Advanced Analytics in Supply Chain

The sole role of analytics is to support decision making. Through Advanced Analytics, a supply chain can leverage more insights with more accuracy. This empowers to take decisions better, faster and/or with more confidence. Specific use-cases include the following:

Create inventory visibility and visualise which products rotate at which speed through your warehouse and why (decreased sales, increased returns). Use the available data to segment your products in high- and low-rotating units and provide this as input to your warehouse manager to relocate goods and alter safety stock levels.

Derive root-causes of delivery promise failures such as vendors who deliver to late, fulfilment partners who exceed average delivery times – and identify supply-chain improvement initiatives.

Get smarter into product development by leveraging data-driven insights on your customer- and order base: what are my customers segments, how did they grow over time, how are they in one region vs. another region, what are their shared preferences, which products features do they like

Reduce lead time by understanding when which drivers impact lead-time at what impact: which parts increase the risk of production delay, which parts require a strategic inventory? With no IT investment, a solid data-mining exercise through your supply chain order-, production- and delivery data can likely already identify low hanging fruit opportunities.

Optimise inventory space and value by forecasting demand with accuracy. Do you overestimate, you will likely overproduce and stack up inventory; do you underestimate, you’ll miss sales. Through analytics we can analyse your historical sales data and assess patterns driven by seasonality, partner activity, marketing activeness, offline sales agents, weather or even country-specific GDP. Turning these patterns into inputs, we predict sales and thus prescribe needed inventory levels.

Locate geographical growth opportunities by visualising all order, delivery- and customer-locations and deriving sweet spots for new sales hubs, production sites or warehousing depots. Assess supply-chain merger potential by visualising overlapping supply-chain networks, assessing overlap and thus assessing strategic added value.

Assess failure patterns of production machines to understand which drivers are recurrently causing failure (volumes, #batch switches, temperature, speed, operator). Then translate these drivers into inputs building an early-warning-trigger tool/model to pre-empt failure (first steps of predictive maintenance).

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