Supply chain network
A Supply Chain Network (SCN) is an evolution of the basic supply chain. Due to rapid technological advancement, organisations with a basic supply chain can develop this chain into a more complex structure involving a higher level of interdependence and connectivity between more organisations, this constitutes a supply chain network.
Businesses are often part of a larger network of organisations, a supply chain network can be used to highlight interactions between organisations; they can also be used to show the flow of information and materials across organisations. Supply chain networks are now more global than ever and are typically structured with five key areas: external suppliers, production centres, distribution centres (DCs), demand zones, and transportation assets
All organisations have or can purchase the components to build a supply chain network, it is the collection of physical locations, transportation vehicles and supporting systems through which the products and services firm markets are managed and ultimately delivered.
Physical locations included in a supply chain network can be manufacturing plants, storage warehouses, carrier crossdocks, major distribution centres, ports, intermodal terminals whether owned by a company, suppliers, a transport carrier, a third-party logistics provider, a retail store or an end customer. Transportation modes that operate within a supply chain network can include the many different types of trucks, trains for boxcar or intermodal unit movement, container ships or cargo planes.
There are many systems which can be utilised to manage and improve a supply chain network include Order Management Systems, Warehouse Management System, Transportation Management Systems, Strategic Logistics Modelling, Inventory Management Systems, Replenishment Systems, Supply Chain Visibility, Optimisation Tools and more. Emerging technologies and standards such as the RFID and the GS1 Global Standards are now making it possible to automate these Supply Chain Networks in a real time manner making them more efficient than the simple supply chain of the past.
Supply Chain Network Design
A supply chain network can be strategically designed in such a way as to reduce the cost of the supply chain; it has been suggested by experts that 80% of supply chain costs are determined by location of facilities and the flow of product between the facilities. Supply chain network design is sometimes referred to as 'Network Modelling', due to the fact a mathematical model can be created to optimise the supply chain network.
Companies have been led to modify their basic supply chain, investing in the tools and resources to develop an improved SCN design that takes into account taxation regulations, new entrants into their industry and availability of resources, has resulted in more complex network designs.
Designing a SCN involves creating a network that incorporates all the facilities, means of production, products, and transportation assets owned by the organisation or those not owned by the organisation but which immediately support the supply chain operations and product flow. The design should also include details of the number and location of facilities: plants, warehouses, and supplier base. Therefore, it can be said that a SCN design is the combination of nodes with capability and capacity, connected by lanes to help products move between facilities
There is no definitive way to design a SCN as the network footprint, the capability and capacity, and product flow—all intertwine and are interdependent. Following on from this, there is also no single optimal SCN design, in designing the network there is an apparent trade-off between responsiveness, risk tolerance and efficiency.
Reverse Supply Chain Network Design
A new requirement for 'reverse supply chain network design' has arisen from the environmental impact of end-of-life goods. This particular network design addresses logistical issues such as collection, processing and recycling of end-of-life goods. Companies that design both forward and reverse supply chain processes together, with recycling & disposal in mind, have been noted to have the greatest success. Through this, organisations can support goods from production to disposal creating a 'closed-loop system'.
Examples of reverse supply network design
Bosch is a company that capitalises on this closed-loop system by building sensors into their power tool motor. Bosch can quickly assess the state of a motor reducing the cost of inspection and disposal, thereby increasing their profit margin on refurbished power tools.
Supply Chain Network Risk Analysis
Though designing a supply chain network can cut costs within a company, it is important to note the supply chain is not static but rather a continually improving model and adapt in response. A key part of designing the Supply Chain Network is ensuring the network is versatile enough to cope with future uncertainties. Though there is inherent uncertainty about the future, a supply chain network risk analysis can be conducted; by using information available, the future business environment can be characterised.
The uncertainties associated with supply chain networks fall within two categories, Endogenous uncertainty and Exogenous uncertainty.
An uncertainty can be categorised as 'endogenous' when the origin of the risk is within the supply chain network itself, such as market volatility or technological turbulence.
An uncertainty can be categorised as 'exogenous' when the origin of the risk is external to the supply chain network. Exogenous uncertainties can be further categorised; ongoing risks such as economic volatility, can be described as a 'continuous risk'. 'Discrete' events refer to infrequent events that could disrupt the supply chain process, such as natural disasters.
By distinguishing between these types of uncertainty, an organisation can decide the best approach to risk management. A company has a very limited ability to prevent exogenous uncertainty. The risk to the supply chain network can be minimised by being well prepared for potential events. Endogenous uncertainty can be somewhat mitigated with precautions such as regular communication between an organisation and supplier.
Document automation in supply chain management & logistics
- Slack, Nigel; Chambers, Stuart; Johnston, Robert (2009-01-01). Operations and Process Management: Principles and Practice for Strategic Impact. Prentice Hall/Financial Times. ISBN 9780273718512.
- "What is a Supply Chain Network? - Logistics & Materials Handling Blog | Adaptalift Hyster". www.aalhysterforklifts.com.au. Retrieved 2015-10-31.
- Klibi, Walid; Martel, Alain (2012-12-16). "Scenario-based Supply Chain Network risk modeling". European Journal of Operational Research. 223 (3): 644–658. doi:10.1016/j.ejor.2012.06.027.
- Guhathakurta, Rahul. "Digital Supply Chain: Finding Fractions Between the Operational Digits". IndraStra Global. 003: 0013. ISSN 2381-3652.
- Watson, Michael; Lewis; Cacioppi; Jayaraman (2013). Supply Chain Network Design: Applying Optimization & Analytics to Global Supply Chain. USA.: Pearson Education, Inc. p. 1. ISBN 978-0-13-301737-3.
- "How to Reduce Costs through Supply Chain Network Optimization". www.industryweek.com. Retrieved 2015-11-05.
- "When is it a good time for supply chain network design?". www.opsrules.com. Retrieved 2015-11-01.
- Pishvaee, Mir Saman; Razmi, Jafar (2012-08-01). "Environmental supply chain network design using multi-objective fuzzy mathematical programming". Applied Mathematical Modelling. 36 (8): 3433–3446. doi:10.1016/j.apm.2011.10.007.
- "The Reverse Supply Chain". Harvard Business Review. Retrieved 2015-11-05.
- Pishvaee, M. S.; Torabi, S. A. (2010-10-16). "A possibilistic programming approach for closed-loop supply chain network design under uncertainty". Fuzzy Sets and Systems. Theme: Games, Optimization and Discrete Structures. 161 (20): 2668–2683. doi:10.1016/j.fss.2010.04.010.
- Trkman, Peter; McCormack, Kevin (2009-06-01). "Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk". International Journal of Production Economics. 119 (2): 247–258. doi:10.1016/j.ijpe.2009.03.002.