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Due to uncertainties in demand across nodes of supply chains, companies usually adopt sub-optimal solutions, such as excessive stock keeping or settling for lost sales – both reported and unreported. We help our clients greatly reduce these uncertainties with node-level and system-level demand forecasts at SKU or category levels. We use machine learning methodologies to develop forecasting models with high predictive power and fully utilise internal and external data such as sales, pricing, promotions, competition, events, weather, etc. In our engagements we also define the implications of forecasting results on related nodes of supply chain with new business rules and processes.
Machine Learning & AI Teams
Inventory Management with Parts Segmentation
Regardless of source of demand forecast – forecasting model, sales people opinions or hybrid of both – end product forecasts do not immediately translate to supply chain practices. This is because, a single end product usually has a long list of bill of materials that are linked to multiple suppliers with different production or delivery constraints and there are usually additional internal constraints such as production or warehousing capacities. These complications become more severe for industries with high number of SKUs. To overcome these uncertainties, we design parts segmentation engine for our clients with segments of buy (produce) to stock and buy (produce) to order considering demand characteristics and lead times of parts in bill of materials. By adopting the results of this engine in their operations, our clients can realise significant savings in terms of stock costs, while decreasing stock-outs. The inputs to the algorithms in this engine should be maintained regularly later in order to reflect changes in lead times, customs tariffs, bill of materials, etc.
Supply Chain Structure
Decision makers need to rethink how supply chain functions as a whole in every several years due to geographic shift of demand, introduction of new technologies and ways to manage the operations. We evaluate numbers and types of nodes (e.g. distributors) in the supply chain with the objective of system-wide cost efficiency. We also evaluate possibilities of collaboration models with partners in the supply chain and identify opportunities for use of macro / micro hubs to fulfil end-customer demand at the right time. Our engagements produce innovative and winning structures in supply chains that need revamped management approach. We define this new management approach with necessary processes and implications on organisation.
With the ongoing shift in global supply chains following COVID-19, certain previous practices are becoming obsolete and importance of procurement is increasing in order to contain costs for organisations. We help our clients throughout the overall source-to-pay journey including spend category strategy definition, vendor management, placing and receiving orders, processing invoices and payments and analytics along these steps. With the advancement of technology both in terms of data capture and processing made previously non-automatable activities automatable. We define the areas of automation potential in order first to increase standardisation in decisions and second to realise FTE savings. Our technology team is generally end-to-end involved to ensure implementation success.
New Digital Technologies
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