Advanced Analytics in Supply Chain
Advanced Analytics is "the use of new mathematical approaches and tools aimed to turn data into new insights. One should think about regressions, forecasts, segmentation and driver-analyses but also think about use-cases around you: e.g., the forecasting accuracy of your Amazon deliveries or your smart Google maps route recommendations.
In Supply Chain, Data Science and AI is offering tools to make the process more accurate, more reliable at reduced costs. They offer smartness in procurement (demand forecasting), in-bound logistics, inventory management (safety stock level recommendations), manufacturing (predictive maintenance) and fulfilment (optimized multi-stop routes).
Although the concept of Advanced Analytics has been trending for many years, many organizations today still struggle in understanding its meaning, envisioning the opportunities and embracing a successful implementation.
We are convinced Advanced Analytics can be accessible for companies of all size, doesnt require a big investment and will bring added-value.
Advanced Analytics applications 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 visualize 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 driversimpact 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.
- Optimize inventory space and value by forecasting demand with accuracy. Do you overestimate, you will likely overproduce and stack up inventory; do you underestimate, youll 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 visualizing 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 visualizing 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).
Above insights are all driven by Advanced Analytics but start from datasets which most organizations have readily available in their ERP or BI system. This stresses again that Advanced Analytics does not need to be complex nor is it designed to be used only by big organizations. With the right supporting technology and introduction, any analyst can now without programming leverage the tools available to get intuitive insights, visualize outcomes or even build a predictive model.
At element61 our belief is that its important to go step-by-step and to start with a tangible, measurable and simple project towards Advanced Analytics (i.e., a proof of concept). This allows the organization to learn and to grasp the value that Advanced Analytics can bring. As the organization gains confidence, the organization can proceed in making a choice in technology, mature in smartness, running more use-cases. Along the full learning and implementation cycle, element61 can help.