Use the Power of Machine Learning to
Kick-Start Digital Transformation
Companies in the Consumer Products industry are striving to adopt digital technologies to respond to increased competition and sector disruption, however, these efforts are hindered by legacy processes, and applications. Industry’s mission-critical and purpose-built applications are built on older platforms and lack agility and intelligence.
Industry Challenges
Excessive Data
Consumer Products manufacturers fail to gain insights from their data.
Disjointed Systems
Duct-taping compute engines together results in a complex architecture that requires excessive data movement.
Inability to Analyze Data
Companies lack infrastructure and technical resources to perform in-the-moment analysis to drive intelligent actions.
These challenges ultimately lead to poor predictions on stale data caused by the data movement between the duct-taped compute engines. As a result, Consumer Product companies fail to offer personalized products, make relevant real-time online product recommendations, and provide delightful customer experience.
The Benefits of Modernized Applications
Improve the Customer Experience
Leverage petabytes of data to deliver accurate online product recommendations.
Optimize the Supply Chain by Factoring Key Data Points
Account for the time of day, local events, weather, current traffic and customer preferences.
Inject Machine Learning and Leverage Additional Data
Improve forecast accuracy, increase inventory visibility and make better recommendations.
Modernized Consumer Product Application Examples
Splice Machine leverages the power of scale-out architecture and machine learning in consumer and retail industries across a variety of use cases.
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 across every channel to get them to convert into a buying motion.
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.
Demand Planning
Today forecasting and demand planning models are increasing in granularity requiring more complexity and real-time planning. Instead of just using statistical projections of past history.
Imagine how effective your demand planning can be if you can incorporate micro, local and in-the-moment information such as weather, local news and events, traffic, and competitive data as inputs to your demand plan.
Order Promising
What happens when the customer asks the salesperson ‘Can you get this complex order to me by this date?’ In most companies – even today with the best ERP system out there – salespeople are relegated to saying, ‘I will go check with order planning and get back to you.’ That’s an opportunity for the customer to go somewhere else and get a competing quote on the order — after all, they have to wait, anyway.
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 salesperson 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.
Order Predictions
What happens when the customer asks the salesperson ‘Can you get this complex order to me by this date?’ In most companies – even today with the best ERP system out there – salespeople are relegated to saying, ‘I will go check with order planning and get back to you.’ That’s an opportunity for the customer to go somewhere else and get a competing quote on the order — after all, they have to wait, anyway.
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 salesperson 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.
The Solution: Modernize Consumer Product Applications
Splice Machine can take you on a journey of scaling your application on a new scale-out SQL architecture, unifying analytics into one platform and injecting machine learning directly onto this app platform. Our process can also get you there faster – in months – without needing to hire infrastructure experts.