Alignment of demand and supply management was supposed to have been solved by now thanks to software applications and new e-business practices. However, in an era characterized by large inventory write-offs and a growing suspicion about the perceived value of enterprise-wide supply chain solutions, logistics professionals have every right to insist on demonstrable return-on-investment (ROI) from the systems they’ve implemented.
So how do you evaluate supply chain performance?
To answer that question, analyst firm IDC partnered with Logistics Today to survey the logistics landscape and bring some clarity to the situation. This article provides a practical guide on how to measure and monitor logistics operations, based on exclusive research undertaken by this magazine and IDC, and supplemented with additional market research and competitive intelligence from IDC.
An analytic application, as IDC defines it, must meet each of the following three conditions:
A closed-loop applications model developed by IDC emphasizes the importance of linking transactional systems with analytic systems (see Figure 1).
The return on investment from analytic applications can be substantial. In fact, analytic projects that focus on operations or supply chain processes have the highest median ROI (277%) as compared to those related to CRM (55%) or finance (139%) processes.
SCM analytic applications enable the monitoring and analysis of operational data generated through such activities as inventory, warehousing, logistics, order management procurement, materials management and manufacturing. The analysis guides decisions on adjustments to operations that are then monitored, continuing a virtuous circle. This linking of analytics to operations and ongoing measurement is the key to maximizing ROI.
As organizations look beyond optimizing short-term processes, further efficiencies are gained by expanding their view to a broader time horizon and a view across the entire supply chain. It is here that analytic applications for SCM become important as a competitive differentiator. Continuous management and improvement of the supply chain are feasible only through the applications of such a system, and analytic applications make the deployment of such systems feasible.
The key to the comparatively high ROI in supply chain management-related analytic processes seems to be the highly focused and defined nature of issues to be solved and relatively shorter decision-making cycles than in other areas of the enterprise. For example, using analytic applications to analyze and improve product production quality, negotiate better sourcing contracts with suppliers, align demand and production plans and forecasts, and optimize delivery routes and inventory levels can result in immediate bottom-line benefits to organizations.
In both cases spreadsheets are the most commonly used software tools for enabling analytic processes in procurement and planning functions. The next most frequently used applications are business intelligence software and enterprise applications (ERP, SCM, CRM). These results were not unexpected.
Spreadsheets (e.g., MS Excel) remain pervasive as analysis tools in organizations of all sizes and industries. However, spreadsheets bring with them numerous problems as companies strive towards decision process automation.
An optimal analytic application is one that creates an environment of controlled empowerment — empowering decision makers (e.g., managers, analysts) to analyze data without ongoing reliance on the information technology (IT) department. Just-in-time data availability without “special” requests to IT should be the norm.
Analytic applications enable interactive query and reporting that allow for analysis of data across various dimensions instead of static reports produced by IT at set time intervals. At the same time the IT department should have control over centralized development, maintenance and management of such analytic applications.
While spreadsheets do provide users with the flexibility to perform various types of data manipulation tasks (e.g., sorting, filtering, charting, pivot tables), they fail to provide centralized control that is crucial from the corporate perspective. The resulting environments include stand-alone, spreadsheet-based applications that usually lead to inefficiencies in data integration, reconciliation, collaboration among end-users, application development and IT staff utilization. The inefficiencies in turn result in dissatisfaction with existing software.
The levels of satisfaction with current procurement and planning analytic software are shown in Figures 5 and 6. As both figures show, spreadsheets or software provided by enterprise applications vendors prompts the highest level of dissatisfaction with end users. At the same time end users seem to be most satisfied with business intelligence software for procurement analytics and planning functions.
Clearly a disconnect still exists between the type of applications most often deployed for the two analytic functions discussed above — the software with the highest adoption levels results in the highest levels of dissatisfaction among users.
Organizations of all sizes are facing an increasingly competitive environment where speed and accuracy of decision making has the potential of providing competitive advantage. Latest market research suggests that many companies are unprepared to face this economic environment based on the types of tools they employ to support both strategic and tactical decision-making processes.
Companies should be increasingly turning to analytic applications to help them measure, analyze and optimize business performance, and to leverage existing investments in transactional systems. Such applications support activities beyond the scope of transactional systems and are currently provided by business intelligence and specialty analytic applications, as well as ERP systems. The decision on where to acquire analytic applications will depend on each company’s internal resources, existing IT environment and feature/ functionality mix of various applications.
However, as stand-alone systems, analytic applications will not have the desired impact and will fail to provide the expected return on investment. Feedback from analytics must lead to corrective action that impacts business operations. If it does not, there is no clear way to measure its impact on the business.
Thus analytic applications must do more than provide information — they must guide the decision-making process, leading to actions that improve business performance. To maximize the business benefits, users should ensure they:
ROI: How to Evaluate Your Supply Chain Performance