Streamlining the Process of Digital Transformation
Splice Machine Use Cases: Intelligent Industrial Applications
Next-Best Customer Action
Optimizing marketing for acquiring and retaining customers – putting the right message, offer, or service in front of the right customer at the right time.
Some prospects are likely in-market, what can you say to them across every channel in a consistent way to get them to convert into a buying motion. Some customers are likely to churn. What can you do to keep them?
Customer-Assisted Selling and Product Configuration
Once the customer is in a buying motion, how can you get them to complete an order? Configuration can be complex with many options and choices in the buying process.
Using machine learning, you can present the most likely packages that are pre-configured to the customer leading to higher revenues.
Today, forecasting and demand planning models are increasing in granularity, requiring more complexity and real-time planning.
Instead of relying only on statistical projections of past history, companies are factoring in a variety of new variables.
With Splice Machine, your applications will be able to consider micro-local and in-the-moment information such as weather, local news and events, traffic, and competitive data.
What happens when the customer asks the salesperson ‘Can you get this complex order to me by this date?’ In most companies, salespeople are relegated to saying, ‘I will go check with order planning and get back to you.’ giving the customer a window to get more quotes.
This order promising problem involves rapid order entry with a request date (or more than one request date for more complex orders), as well as shipping rules for how to group the items. Then, the system calculates whole order available-to-promise (ATP) as well as ATP by line item. This gives the sales person instant insight and negotiating room to commit a large portion of the order to meet the customer’s request date(s). Without this capability, companies rely on excessive safety stock to buffer demand tying up working capital.
Sales and operations people would love to have a crystal ball that can predict what will go wrong in the supply chain.
Now, they can use artificial intelligence to see around corners by developing machine learning models that predict events requiring changes to the plan and then use those predictions in order to proactively promise orders and schedule shipments against the predictions.
Preventative Maintenance and Spare Part ATP
As companies commit to SLAs and warranties on their products, they are instrumenting products with sensors that can monitor the products’ health. Additionally, as manufacturing and distribution becomes more complex and schedules become tight, monitoring the health and location of assets become key enablers. Any outage can ripple chaos through the supply chain, creating late orders, or requiring excessive safety stock.
With preventive maintenance using machine learning, companies can predict outages of components in both their own product in-situ at the customer as well as in their internal assets and perform real-time ATP checks to order spare parts and dispatch work orders to replace parts without experiencing serious outages.