Supply_it is one of the SaaS solutions from MHP's Industrial Cloud Solutions portfolio. They accelerate the digital transformation along the value chain. © MHP

The prerequisite for this is systematic, data-based planning. However, this is precisely where there are often deficits: even today, many companies still plan their intralogistics primarily based on experience from past projects and the know-how of their employees: a lengthy, iterative and generally error-prone process - especially when the framework conditions for production change frequently due to new products and variants.

Digital twins for an overview

Intelligent planning tools can help here. The starting point for these systems is usually a digital twin: this digitally and mathematically maps the real intralogistics environment and records all existing processes, including the associated costs. To do this, it takes data from various sources - such as the company's own SAP system - as well as the product lifecycle management system. Based on this data, the algorithms connected to the digital twin analyze the current status. This enables the system to automatically determine the associated costs for each process step and for each individual component that is moved to or on an assembly line. This automated allocation is an important competitive factor, especially for companies that take thousands of individual components into account in material flow planning - because only sufficient transparency enables targeted improvements.

A co-pilot for planning

Digital twins are nothing new in principle. Brick-and-mortar retailers, for example, have been using them for some time to optimize sales areas and shorten internal transport routes. However, there have hardly been any solutions on the market for production-related intralogistics in particular. The SaaS solution Supply_it from MHP, presented for the first time at LogiMAT in collaboration with Porsche Consulting, aims to close this gap: it provides planners with targeted support using the co-pilot stored in the system. This easily visualizes the existing cost structure of intralogistics and proactively makes suggestions as to how and where companies can achieve the greatest savings and time savings through their material flow planning. Among other things, the stored logic automatically searches for the most cost-effective process, taking transport routes into account. It also identifies different possible combinations in which production can install individual components as efficiently as possible. The logic also takes into account existing restrictions, such as capacity limits of the transport systems used or storage areas.

The cloud solution also offers helpful simulation tools: If a company wants to adapt its processes, Supply_it Logic calculates the associated effects - even for different scenarios. This reduces the risk of companies introducing inefficient processes and the danger of planning errors. In concrete terms, our practical experience shows that with Supply_it, user companies are able to achieve a cost-optimized allocation of components in a short time - often in less than ten minutes.

OEM saves around 10 percent of intralogistics costs

An automotive manufacturer is already successfully using Supply_it in its material flow planning and uses it to manage more than 3,000 individual components. Thanks to its introduction, the OEM has been able to save around 10 percent of its intralogistics costs to date. One reason for this is the more efficient utilization of the driverless transport vehicles used in production.

The company achieved further optimizations directly on the assembly line: for example, it was able to reduce its handling costs for installing seat belts by 1.50 euros per piece by reorganizing the relevant processes. With around 60,000 belts installed per year, a single cost-optimized step reduces the total annual costs by around 90,000 euros. This shows impressively that intelligent solutions save considerable costs - companies from the mechanical engineering or automotive industries that have to evaluate a large number of individual components and have a large number of restrictions such as picking and sequencing simply need to be prepared to part with conventional tools such as Excel and open up to new tools.