Im Einzelhandel entstehen jährlich weltweit 121 Millionen Tonnen Lebensmittelabfälle, was sowohl ökologische als auch finanzielle Herausforderungen für Unternehmen mit sich bringt. Die unzureichende Planung von Ressourcen und Warenangebot sowie ineffiziente Lieferketten verschlechtern die operative Marge und verringern die Flächenrentabilität. Langsam rotierende Produkte und nicht optimierte Bestände führen zu erheblichen finanziellen Einbußen und verschwendeten Ressourcen. Circly bietet eine fortschrittliche KI-Lösung zur Optimierung dieser Prozesse, die sowohl Umwelt als auch Finanzen zugutekommt.

Shelf metre optimisation and conservation of valuable resources

Circly has created a solution to enable retail companies to be environmentally friendly and sustainable while improving profit margins. Utilizing Circly’s predictive AI (artifical intelligence) solution, the high level of accuracy of sales predictions enable precise planning of resources, resulting in increased shelf availability, reducing excess inventories, stock-outs and food waste.

 

Keywords

• Supply Chain optimization for retail companies
• Standardization of data structure and transmission
• Sales forecasting through AI
• Waste reduction and financial upsides
• Finance savings

Herausforderungen der Supply Chain Optimierung im Einzelhandel

Circly's challenge was to optimize and enhance the retail company's existing supply chain planning process. Prior methods consisted of manual efforts, analyzing sales figures through Excel files and statistical planning of future demands and required resources. Due to this manual approach, human resources and product planning was not optimally predicted resulting in inefficiencies, causing negative impacts to financials.

Umsetzung der KI-Lösung zur Effizienzsteigerung im Einzelhandel

Ein wesentlicher Bestandteil der Optimierung war das Verständnis und die Verknüpfung vorhandener Prozesse. Die betriebliche Planung der meisten Abläufe hängt von der Nachfrage und einer Vielzahl von Faktoren ab. Diese reichen vom Produktportfolio bis hin zu Bestellmengen, Verkaufsdaten, Preisschwankungen, Rabatten etc.

 

After analyzing the existing system and its processes, suitable machine learning concepts were selected, implemented and trained with existing data. The data is composed of internal and external regressors that influence demand and forecasting. Internal factors include sales data and product groups, while external factors include weather data or public holidays. The challenge was to also address COVID-19 impacts and extract useful information from it. During the implementation of the system, Circly encountered several efficiency gaps, which were addressed and corrected.

 

Circly's first aim was to implement consistent data structures and transfer, enabling the models to simplify and optimize sales forecasting. In doing so many manual processes were partially automated, while considering employee concerns. The implementation of a fully automated AI system is a gradual process, as the trust in this technology needs to be gained and the new process logic has to be understood. However, the ultimate goal is to implement a fully automated interface to the existing system. This will optimize resource planning, minimize food waste and workload distribution of personnel through standardized processes.

Ergebnisse der KI-gestützten Einzelhandel Optimierung

Environment

• • Reduction of food waste

• • Resource reduction through accurate planning

• • Sustainable supply chain management

Manpower

 

• • Standardized system allowing optimized workload distribution

• • Increased efficiency

• • Reduction of redundant work processes

Company

 

• • Reduced stock vacancy or overstocking

• • Space optimization for higher turnover per sales area

• • Operating cost reduction through elimination of shortages and surpluses

• • Improved reliability of the supply chain

For further information, please contact contact@circly.at or use our contact form https://circly.at/en/contact to book an appointment.

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