Last mile delivery is a complex business with low margins. Be it food, grocery distribution to door steps or parcel/courier delivery from hubs to door steps or daily replenishment of super markets from warehouses. This operations become more complicated if we consider time windows for delivery, truck size restrictions in some neighborhoods, driver shifts and break regulations, SLA, fixed and variable costs of trucks etc. In mathematical terms, the above problem is NP-Hard. SCMworx implements our partner’s cloud based solution which is developed using Meta Heuristics (Simulated Annealing and Tabu Search) and Local Search techniques. This solution produces high quality solutions compared with simple heuristics like Clarke and Wright and is very scalable compared to models developed in mixed integer programming.
A common problem for large process based manufacturing organizations with geographically wide distribution is the need to rationalize economy of scale in production with a large and complex supply chain network. There is a trade-off between economy of scale achieved through increased production at a more distant site versus additional transportation costs involved. Most industries face conflicting pressures to take advantage of significant production economy of scale on the one hand and be customer responsive through fast and lean distribution system on the other hand. SCMworx experienced consultants backed with complex mathematical models based on Operations Research can help these organizations evaluate and finalize different strategic decisions like facility locations, line (technology) selection at each facility and tactical decisions like product mix on each line, quantity to be supplied from each plant to depot and depot to market. Candidate options of plants and depots, annual demand for different products at different demand centers, fixed and variable costs, transportation costs, taxes are some of the inputs required for analysis.
Most of the warehouses face issues in managing inbound and outbound logistics at their warehouses resulting in huge delays in operations. Problems like allocation of docks to inbound and outbound trucks, capacity constraints of docks, and material handling equipment, loading and unloading of trucks, scheduling trucks based on the demand and dock availability, material availability are making the planning process very complex and difficult to manage manually. Dock scheduling solution addresses all the above problems using constraint programming techniques. This solution adheres to all the constraints and provides a feasible dock schedule, loading and unloading schedule of trucks, and truck dispatch schedule by optimally using the dock space and material handling equipment. ROI will be through effective utilization of dock space, material handling resource capacities, reduction in delays, on time deliveries resulting in overall improvement in warehouse operational efficiency.
Demand planning across an organization requires marshalling a wide range of data like historical sales data, promotions data and data for cause and effect analysis. In order to make the forecast realistic and practical, planners tweak the forecast numbers with their judgemental forecast. Most of this data reside in spreadsheets and therefore building a consensus with multiple stakeholders across multiple geographies holding on to multiple versions of the data becomes a very complex, error prone and time consuming activity. Moreover, rapid introduction of new products calls for a new approach to forecasting for New products. SCMworx offers a cloud based demand planning platform that addresses all these issues. The cloud based Demand Planning systems gives a single shot view of data to all the stakeholders and thereby can overcome data visibility limitations. By using industry standard forecasting algorithms, it also improves the forecasting accuracy drastically by finding hidden patterns in the data. The solution can also aggregate and disaggregate the data at multiple levels so that the forecasting can be done at the required planning level and then passed on to the downstream applications at an execution level. Industry standard algorithms for New products also helps in forecasting for products with no sales history. Promotions that can change the forecast numbers can also be planned and impact of the promotions can be embedded in the final forecast. Additional qualitative and quantitative information about the sentiments of the products and the competitors products in the social media also helps in tweaking the judgemental forecast numbers and helps in taking informed decisions.