Organisations looking to improve the management of their supply chains are often faced with a complex and overwhelming array of technology choices. Decisions end up being made, resulting in the inappropriate use of systems, integration complexity due to fragmented technology architectures and costly write-offs or re-implementations.
Our broad exposure into the use of various technologies to improve supply chain management and execution has led us to develop a model which puts these technology choices into context and hopefully simplifies debate and decision-making in this regard.
We have chosen to group technologies based on two fundamental attributes. The first is the perspective the technology solution has of the entire value chain, from a narrow function-oriented view on the one hand to a holistic integrated view on the other.
The second attribute considers the time period under review, where some technologies solutions typically concern themselves with recording historical events, while others are geared to focus on determining and controlling future outcomes.
The diagram below shows how we have grouped supply chain management technologies into four categories, the characteristics of which will be defined in more detail in this paper.
ERP systems consist of a set of modular business applications and have traditionally been geared to support the execution of single function transactions or capture activity/event information. Their focus is therefore often dedicated to improving process control and efficiency as well as accelerating process execution.
ERP systems have evolved to include the orchestration of integrated processes, fulfilling the role of supply chain event management tools. This extends their value chain perspective to include the control and response to planned and unplanned events, thereby further accelerating and improving process execution. By extending the review period perspective, ERP systems are able to use defined business rules to consider short-term options and implications and support more proactive operational decision-making. Available to promise and time slot allocations are examples of this.
Supply chain business intelligence (BI) involves the use of data warehousing, presentation and analytical technologies in the collection, management and analysis of supply chain activities and measures. Supply chain BI solutions focus on creating visibility and supporting analysis of historical transaction/activity data to assist in improving the understanding of past performance and supporting informed decision-making.
On the function-oriented end we see supply chain performance management tools providing detailed visibility of operational key performance indicators against pre-defined thresholds/targets. Examples of this are management information systems, operational dashboards and real-time alerts. When applied for more holistic purposes, BI technologies are used to support tactical trend analysis across the broader value chain and assist in understanding cause and effect patterns at different levels of the organisation.
Examples include enterprise balanced scorecards, ad-hoc analytics and data mining.
The distinction between technologies which provide supply chain planning (SCP) support and those that assist in advanced planning and scheduling (APS) is slightly more complex. This is often an area of technology which prompts the most debate and where the boundaries are least understood.
Both SCP and APS (also referred to as advanced planning and optimisation) tools do not perform transactions themselves, but rather use mathematical/heuristic modelling and simulation to analyse and optimise the supply chain. While both provide planning and decision support at strategic, tactical and operational levels, SCP provides management tools that focus on holistic supply chain decision-making, and APS focuses on detailed function optimisation under the constraint of previously determined strategic priorities.
SCP applications typically span the entire value chain by providing a means to model scenarios and simulate outcomes. As such these tools do not make decisions, but rather allow managers to better make decisions. SCP tools are often applied to manage supply channel constraints, find optimal costs option, facilitate collaborative links with key suppliers and create alignment across the supply chain.
Naturally, optimisation can extend to all areas in the supply chain where data is available (including SC partners) as well as integrate planning internally. For SCP tools to operate optimally, information sharing with partners is essential. This enables organisations to tame the bullwhip effect and curb other inefficiencies inherent in planning where visibility is limited. Typical functions include support for: strategic network design & tactical decision support, sales & operations planning, CPFR and product life cycle management.
APS typically operates in the domain of internal function planning, control and optimisation to synchronising capacity and workload, with the aim being to maximise on-time delivery, inventory turns or profit.
APS tools are often used to add a layer of planning to the strong transactional capability of ERP systems and the integration is often bi-directional (APS decisions impact operations and operations influence decision outcomes). It is important to note that all APS functions are constrained in some way by fixed or defined business rules. Typical APS activities include functional resource and material optimisation, what-if analysis, finite scheduling, constraint management, dynamic routing and lead time calculation.
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