Every Product Manager in the World spend a lot of time and resources keeping the company Product Catalog up to date. The Machine Learning Supply Chain module of our Mash’n Learn project tackles all aspect of gathering data from the internet to enrich product data sheet.
Enhancing Product Catalog for Supply Chain
Improving Product data to fit GS1 standard is the first value-added component of a good Machine Learning tool, but LR Physics goes further with using fractal chaos to establish Demand data for a better forecasting.
What makes forecasting demand so challenging? Rather than appearing as a logical series of numbers, in today’s business environment demand more often seems like a pattern of partially constrained chaos. Demand is increasingly influenced by multiple internal and external factors that drive it up and down in ways that can’t be understood by simply looking at a historical time-series of aggregated demand buckets. Instead, demand should be viewed as being driven by a complex series of indicators that can be nearly impossible to manage with traditional forecasting algorithms.
Our Machine Learning Supply Chain module can help companies address demand-forecasting challenges by reliably modeling the numerous causes of demand variation. Machine learning is a computer-based discipline in which algorithms can actually “learn” from the data. Rather than following only explicitly programmed instructions, these algorithms use data to build and constantly refine a model to make predictions. We currently use IBM Watson APIs and in house built algorithms to process big chuncks of data.
There are numerous source of biases in Demand data
- Lots of promotions
- Lots of new products
- Lots of “long-tail” demand
- Growing complexity
- Extreme seasonality
- Just too much data
You get it; most of the errors are therefore coming from external factors.
How could you benefit from machine learning?
One way to know is by finding out whether your old planning system may be causing escalating costs. Here are three potential signs of this problem, and how machine learning can help to address them:
Too much stock
You can’t trust your safety-stock levels to deliver the required service levels, so you keep them artificially high. By taking more demand variables into account, machine learning can help companies with a diverse range of SKU profiles, including long-tail items, to set optimal, lower levels they can trust.
Too much work on planners
Your team is spending too much time manually adjusting and evaluating forecasts, and often is still not able to deliver them accurately enough or on time. This leads to poor productivity and morale. Machine learning takes more demand variables into account and weights each according to its significance, resulting in much more accurate forecasts. This helps planners succeed in their roles and frees up time for them to refine forecasts using their personal insights and business knowledge.
Demand consensus are wrong
Your consensus forecast from the S&OP is unreliable, or the collaboration process behind it is too slow to adapt to the dynamic nature of the market and SKU behavior. Machine learning’s high level of automation can improve the quality of the short- and mid-term forecast by picking up key trends from transactional and promotional data and providing actionable insights about those trends, thereby making the S&OP process more efficient and effective in achieving your business objectives.
Enhancing Product Catalog for Digital Marketing & Advertising
On the top of the Supply Chain components, we are also making sure our algorithms are providing generated feeds for all major Advertising and Product Listing platforms. This component is keen to LR Physics Supply Chain expert Stephan Pire who always find himself working out in enterpise environments where Marketing is completely disconnected from Supply Chain.
Mash’n Learn Machine Learning Supply Chain module is ready for your ERP
Anyway, please contact us at LR-Physics for any need for Machine Learning Supply Chain applications. We will come back to you real quick. We will be listening to your Supply problems and giving you opportunities to fix them in a fast implementation using our module.
We are already integrated with SAP, JD Edwards, Demantra and Tryton. Since we follow industry forecast guidelines, we are able to integrate with any serious Enterprise Forecasting tool.
Check out Mash’n Learn presentation on SlideShare
We build integrated Data Science models involving algorithms scraping the internet for sensible commercial data. With those data, we build analytics and predictive forecasts to help retailers making the right investment decision. Our tools is deployed at 7 retailers (as of July 2016) and 1 energy provider.
What to read on Machine Learning?
So you can check out our Library about the Blockchain topic or check out those books: