WiSys adaptable tools are purpose-built to automate unique business processes and allow agile business decisions based on real-time data through:. The benefits of real-time recording of data include lower costs, fewer data entry errors, improved customer service, reduced inventory, better compliance, and the ability to expand without additional personnel.
SAP Integrated Business Planning
Real-Time material management at the first touch point in your organization. WiSys Pallet Management provides a very robust solution for building, shipping and recording the movement of pallets. SDK for Macola. Agility Pallets provides for the storage and movement of pallets license plate in the warehouse using barcode scanning. You can build a pallet on the fly or during the fulfillment…. Agility is an out-of-the box solution that is easy to install….
This integration is key to helping Macola deliver on their promise…. Want to learn more about how Wisys Agility works and what Wisys can do for your business?
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See how the WiSys Agility applications improve business processes throughout your supply chain to create a real-time warehouse environment — from planning and receiving an order to filling and shipping that order. Ready to learn how to use our tools? Visits our video training library where we share everything from introductions to technical drill-downs to make sure you are using all the WiSys tools effectively. The digitization of the supply chain enables companies to address the new requirements of the customers, the challenges on the supply side as well as the remaining expectations in efficiency improvement.
Digitization brings about a Supply Chain 4. Digital waste prevents supply chains from leveraging the potential of Supply Chain 4. In today's supply chains many sources of digital waste can be found in addition to the existing waste that prevent the potential of Supply Chain 4. The sources of digital waste can be classified in three types:.
Often, available data is handled manually data collection in a system, paper-based data handling, etc. Another example in warehousing is advanced shipping notifications, which are received but not used to optimize the inbound process. On top of these examples, it is typically not clear which additional data could be leveraged to improve processes, e.
In current systems, this signal will not be recognized and will lead to a lower supplier service level reported at the end of the month. If the worst comes to the worst, the issue will cause trouble in the assembly line replenishment and operational problems.
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Many companies have started to implement an integrated planning process, but very often this is still done in silos and not all information is leveraged to achieve the best planning result possible. In addition, it can frequently be observed that automatically determined planning or statistical forecast data is manually overwritten by planners.
Especially for parts moving at medium or high speed, the manual overwrites usually have a negative impact on the forecasting accuracy. Beside the intracompany optimization, the process optimization between companies has not been fully leveraged yet and improvement potentials created by increased transparency are not realized.
To get to the advanced level of integrated process optimization, the organizational setup, governance, processes, and incentives need to be aligned within and between partners in the supply chain. Nowadays, warehousing, assembly line replenishment, transport management, etc.
Warehouse operations are still managed in batches of one to two hours, not allowing the real-time allocation of new orders and dynamic routing. Also, opportunities arising from new devices, such as wearables e. Supply Chain 4. In the end, the improvements enable a step change in service, cost, capital, and agility.
The future supply chain planning will largely benefit from big data and advanced analytics as well as from the automation of knowledge work. Two example levers with significant impact are "predictive analytics in demand planning" and "closed-loop planning. Predictive analytics in demand planning analyzes hundreds to thousands of internal as well as external demand influencing variables e.
Companies using Oracle Supply Chain Management and its marketshare
These new technologies enable a significant improvement of demand forecast accuracy, often reducing the forecasting error by 30 to 50 percent. Also, the days of a "single truth" regarding the forecasting numbers are over - these advanced algorithms provide probability distributions of the expected demand volume rather than a single forecast number. Widely automated and fully integrated closed-loop demand and supply planning breaks the traditional boundaries between the different planning steps and transforms planning into a flexible, continuous process.
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Instead of using fixed safety stocks, each replenishment planning considers the expected demand probability distribution and replenishes to fulfill a certain service level - the resulting implicit safety stocks are therefore different with every single reorder. Another powerful feature of closed-loop planning is the integration of pricing decisions with the demand and supply planning; depending on the stock levels, expected demand, and capability to replenish, prices can be dynamically adapted to optimize the overall profit made and minimize inventories at the same time.
Logistics will take a huge step change through better connectivity, advanced analytics, additive manufacturing, and advanced automation. For example, as warehouses are being automated, we will see a significantly increasing amount of autonomous and smart vehicles, and 3-D printing changes warehousing and inventory management strategies completely. The next generation of touch, voice, and graphical user interfaces and their quick proliferation via consumer devices facilitates a much better integration of machines in almost any process in warehousing operations.
For example, the breakthrough of optical headmounted displays, such as Google Glass, enables location-based instructions to workers, giving guidance for the picking process. Advanced robotics solutions have emerged for the improved picking of cases and single pieces, and the use of exoskeletons that emulate the human physiology and can support straining manual movements will have a major impact on warehouse productivity.
In total, warehouse automations become much more holistic, with some warehouses being fully linked to production loading points, so that the entire process is carried out without manual intervention. Autonomous and smart vehicles will lead to significant operating cost reduction in transportation and product handling and at the same time provide benefits regarding lead times and lower environmental costs. The use of self-guided vehicles in controlled environments e.
Autonomous trucks for use on public streets, however, are just being piloted in Europe and North America with promising results so far. Besides the automation of warehouse processes, additive manufacturing will also have a significant impact on physical flows in the supply chain. For example, 3-D printing has become much more relevant for a broad range of business applications, such as local production of slowly moving spare parts or tools.
This development is driven by an expanding range of printing materials, rapidly declining prices for the printers, and increased precision and quality. By now, the first production facilities that operate exclusively with 3-D printers have been established. Performance management is indeed changing tremendously.
Whereas in the past, the generation of KPI dashboards was a major task and KPIs were only available at aggregated levels, now granular data is available in real time from internal and external sources. This moves the performance management process from a regular, often monthly process to an operational process aimed at exception handling and continuous improvement. For example, planners can be pointed to critical supply chain disruptions and further supported by an automatic handling of minor exceptions or potential solutions for the larger ones.
Automated root cause analyses are one approach for exception handling. The performance management system is able to identify the root causes of an exception by either comparing it to a predefined set of underlying indicators or by conducting big data analyses, leveraging data mining and machine learning techniques. Based on the identified root cause, the system will automatically trigger countermeasures, such as activating a replenishment order or changing parameter settings in the planning systems, such as safety stocks.
Two examples of how order management is improved are no-touch order processing and real-time replanning, which lead to lower costs through automation of efforts, higher reliability due to granular feedback, and superior customer experience through immediate and reliable responses.
No-touch order processing is the logical next step after implementing a reliable available-to-promise ATP process. Through an integration of the ordering systems, linking to ATP, and through an enrichment with order rules, the system can be used to fully automate the ordering process. The goal is to have a complete "no-touch" process, where no manual intervention is required between order intake and order confirmation.
Very stringent order rules that have to be followed, and continuously updated master data are prerequisites.
Supply Chain Management Based on SAP Systems: Order Management in Manufacturing Companies
Real-time replanning enables order date confirmations through instantaneous, in-memory replanning of the production schedule and the replenishment in consideration of all constraints. Therefore the supply chain setup is always up to date, leading to a very reliable planning base. On top, additional services can be offered to the customers, e.