According to PwC Research, 72% of respondents stated that AI will be fundamental
to their business in the coming future; in fact, it will be a business advantage.
Visionary leaders believe that artificial intelligence (AI) will be crucial to providing businesses with a future edge. Therefore, every business leader, especially those interested in business fulfilment, should be focused on AI:
Why is AI’s role in fulfilment so crucial?
AI is being looked at primarily for predicting consumer behaviour, especially with respect to lateral buying patterns and product vs. consumer profile matching. However, AI can play a bigger role in the business.
It can help the algorithm to learn faster and look for patterns not visible to the sharpest human eye. As a result, it can increase business fulfilment, which is pure science. Unlike with branding or communication, nothing is subjective. Fulfilment has binary results – either the product is in stock or it is not; on time or not. That said, these binary results are affected by numerous micro-variables which may not be manually computable at any given point in time. Once AI algorithms learn more about these micro-variables, they can exceed the applications of operations research to make fulfilment a more efficient process. AI algorithms can identify demand before it arises, adjust fulfilment levels using big data analytics and maintain prices at the most effective rates possible to ensure the right balance between demand and profitability.
Below are ways in which AI will impact business fulfilment:
1. Storage forecasting and planning
The best way to monitor data has been with a reliance on spreadsheets. They excel at recording and analysing data, within the limits of human analytical and computational powers. When it comes to fulfilment and operations, there is simply too much data to be analysed using spreadsheets.
Businesses have been generating micro and macro data since the beginning of time. Macro data includes demand patterns, process timelines and warehouse locations. Micro data includes parameters like average delivery time between two points and weighted average weight of products with respect to demand and profitability. Micro data is simply a meaningful permutation and combination of various small parameters with an impact on the macro picture of the business. The problem is that while macro trends are easily visible, the micro data is too specific to appear on an analyst’s radar.
For this reason, AI has greater potential for analysing data. It can delve more deeply into the depths of data than a human can and pick up trends the human eye cannot. Thus, AI can help form better conclusions by using these trends and connecting them with the other business processes. These abilities, when paired with micro-analysis, can help empower managers to use AI in making fulfilment an efficient process.
2. 24/7 operations
When breaking down all the processes carried out by warehouses and storage facilities, they include goods placement, fulfilment levels and keeping storage in sync with demand cycles. These are all linear processes, run on predetermined rules which require a huge amount of automation. Most jobs which have been consumed by automation, were all very predictive and monotonous; yet the warehouses are highly-dependent on human operation to keep them running.
The problem with human operation is that it cannot work on same efficiency level all the time. When these processes are automated; however, the facilities can run 24/7. This decreases overhead, since the same resources used for more time also increase efficiency in order fulfilment because machines are less-prone to error. Also, since the processes are expedited, they give businesses a marketing edge due to decreased delivery times.
Running operations 24/7 at equal efficiency levels can harm a business if done only by humans because teams would need to be set up in shifts. Additionally, off-shore assistance would be needed, as well as the occasional outsource and finally, there would be added costs. But with AI taking over menial and repetitive tasks, most of the ground work is taken care of.
3. Efficient processes and order picking
Most people believe AI is merely a tool for analytics – but this is only half the picture. AI is about helping the algorithm learn by itself and as is well-known, algorithms can help with running both software and hardware.
The next level of efficiency is AI-integrated hardware. Hardware on the warehousing level consists only of the basic machines used for moving and placing boxes. AI elevates this by leveraging machine learning and neural networks. GreyOrange is a business creating warehouse-level robots using AI and machine learning. Their biggest advantage is accurately-predictable processing times & delivery schedules with next to no errors in product placement and storage. Human movers are prone to injury or risk handling damage.; This problem resolves itself when AI-powered machines are used for package handling. Boston Dynamics has garnered fame for showing precisely how robots can replicate human locomotion and when necessary, improve it.
4. Real time projection tracking
Current AI development levels, put businesses at the penultimate stage. Projections are not taken seriously because they are not accurate, and too many variables make processes unpredictable. People overlook to the process inconsistency and associated human element on all business levels. AI solves both these problems.
Businesses can predict optimal efficiency levels for each business fulfilment process using better analysis. And with AI on the hardware stage as well, these expected efficiency levels can be easily-met. Thus, processes become consistent leading to more- accurate projections.
According to published research, about a survey of 6,000 respondents, 38% of consumers believe that AI will soon improve customer service. This is where better projections are required – they can help businesses make promises that will stand the test of time. Ultimately, this will add to the brand’s equity because businesses will be able to provide the product when the customer needs it.
