Maximal order fulfilment has been the coveted dream for businesses worldwide for quite some time now. However, improving order fulfilment has become increasingly difficult to realize. Only one in three manufacturing companies are confident about their order fulfilment capabilities, while many high-tech companies are unable to meet their allocation commitments, failing nearly 50% of the time.

Such statistics point to the high variability of supply and demand across industries in the supply chain space. Complex market dynamics of today are affecting businesses in ways that they are currently unequipped to handle. Events ranging from constricting delivery windows to uncertain demand are forcing companies to push for high spending through greater resource and labour allocations. Even the most harmonized order fulfilment strategies are prone to fail in the event of sudden order surges or product recalls.

A close look at the challenges facing manufacturing industries today suggests that handling variable and complex order specifications in full and meeting delivery timelines on an ongoing basis are common pain points.

Dispatch- and Transportation- linked Challenges

In any supply chain, it is impossible for global production to freely respond to the demand. This is because manufacturing is limited by a set of constraints. Consequently, there is limited inventory existing at a time in production or across the different warehouses of a business. The function of dispatch is to create the flow of stock for a set of orders such that the maximum fulfilment of orders is possible.

However, harmonizing this flow for optimal value can be challenging.

a. Challenges in stock allocation. Inventory may not always exist where the demand is. This is especially true for an organization offering high product variety with multiple variants, colours, and other specifications. For such an organization, especially as the geographical spread of the consumer base widens, it is difficult to plan the inventory at each stock point in a way that addresses the consumer demand completely. This situation can be best exemplified for a diverse nation such as India where consumer behaviour and preferences for colours, materials, and products vary as per the economics, geography, and culture of a region. Ensuring the availability of different model specifications in the right quantity, then, becomes extremely difficult.For businesses that deal with high order volumes, failure to meet the orders in the desired mix and the right quantity often leads to large backorders and subsequent ramp-up of production to meet these orders. High-frequency occurrence of such events can lead to maxing out of resources and create a significant cost burden on the business.

b. Transportation Challenges. For the orders that can be captured with the available inventory, the next biggest challenges are the adherence of SLA/ETA commitments. Dispatches need to be scheduled based on the fleet size and loading capacity of vehicles with the right selection of routes.

1. 1. 1. The fleet size – Dispatches need to be planned day to day with the right visibility of the available carriers such that vehicle waiting time is minimized and there are no SLA and ETA breaches.
      2. The vehicle loading capacity – Every carrier has a limited stock carrying capacity. Dispatch can be done to effect optimal volumetric packing. However, for fragile products or products with greater risk of damage during transit, there could be unique loading restrictions. Within these constraints, dispatch must be planned with the aim to maximally utilize the loading space.
      3. Vehicle Routing – For meeting ETAs, routes are planned based on several factors, such as road conditions, fuel consumption, or the distance of transit. Although dispatches may be planned such that transit time and distance are reduced, scenarios just as stock unavailability can require longer transit routes to be planned. Such exception handling needs to be part of the order fulfilment process to make planning more reactive to consumer demand.

Optimization for Order Fulfilment – The Solution for Maximum Order Fulfilment

To override these challenges, it is necessary to automate dispatch and transportation planning and deploy an optimization solution that can integrate the different dispatch and transportation criteria and objectives. The resultant optimal plans are then derived from the given set of variables and constraints.

A. Optimization of Dispatch Allocation planning

A wide geographical spread of distribution, a large customer base or dealer network, the plurality of product types and specifications, diverse order destinations are all factors that make the realization of optimal stock allocation a difficult human endeavour. This is especially true when maximum order fulfilment is an essential outcome of the process.

An important solution in this direction and one with an immense potential to override the challenges to order fulfilment is AI-driven planning.

AI-driven planning

Recent advances in AI/ML technologies has enabled the attainment of high optimality for high complexity of data. Deep learning algorithms, which are inspired by the neural architecture of the brain, operates on massive data sets. When applied to the environment of dispatch, dispatch plans are generated with the maximum possible order fulfilment.

Dispatch optimization systems, such as Verdis Dispatch Allocation and Planning (DAP), use machine learning algorithms to create optimal stock allocation plans that achieve a 100% order fulfilment. This is possible through the analysis of the complete solution space of dispatch – a space made of millions of data points. Possible solutions are simultaneously compared with each other to determine the most optimal solution. This solution is developed within a short processing window, making planning fast and efficient.

Incorporating higher order processes. Besides planning being guided by technology, the incorporation of higher order processes can improve the efficiency of dispatch and, therefore, of order fulfilment. For instance, Verdis uses the twin strategies of club loading and stock transfer.

• Club Loading. When order fulfilment is impeded by insufficient stock existing at a single stock point, dispatches can be planned from other locations through a combination of stock.
• Stock Transfer. For fulfilment of orders, stock can also be transferred from different stock points to a specific stock point such that orders arising from locations with proximity to the latter are fulfilled. Besides stock transference, when the fulfilment of an order requires a combination of inventories existing in different warehouses, the stock from these can be clubbed for order fulfilment.

B. Optimization of Transportation Planning

Underutilization of fleet and carrier space is a wasted resource, not just in terms of revenue but also efficiency. For efficient transportation planning, the constraints of fleet size and vehicle space need to be factored in besides the requirements of freight costs reduction and SLA adherence.

The remedies to such issues lie in optimization.

• Through optimized planning, it is possible to optimally utilize the available carriers and space to meet delivery timelines and lower total freight costs.
  To ensure this-
  • Orders can be clubbed based on SLAs, destinations, or cost considerations.
  • Optimization solutions can simulate multiple loading scenarios at a time to determine the plan that offers maximum space utilization and satisfies the constraints of loading.
  • Further, vehicle loading can be effected at par with a machine-generated loading sequence plan which minimizes loading and unloading time.

Such plans are safe from most instances of errors, and, therefore, damages or unwarranted delays are less likely to occur.

Products like the Verdis Transportation module ensures SLA adherence and lower freight costs through order consolidation. For instance, orders with similar SLA are clubbed in a single carrier. This serves to minimize the number of dispatches, therefore effectively, ensuring lower costs and greater availability of carriers at a time for scheduling.

• The implementation of route planning and optimization ensures greater efficiency of dispatches and lead time constrictions through real-time visibility of dispatch and vehicle status.
  • Route planning automation can ensure the fulfilment of orders irrespective of supply chain inefficiencies. For instance, when orders from geographies with greater lead times, such as from hilly areas arise, or when orders require fulfilment from distantly located stock points, route planning software can offer recommendations for cost-effective transit routes. Dispatchers can choose to accept or reject such recommendations based on cost-benefit evaluations.
  • With route optimization, the selection of routes is based on lead times and cost considerations. Such optimization ensures faster order fulfilment. Real-time variables, such as traffic conditions, geography, as well as the availability of personnel for loading or unloading are factored in while generating the plan outputs.

 Conclusion

Although prevailing market conditions create enormous complexity in order fulfilment and management, there exist enormous opportunities in the sphere of information technology, particularly with AI/ML-based solutions that are designed towards attaining supply chain advantages. With such tools, organizations can strive to improve their competitive positioning. One such opportunity lies in the area of optimization. With optimization, businesses can fulfil complex orders and reduce instances of error and inefficiencies of planning. This helps to ensure greater and faster order fulfilment and, in turn, leads to enhanced customer service levels.