Supply Chain Analytics

Accurate forecasting enhances route optimization for order pick-ups for waste tyre management company

Business Outcomes

50%

increase in order pick up efficiency

20%

New customer acquisitions

80%

Decrease in customer complaints

The client is a global waste tyre management company that focusses on collection of scrap tyres in huge stocks from small vendors and manage its disposal either by re-use, re-cycle or recovery processes.

Business Need

They relied on a manual system for pick-up and disposal of tyres that was time-consuming, erroneous and cost-heavy.

Limited access to historical data led to inaccurate trend analysis or demand fluctuations. Information gaps on truck availability, stock, customer attrition, growth in the service area, competition, etc. affected the overall process efficiency. Consolidation of data captured through varied sources spread across multiple systems was another challenge.

Without an integrated view of the entire supply chain, the company was unable to build a brand loyalty and optimize cost. They partnered with Hitech Analytics to develop an intelligent route optimization based on order forecasting to:

Replace existing manual forecasting and bring down error rate from +/-50% to +/- 10%.
Serve increased clientele within the same fuel cost.

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Solution

Hitech Analytics team designed a route optimization solution with the help of accurate order forecasting to streamline operations, consisting of:

AI-based route optimization application – Automated extraction of data on clients to be served, schedule of the pick-up, the volume of tyres to be collected, and the best possible route to avoid any bottlenecks. The application identified the firm’s physical locations using geospatial data and recommended the best route.
Interactive user interface – Tyre volume for each client along with any changes in the data was recorded daily.
Intuitive dashboard – Showcased KPIs such as tyre volume and its growth across clients, geographies, client segment, time-period, etc. along with actionable insights such as any patterns, trends and deviations, etc., and alerts based on pre-specified criteria.

Approach

Hitech Analytics team took the following steps to address data quality, quantity, and consolidation issues:

Identify and consolidate sources of information on the number of vehicles and their growth that helped with sales and marketing initiatives.
Developed custom tools to automate the process of integrating data sources.
Employed extensive data cleansing to ensure accuracy of data feeding in the machine learning algorithms
Selected machine learning and deep learning methodologies using Neural Networks to enable estimating order frequency and volume at the individual client level. This involved simulating an exceptionally substantial number of iterations (somewhere around 10,000+) across a different set of advanced ML methodologies (Neural Networks) and different parameters to achieve a satisfactory level of accuracy (+/- 10% average error)

Results

Improved supply chain efficiencies and ensured delivery profitability.
Boosted data-driven decision making and increased productivity.
Predicted demand accurately to optimize cost.
Enhanced logistics management and seamlessly direct trucks to locations with high demand.
Upgraded customer service standards and saved on penalties.

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