How AI can Lift Productivity of Businesses

September 18, 2019 Prima Saraiya, VOLANSYS

With recent innovations in data storage, analysis and cloud computing, Artificial Intelligence is improving efficiency and performance in businesses. As per the Research and Markets report, the AI market is projected to grow at a CAGR (Compound Annual Growth Rate) of 52 percent till 2025. It helps entrepreneurs make more informed business-related decisions. AI has the power to increase productivity of a business by integrating information and communication systems, delivering intelligent machine automation, providing early warnings of potential problems and much more.       

Here are a few ways business owners can raise their sales and productivity using Artificial Intelligence (AI) and machine Learning (ML).

Predictive Maintenance

Previously, the approach to asset (equipment/appliances) maintenance was tedious and reactive. However, today with Artificial Intelligence and Machine Learning in place, organizations have started investing in predictive maintenance solutions that improve operating efficiency. Predictive maintenance uses sensors to track the conditions of equipment and analyses data on an ongoing basis, enabling organizations to service equipment when they actually need it instead of at scheduled service times, thereby minimizing downtime.

Machines can even be set up to assess self-conditions, order required replacement parts and schedule a technician when needed. Taking predictive maintenance even further, algorithms based on big data can be utilized to predict future failures. Thus, it can be said that AI-enhanced predictive maintenance of industrial instruments/machines can reduce annual maintenance costs, downtime and inspection costs. As per an A.T. Kearney survey in Industry Week of 558 companies using predictive maintenance, an average of 20.1 percent reduction in equipment downtime was observed.

Accurate Demand Forecasting

In Supply Chain, demand forecasting is a fundamental tool for order planning and general strategy. It aids inventory supervisors in planning monthly orders, understanding seasonal trends, saving time on re-ordering and diminishing Stock-Outs. Technology learns from the past and can evaluate the complex factors involved in demand forecasting like market and economic forces, latest trends, etc.

This enhances forecast accuracy and eliminates guesswork. AI enabled forecasting ensures product availability, at the same time reducing inventory pile-up. It helps businesses understand their customer purchasing pattern in detail. Using this market-basket analysis, sales teams are also able to create demand for other products.

Not only does this improve the demand forecast, it can help the organizations generate business. It is similar to consumer e-commerce websites, such as Amazon recommending products based on browsing or buying patterns. Recently, a retailer of bikes and kayaks with 600 stores reduced their inventory requirements by 7 percent and inventory carrying cost by 4.5 percent using AI enabled demand forecasting tools.

AI can also increase efficiencies of tasks as simple as taking physical inventory. A task that may take days to complete can be completed in 24 hours using camera enabled drones that fly through the warehouse, scan and check for misplaced items. Thus, using AI and ML, systems can test multiple mathematical models of demand, production, outcomes with precision and also place material procurement orders based on this analysis.

 

Personalized Offerings

Today, consumers prefer customized, personalized, and unique products/services over standardized ones. Developments in AI and software intelligence are enabling businesses to take personalization to the next level by building services and products that are highly pertinent to the individual consumer’s needs.

Such businesses have an opportunity to create differentiated propositions that may command a price premium, improve consumer traffic and conversion. A recent consumer survey by Deloitte showed that 20 percent of consumers are willing to pay a 20 percent premium for personalized products or services. This also enables brands to build greater trust with their customers via personalized offerings. Eighty-three percent of consumers in both the U.S. and U.K. accept that trusted retailers use their personal information in order to receive tailored products, offers and recommendations.

Optimizing Processes

Machines powered by Artificial Intelligence (AI) engines running Machine Learning Algorithms are capable of improving manufacturing processes efficiently. AI systems monitor parameters like cycle time, temperatures, quantities used, lead time, down time and errors to optimize manufacturing/production of a business.

Analyzing sales calls is one of the crucial aspects of businesses. In the past, it was a manual process, but now, AI conversational tools are automating the entire process, thus reducing efforts and saving time. Such tools record each outbound call and pick up cues that identify how the call went. Machine learning algorithms also quell business fraud by spotting anomalies and discrepancies in day-to-day data exchange processes.

Thus, machine based neural networks can understand large volumes of data in seconds, providing perfect solutions at the fingertips of decision makers, boosting efficiency and business sales. It offers real-time insights that can be applied to rapidly changing business environments. 

Prima Saraiya is associated with VOLANSYS as a Senior Marketing Executive with proven experience in digital marketing, lead generation, collateral development, brand promotions, partner relationships, and events management.  

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