Supply Chain Problems Assignment: Autoliv and Forecasting

This assignment delves into supply chain management, addressing two key problems. The first problem analyzes the 'Lean Systems at Autoliv' case, examining how Autoliv minimizes waste through lean principles like increased traceability and reduced inappropriate processing and overproduction. The solution discusses three specific considerations related to Autoliv's manufacturing environment and connects them to the eight types of waste. The second problem tackles the challenges of forecasting, particularly for seasonal and fashionable products. It explores the difficulties in forecasting at Deckers, emphasizing the role of data availability and asymmetry. The solution proposes using machine learning and artificial intelligence for demand prediction in the absence of historical data, highlighting the importance of end-to-end supply chain perspective and inventory management strategies like targeted inventory and inventory clearance.

Running head: SUPPLY CHAIN PROBLEMS
Supply Chain Problems
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SUPPLY CHAIN PROBLEMS 2
Problem 2
A closer look at the case shows that Autocliv is a perfect example of companies that have
successfully implemented lean supply chain. Autocliv case clearly justify the fact that the
success or failure of an enterprise depends to a large extent on whether its business management
model is consistent with the customer-centric economic environment. Lean production is a
method that enables companies to achieve this advanced management model, both within the
company and throughout the supply chain.
First, by increasing traceability and visibility, Autocliv minimize waiting waste. At
Autocliv, the parts are always in a non-stagnation, non-stacking, and not exceeding, one-by-one
flow production method. First, there is smooth transition from one process to another. Second,
only qualified products are allowed to flow to the next process (Tompkins, 2014). .
Second, through lean system, Autocliv is able to reduce inappropriate processing.
Autocliv re-observe the operations performed based on previous experience, whether the
operations that are being performed are necessary, whether the procedures can be adjusted,
whether different types of tools can be unified, and whether general-purpose tools can use
special tools. Whether existing record forms are necessary and streamlined. Whether electronic
documents can be used, whether the existing item storage position and storage area can be
adjusted, and whether the height of the existing worktable can be adjusted. Whether the existing
pipeline can be shortened and adjusted (Singh, Garg & Sharma, 2011).
Third, by applying lean system, Autocliv reduces the waste related to overproduction. In
order to obtain profits by reducing costs, Autocliv work hard on materials, products, finished
product quantities, labor costs, equipment purchase and management costs, and eliminate all
kinds of waste. Generally, Autocliv is able to realize zero inventory or minimize inventory
through kanban production and lean procurement , and quickly respond to market changes.
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Problem 4
a) What factors make forecasting at Deckers particularly challenging?
The availability of data and tools for forecasting makes forecasting at Deckers challenging. The
supply data is asymmetrical. Faced with complex supply chain issues, practitioners risk
confusing purposes and means. For example, when a replenishment is required, determining the
weekly demand forecast is needed for a numerical formula used to calculate reorder quantities.
Weekly forecasts are only intermediate calculations, and order quantity is the final decision. It is
a fallacy to think that optimizing numerical components based on arbitrary mathematical
measures (such as demand forecasts optimized based on WMAPE (Weighted Average Absolute
Percent Error)) will mechanically generate financial returns in some way. Although this may
seem counter-intuitive in the supply chain, this is often not the case. Supply chain issues are
generally highly asymmetric (Özdemir, Simonetti & Jannelli, 2015).
b) How can forecasts be made for seasonal, fashionable products for which there is no
history file?
The forecast for products for which there is no history file can be made through machine
learning. Artificial intelligence-based demand prediction uses machine learning, and based on
this concept, machines can learn autonomously through the data we submit to the machine.
Artificial intelligence can infer and grasp the big picture through deep learning, so the system
can recommend to us what measures to take (Ntabe, et al 2015). Machine learning tells you why
something happened, and deep learning tells you how to respond. Therefore, a more intelligent
supply chain should no longer allow manual intervention by supply chain professionals. Manual
intervention may go wrong, and they should be allowed to engage in strategic work. Now,
predictions based on artificial intelligence and machine learning have become a reality for large
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SUPPLY CHAIN PROBLEMS 4
and small retailers, especially those grocery suppliers who need to buy extremely fresh foods and
prepared foods. An irrational inventory plan can severely impact retailer logistics, and multi-
channel retailing can complicate matters. Looking at the supply chain from an end-to-end
perspective is beneficial for timely replenishment of inventory and efficiency, and artificial
intelligence can provide this perspective by analyzing various demand patterns (Omogbai &
Salonitis, 2017).
It can also use inventory clearance. Through this, Deckers could decide whether the
currently held inventory products should be destroyed or sold through auxiliary channels (usually
high discounts). In fact, the backlog of inventory unnecessarily disrupts the warehouse, resulting
in higher costs than the economic value of the inventory itself. Depending on the vertical market,
inventory can be cleared through promotions, specialized channels or pure destruction
(Montgomery, Jennings & Pfund, 2011).
Deckers could also opt for targeted inventory. Here, it will need to make a trade-off
decision between labor costs associated with restocking operations and the negative impact of
virtual inventory on supply chain performance. In fact, public-facing retail stores have much
higher inaccuracies than warehouses or factories where employees are restricted.
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References
Montgomery, D. C., Jennings, C. L., & Pfund, M. E. (2011). Managing, controlling, and
improving quality. John Wiley & Sons New York, USA
Ntabe, E. N., LeBel, L., Munson, A. D., & Santa-Eulalia, L. A. (2015). A systematic literature
review of the supply chain operations reference (SCOR) model application with special
attention to environmental issues. International Journal of Production Economics, 169,
310-332. doi:10.1016/j.ijpe.2015.08.008
Omogbai, O., & Salonitis, K. (2017). The Implementation of 5S Lean Tool Using System
Dynamics Approach. Procedia CIRP, 60, 380-385. doi:10.1016/j.procir.2017.01.057
Özdemir, A. İ., Simonetti, B., & Jannelli, R. (2015). Determining critical success factors related
to the effect of supply chain integration and competition capabilities on business
performance. Quality & Quantity, 49(4), 1621-1632.
Robinson, S., Radnor, Z. J., Burgess, N., & Worthington, C. (2012). SimLean: Utilising
simulation in the implementation of lean in healthcare. European Journal of Operational
Research, 219(1), 188-197
Singh, B., Garg, S. K., & Sharma, S. K. (2011). Value stream mapping: literature review and
implications for Indian industry. The International Journal of Advanced Manufacturing
Technology, 53(5), 799-809.
Taylor, R. D. (2010). Exploring the impact of Lean design and Lean supply chain management
on an organization’s innovation capability. Minneapolis: University of Minnesota.
Tompkins, J. (2014). Applying lean methods to supply chains. Industrial Engineer, 46(4), 26.
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