3 Steps to Building An Integrated Supply Chain in Indonesia

The COVID-19 pandemic has pushed the supply chain organization to seek tools that can help them make a better, more accurate, informed, and faster decision. By using a new approach and data insight, it will help supply chain organizations to have better insight that will make their operations much more effective and productive. Here are some of the new approaches that will produce massive improvements.

Improved part segmentation

Market trends and customer demands could change in unexpected ways. Companies that stock parts entirely based on historical data may end up with excess inventory. For example, some household appliances today have Wi-Fi features. If demand for them increases, manufacturers may be overwhelmed. To avoid this problem, industrial companies with high levels of product customization have to divide parts into two segments: purchased to stock and purchased to order. 

These are the three metrics that are essential for the segmentation. The first is customer lead time, which is defined as the time between receiving an order and the specified delivery date. The second one is supplier lead time which is defined as the time between receiving an order from supplier and the specified shipment date, both for actual and contractual supplier. And the third one is time limit, which is a cutoff time that is calculated as the customer's lead time minus the number of days it takes to manufacture, process, and ship the product.

Enhanced stocking-algorithm accuracy

Algorithm based methods to monitor stock inventory typically work well for low customization and high volume business, but this method is much less effective for lower volumes and multiple product companies.

Beside the algorithm that's causing problems, the current ERP and forecasting tools often rely on specialized skills and are challenging to handle with. So, The SSP (segment, stock, & plan) approach works to eliminate the issues by combining the following elements: 

- A limited forecast period. Under the SSP approach, the stocking algorithm only creates forecasts for later time periods— an approach for reducing assumptions and errors while improving order quantity accuracy.

- An embedded mechanism for scaling. If the sales forecast unexpectedly changes, the SSP stocking algorithm will predict the necessary increases or decreases to the order level for all parts. The new stocking algorithm will also lead to better decisions that are made from more accurate historical data. 

- An accurate historical data. By using an average across a few lead-time segments, this new stocking algorithm measures historical demand and consumption. For example, if the lead time is 30 days, the traditional algorithms will use 12 increments to cover a whole year of historical data (12 x 30 = 360 days). But, instead of just using an average across a few lead-time segments, this new approach of the stocking algorithm uses daily increments of lead time – using 360 increments and taking the average. So, the new algorithm would be 30 times more accurate than the traditional algorithm, because it uses 360 increments rather than 12. 

In terms of the complexity of businesses, Telkomsel IoT Control Tower as the advanced technology allows the company to have optimized and reliable data for a more effective delivery. Also, this will take the advantage of stocking-algorithm with more accurate data, resulting in better decision making.