Typically, as soon as the new supply chain system goes live, an organization is eager to have the basic transactions—production planning, materials planning, and procurement—up and running, and it rushes to put these in place. The organization focuses on the transaction reports, and supply chain optimization, monitoring, and performance management all take a “back seat” (are placed second in importance).
Most organizations have been operating with a supply chain system for a fair amount of time, and they have advanced along the learning curve. That is, they have reached a certain level of understanding of supply chain management (SCM). Now their focus is moving toward the next level of complexities: supply chain monitoring and performance management.
Following are some of the more important goals organizations are trying to reach, the functionalities they require to achieve these goals, and current trends in the area of supply chain reporting, monitoring, and performance management.
1. Corporate strategy to operations strategy
Organizations are trying to create a framework for top management to link corporate strategy and operations strategy. So once senior-level executives (CXOs) decide on on a corporate strategy’s objectives, they want to break it down at every level of the organization’s hierarchy. At each of these levels, the key performance indicators (KPIs) are designed to align to the top-level key performance index.
For example, the chief operations officer (COO) may be targeting a 5 percent reduction in costs for the current financial year. To meet this objective, the COO may plan to reduce SCM costs by 4 percent. SCM costs may be the responsibility of the vice president of supply chain. SCM costs, in turn, may be broken down further into lower-level KPIs, which might be the responsibility of lower-level managers. Thus, the operations strategy is modeled to align with the top-level corporate strategy.
2. Dashboards and scorecards
Organizations are looking forward to visually intuitive dashboards that display the current status of KPIs vis-a-vis their targets. Color codes are an added advantage in that they denote the status of various KPIs. Organizations require a flexible tool that will allow them to model the scorecard by giving different weights to various KPIs.
3. Target setting
The supply chain analytics system should be able to capture the target values of KPIs, and should provide the functionality to direct to the bottom level the target that is decided at the top level of the organization’s hierarchy, based on some predefined logic.
4. Benchmarking
Benchmarking is an important feature for organizations that want to measure their performance against industry standards. Thus, the system should be able to capture data from various publications that publish KPI benchmark figures for various industries.
5. Predefined KPI models
Organizations anticipate the benefits of out-of-the-box, predefined KPIs that are based on popular industry-standard supply chain models, such as the supply chain operations reference (SCOR) model. They believe that the out-of-the-box content will help save on implementation time. The system should be flexible to support any changes that an organization might want to make to the KPI hierarchy or the KPI formula.
6. Flexible reporting structure
The system should have various predefined reports. The reporting structure should be flexible so that users can customize reports by adding or removing columns to meet their requirements.
7. Drill-down feature to perform root cause analysis
The drill-down feature is essential, as this allows users to navigate through the various levels of the KPI hierarchy. This will enable users to perform a root cause analysis of any supply chain problem, thus saving valuable time when diagnosing and correcting problems. The drill-down functionality should also be available for various dimensions, such as product, product group, customer, customer group, company, region, etc.
8. Role-based access
The system should support role-based access to data, KPIs, and reports, which is required to maintain data confidentiality and data integrity. Role-based accessibility should also be supported at the various levels of the dimensions. For example, a user may have access to the delivery schedule report only for certain product categories and certain regions.
9. Simulation and what-if analysis
Simulation is a very important feature, as executives can simulate various scenarios and perform what-if analysis. They can see how the KPIs perform under different conditions. For example, the vice president of supply chain may change the value of the production schedule adherence KPI to see how it affects inventory levels. This will help to fix the KPI target at an optimal level.
10. Predictive modeling
Predictive modeling will help the system build relationships between various KPIs, based on the past transitional data. For example, the system should be able to find the correlation between forecast accuracy KPI data and the finished goods inventory data, based on past information. The system needs a predictive engine to perform such analysis. Once relationships between KPIs have been established, the system can store them, which will help users to perform root cause analyses.
11. Alerts
The system should be able to generate alerts and send notifications, through e-mail or other means, about changes or problems, thus allowing the person responsible to take timely corrective actions.
12. Data flow
A tool this powerful and flexible must be built on a data warehousing framework, which inherently supports some of the features discussed above. Data can be uploaded into the data warehouse (DW) through various means. Direct links can be established between the supply chain analytics system and the transactional and planning systems (that is, enterprise resource planning [ERP] and supply chain planning [SCP] and optimization systems), or data can be uploaded into the DW through flat files. (See Figure 1.)
Figure 1. The flow of information to the supply chain analytics system from other key enterprise systems.
Based on my experiences working on supply chain projects with a variety of companies, Table 1 shows how user companies rate the supply chain analytics system features discussed above.
| Functionalities | Essential Functions | Nice-to-have Functions |
| Ability to link corporate strategy to operations strategy | Y | |
| Dashboards and scorecards | Y | |
| Target setting | Y | |
| Benchmarking | Y | |
| Predefined KPI models | Y | |
| Flexible reporting structure | Y | |
| Drill-down feature to perform root cause analysis | Y | |
| Role-based access | Y | |
| Simulation and what-if analysis | Y | |
| Predictive modeling | Y | |
| Alerts | Y | |
| Data flow | Y |
Table 1. Company ratings of supply chain analytics systems’ features.
Supply chain analytics is becoming popular, as organizations are finding traditional reporting structures incapable of handling the increasing complexities of their supply chain. In addition, supply chain analytics allows organizations to make the necessary alignment between top-level KPIs and lower-level KPIs, which helps the organization work toward a uniform goal and vision.
In essence, supply chain analytics is a tool that will increase the speed of decision making, which will help to increase the supply chain’s flexibility and adaptability, and help organizations cope with the uncertainties of the operating environment.
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