In the post GST regime, leading automotive majors are remodeling their supply chain networks to meet the demands consistently by building an effective warehouse network. With GST phasing out all forms of indirect taxes, with one common tax to be paid at the origin for all shipments, the logistics sector has emerged as one of the key beneficiaries of the GST regime. At a higher level, phasing out of state-level taxes can lead to fewer transit delays and lead to efficient warehouse and yard management, and can also help the automotive industry expand their distribution footprint.
And automotive executives have been paying close attention to what the implications of GST might mean under the new environment, especially to the network optimization stage in which improvements in logistics management can make a positive business impact, increase profitability and enhance network asset utilization.
Data and analytics is coming handy to solve some of the toughest challenges in supply chain management. Analytics India Magazine caught up with Mahindra & Mahindra (M&M) to discuss how the world’s largest tractor maker is putting the network optimization in the fast lane in the post-GST era with data and analytics. M&M’s scale and foothold in the automotive business is well-known. Talking about one of their use cases in Logistics Freight Management, the M&M team discussed how the post-GST era demanded an improved stockyard network aimed towards building a one-market ecosystem, with minimum stockyards needed to fulfill the country demand keeping the appropriate dealer serviceability standards.
In this scenario, deploying analytics not only gives competitive advantage but also brings down the immediate transportation costs and capital expenditure costs in the long run. Another idea was to explore a synergistic model that would pare down costs and lend operational efficiency. “This would also provide an opportunity to create synergy by combining the stockyard locations of the three independent business units (Automotive Division, Farm Division and Swaraj Division) that would eventually reduce costs and drive operational efficiency,” the team said.
Using Linear Optimization Model for Logistics Freight Optimization
Linear Optimization models are represented by linear relationships or constraints and are used in major areas of decision-making such as in transportation, manufacturing, telecommunications, engineering design and financial services. Linear programming (LP) (also called linear optimization) is a special case of mathematical programming (mathematical optimization). At M&M, the challenge was to build an improved PLANT-RSO-Dealer network through linear optimization approach.
Through this optimization model, M&M identified the minimum number of RSOs required on the basis of demand-supply constraints and business feasibility – the bottom-line being dealer serviceability constraints. Based on this, the team built a cost curve from the freight costs, handling costs and total costs to identify the optimal number of locations. Freight Costs will go down with an increase in number of RSOs, while Handling costs will go up. By building the cost curve, the optimal number of locations with the best mix of Freight & Handling costs is obtained. By building the cost curve, the optimal number of locations with the best mix of freight and handling cost is obtained, said the team.
Using the minimum number of RSOs obtained, the team optimized transportation costs further by assigning dealers to specific Plant and RSO combinations, basis various business constraints. For example: Allocated demand at a plant, model should be equal to the production capacity of the model at that plant. Keeping dealer serviceability constraints intact.
Leveraging Big data open source platform Python, millions of combinations of scenarios were explored to arrive at the best solution. Talking about the model, the team further explained how many of the constraints were tweaked during the optimization exercise with feedback from logistics experts at M&M. For example, remote locations of few dealers led to sub-optimal solutions due to increased backward movement. Hence, due to business feasibility many such outlier treatments were made across parameters to arrive at optimal selection of Plant-RSO-Dealer network.
The analytical solution enabled the organization to draw out many insights on how to arrive at stockyard location & what mode of transportation : (Train, Trailer, Own power) to use.
Under the GST environment, up to 15 state taxes and tariffs are replaced with a single tax that will significantly improve the ease of doing business and give rise to a borderless market. And data and analytics can be instrumental in mapping the demand and supply clusters. Another major impact will be seen in enabling the set-up of an ideal network that has the optimum number, size and location of warehouses.
Mahindra and Mahindra incubated an internal advanced analytics (Information Insights Center-IIC) team at the beginning of 2016 to focus on advanced analytics solutions and to provide insights across various business functions. IIC worked on the GST logistics network optimization project with logistics experts from Automotive and Tractor business units to build the advanced analytics framework.