Anyone who has worked or working in the supply chain, they know what I am talking about. Planning and procurement were never easy and in the COVID world, it’s one of the hardest with the wild swing in commodity pricing, unpredictable consumer behavior, and ongoing geopolitical tensions such as deglobalization.
If you are using any forecasting no matter what, everything is now off the table because all machine learning and forecasting solutions heavily rely on past data, and you don’t have any past data for such a situation.
Let’s do a little touch up the current situation before talking about how we can fix it using specialized data engineering.
This crisis is unique by itself, the global supply chain is now facing the following challenges.
Everyone knows god made the world rest all China, which means China is the world’s factory where pretty much everything is made or it supplies critical components to some other country before it becomes a finished product. For example, 70% of Indian medicine raw material comes to China. In the US, from iPhone to dollarama everything comes from China. Once factories are shut down you can’t make most goods.
The US is the world’s largest importer. Pretty much half of the world makes their living by selling goods & services to US consumers. Once the US gets shut down due to the virus it will send a ripple effect which will be far more reaching then China’s shutdown. China’s shutdown only directly impacts raw material (resource) countries such as Canada, Saudi, Russia, but the US shutdown will dramatically impact the rest of the world including India, Africa, Asia, and China too.
Wild moves in Commodity prices
This is another beast, which will cause more pain than anything else. For any planning, procurement person forecasting the future price and based on that designing the whole supply chain is a day to day work. Owing to the aforesaid mentioned supply and demand shock, coupled with totally unpredictable consumer behavior is causing have to commodity pricing. For example, locking -$40 US oil could seem like madness considering if you take the delivery, you have to pay for storage. That makes life more complicated with all future based commodity pricing model.
Now when we are talking about commodities, we are talking about all metals, etc, which constitute nails to cars and locomotives. Even little miscalculation could lead to massive swing ( both up/down ) for a manufacturer.
Impulse Analytics is for rescue
It is important to know that even though no one could have predicted an event like COVID-19 but what is important for the businesses is to be able to forecast what will happen in the future and enable them to take control of the future demand surge or the plateaued demand that they may see for short/longer terms. This will allow the businesses to meet up the pent up demand once the market opens up again and the situation returns to normalcy and at the same time allow to stop the hoarding mentality that can lead to overpriced inventory ordering to meet the expected demand surge which may not come within the expected time.
In data science, we call it to impulse analysis. At beCloudReady we have a sophisticated machine learning platform called SKUCaster which can incorporate a shock on demand & supply over the expected sales in the coming months. The platform can incorporate the impulse due to the temporary halt in the supply and demand cycles. Impulse analytics looks to make up the difference between what was expected and what the actual demand is and then tries to make up for the pent up demand that will inevitably come back when normalcy returns. However, this is not to say that the future demand is simply going to be the difference between the projected demand and the actual demand ruled over the future periods it is much more complicated than that.
In statistical terms, the impulse response is the derivative with respect to the shocks. beCloudReady SKUCaster product uses the power of machine learning and AI models and employs methods like vector autoregressive models, LSTM models, and neural network models. The main purpose is to describe the distribution of the explanatory variables which ultimately impact the demand in reaction to a shock in one or more variables. In this case, it will be literally be all the variables that are related to the end customer demand and the incoming supply variables. What we’re essentially looking at is to be able to trace the shock and its transmission over time. Even in this time of unpredictable demand the businesses still have to plan about the future and protect their customer base so that they don’t have to face the lost sales due to miscalculated inventory or carry huge inventory costs because they did not assess the impact of the shock and expected the demand to return to normalcy faster than it did.
Just plain inventory management in the warehouses is one thing but we also need to plan and book trucking companies, manpower, etc so that we can supply the goods to the end customer. That means we forecast now and order as of yesterday if we do not want to survive now but go out of business when the things are returning to normal.
beCloudReady SKUCaster can actually forecast the demand by accounting for the blip in demand now and the expected surge in the demand later that too within a reasonable confidence interval.
Machine learning and AI models allow beCloudReady SKUCaster to look for both the Generalised impulse responses as well as the temporal impulse responses. As we can see there is also the need to separate out the impulse by SKU categories and geographies as the demand for necessary parts and items will be more compared to optional parts and items. SKUCaster first segments the SKUs into different groups and one of the most important variables as of now is the sensitivity of the demand to the shock which can be measured and given a weightage in the segmentation. This means that the SKUCaster will give a higher forecast to a highly sensitive and necessary SKU compared to the SKUs which will move slowly.