Supply chain automation has been the primary focus of many companies for well over a decade. While the production line, storage protocols and loading procedures have advanced at lightning speeds, the decision-making process has remained more or less the same.
A small number of issues are identified that can be handled by preset procedures at the ground level. Anything that happens outside this set is passed up the chain of command to be analyzed and dealt with. The time lost in conveying this information, even with IoT devices in place, waiting for a response and then acting on that response, eats up considerable chunks of otherwise productive time.
Logistics is a critical supply chain component of the manufacturing industry. Road Transport in India constitutes a bulk of the logistics chain. Its optimum and efficient utilization governs supply chain profit margins for many companies. On-time delivery by vehicles also helps in customer satisfaction and retention. To control and improve the utilization of these mobile assets, it’s imperative to monitor and manage their movement and performance.
The reports linked to GPS-based location tracking are useful in performing post-mortem analysis of the delivery performance, however the data flows in only after the mistake or issue has already taken place. Developing action points after the fact is reactive and detrimental to business in the long run.
Why does this happen despite the readily available technology and mountains of processed data at your disposal? There are several reasons.
First, the lack of coherent data. Several parameters are monitored separately under separate reports. It’s difficult to relate them to each other to arrive at a concrete conclusion. Normally, individual truck data is available, but cumulative data clustered at a logical hierarchical level is not.
Second, the absence of advanced analytical tools. There is a limit to the manual analysis that can be performed on a spreadsheet.
Third, the presence of functional silos with conflicting objectives in the same organization. For example, the dispatch team focuses on volumes, but the safety team focuses on incident-free delivery. Add to these the challenge of maintaining multiple service providers across different functions and a basic understanding of the problem starts to form.
A control tower addresses all these problems.
Imagine all your data points being captured coherently and analyzed against your entire database. You can determine the best transporter in a plant or the worst-performing plant in a region without having to muddle through piles of reports. Imagine advanced analytics providing you with live, definite and measurable action points. The system will identify actions that can be taken to rectify mistakes on the fly. While doing this, it is keeping an eye on every other aspect it’s plugged into and making sure they clock over nicely. The centralized nature of this system means that all relevant stakeholders will be using a single tool to measure and meet their key performance indicators while keeping the focus on the overall organizational objective.
The control tower relays automatic warning to the relevant process or task owner before a performance parameter is breached. The suggested action helps stem the issue before it gets out of hand. Measurements of user response time, effect of the response and timeframe to implement are used to further enhance the system. Ultimately, this helps improve performance, set benchmarks and fine-tune your logistics chain into a well-oiled machine.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.