

At present, vendors providing collaborative tools have worked diligently to create process workflows that can scale across different departments and divisions, encompassing transaction-based and information-sharing workflows in their applications. The reliance on Business Process Management (BPM) and support for the Business Process Execution Language (BPEL) format for communications continues to differentiate leading applications from others in this market (Harley, 2011). There is also a growing focus on creating more effective programs for tracking project performance and analytics across the enterprise in terms of dollars and time invested per resource in a given project (Cross et al., 2010).

Higher-end applications also have the ability to anticipate and learn from queries submitted by knowledge managers, analysts, and production staff. Taken together, these queries are interpolated over time into a series of rules or constraints that govern the data selected to fulfill a given request for information. This reliance on rules- and constraint-based logic to gain greater insight into knowledge worker queries has proven to be highly effective for streamlining supply chains, given the augmented insight it makes possible (Ramesh, Banwet, & Shankar, 2010). The use of constraint-based and rules-based knowledge management systems is also streamlining the development of more complex manufacturing workflows, making build-to-order, configure-to-order, and engineer-to-order workflows more effective over time (Rosenzweig, 2009).

Supply Chain Collaboration and Manufacturing Workflows

The use of constraint- and rules-based software engines as part of collaboration applications and platforms is also highly effective in helping enterprises move beyond a narrow focus on a single set of metrics, replacing that perspective with one that sees across the entire organization. Constraint- and rules-based engines in collaboration platforms are critical for accessing, interpreting, and making full use of the massive amounts of data companies have accumulated over decades but previously lacked the analytical tools to interpret (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011).

The reliance on collaborative applications and platforms that are capable of streamlining complex manufacturing processes — reducing per-unit costs through greater intelligence-driven efficiencies — represents an emerging area of best practices in collaborative systems for manufacturing (Rosenzweig, 2009). These systems have the ability to align information and knowledge to specific steps in a lean manufacturing process, further accelerating a company's progress toward its objectives of producing higher-quality, lower-cost products (Rosenzweig, 2009). This integration of lean manufacturing concepts and knowledge management is most often found in highly complex, specialized manufacturing operations.

A prime example is in the area of production scheduling and production system planning and process definition. The Toyota Production System (TPS), perhaps one of the best-known systems in the automotive industry, is specifically designed to support highly integrative knowledge management workflows and development processes for onboarding new suppliers as quickly and accurately as possible (Dyer & Nobeoka, 2000). The Toyota Production System is also known for its ability to convert expertise and intelligence into a lasting competitive advantage, turning supply chain expertise and insight into a formidable process advantage that translates into profitability and long-term growth across many of the company's product lines globally (Dyer & Nobeoka, 2000).

Conclusion

The development of collaborative platforms and applications to streamline supply chain and complex manufacturing processes is predicated on how effectively an enterprise can manage and overcome resistance to change while also re-engineering wide areas of its production processes. Collaborative systems share a common set of attributes, with the need for greater analytics, data intelligence, and integration representing an unmet need that vendors must respond to and fulfill. As enterprises grow more complex and interdependent, closing this gap will become not merely a competitive advantage but a strategic imperative.