The biggest challenge for organisations is to make the right decision based on the right and especially timely information. How do you use data analytics to improve your performance?
Trends
Big data is now seen as an important tool to stay competitive. The big problem for many organisations is analysing the information and turning it into a competitive advantage or reducing costs. As changes in the market are accelerating, historical data analysis is now obsolete. Organisations need better prediction and scenario techniques to achieve optimisation of, for instance, warehousing, transport but also product portfolio choices.
Need for integration
Omnichannel developments require more integration throughout the chain to achieve optimal fulfilment within each channel. Interaction between channels, cross-channelling, is inevitable. Supply chain towers are indispensable in this respect. Towers ensure integration of processes and technology with the aim of a streamlined flow from Procurement to customer regardless of the complexity, size or global span of the supply chain. Controlling this therefore impacts all players in the chain.
- eCommerce and multichannel or cross-channel demand affects 87 per cent of companies
- 65 per cent bypass their own DC and ship directly to the shop via third parties (suppliers, vendors, 3PLs, breakbulk)
- 61 per cent have models for direct-to-home delivery (this is already 30 per cent less than 2 years ago)
- Inbound-to-outbound segmentation and cost-to-serve (CTS) require higher levels of big data, collaboration and analytics than ever before. Even more rigorous approaches are needed to proactively implement item-level inventory optimisations, which only 23 per cent of poor performers can do
- Managing costs and rates by channel, mode, customer and product for proper omni-channel fulfilment is limited. Fragmented collaboration and data sharing limit transparency in developments and rates. In fact, linking these components from inbound to outbound is severely limited among top-performing companies; only about 35% of top performers can segment their logistics/transportation rates and costs.
Aberdeen research shows what is needed on the one hand and how companies are struggling with the issue and clearly showing declining results due to lack of transparency, collaboration and the right information.
Type of analysis
The proper and timely analysis of data is therefore a prerequisite for meeting the above challenges. This not only involves historical developments, but also trends and the development of forecasts and scenarios to provide insight into the consequences of decisions in case of changes.
· Descriptive. Understanding and analysing what has happened or is currently taking place. Often on historical figures. Often micro data is aggregated to macro level and incorporated into balance score cards or similar reports.
· Predictive. What might happen? Predictive analytics predicts future opportunities and trends and finds connections in data that are not immediately obvious with traditional or descriptive analytics.
· Prescriptive analysis. What should be done? Using data to prescribe the best course of action and increase the chances of achieving the optimal result. This is a forward-looking, future state or alternative (time/cost) path analysis, which provides forward-looking insights about the business. So predicting what is likely to happen and why it is likely to happen. Prescriptive analysis evaluates and defines a new way of working, sets business objectives and optimises and reduces bottlenecks, to ultimately achieve an optimal result in terms of cost and service.
Segment and optimise
To optimise your future supply chain and get from analysis to operational and financial results, there are a number of conditions you need to meet.
Determine what your current status is by business unit, product, channel and even processes. Here, determine operational costs, volumes of product flows from supplier to customer. In doing so, use descriptive analysis approach to determine your current situation. Also describe the conditions for optimisation in terms of service, cost, capacity and possible constraints, so that you lay the foundation for further optimisation.
Future. predictive analytics can arrive at scenario optimisations based on the current status. The preconditions you defined earlier are leading in this process. In doing so, have several scenarios calculated and put the results side by side. In doing so, do not be afraid that extreme scenarios will emerge.
Play the game. See what the effects are on the scenarios if you shift products, or choose a different channel. Change conditions from, say, 48-hour delivery to 24 hours. From complete deliveries to partial deliveries.
Engage the organisation. The many variants have an impact on day-to-day operations. Often, employees directly involved in operations see through certain (im)possibilities of scenarios. Their input is essential to arrive at a good set of conditions that determine your scenarios. Eventually, then, comes the point of prescriptive analysis and scenario: what should happen and what should definitely not happen?
Close the loop
Use the newly developed scenario or scenarios as a benchmark for your daily planning and execution. Is there a fit between reality and the scenario? Where are the deviations and what causes them. You use this information to further optimise your scenarios and discover and anticipate trend breaks in time.
