Paired Assignment Results
First is a bit of insight from one of our fellow students on their company's use of APP. Good stuff here, relate this to the Exam review and hopefully some lights should start turning on!
[In] relation the article described, my current employer uses a variation of aggregate production planning for inventory forecasting. We are a retail business that is directly impacted by variation in consumer demand. Being as how we are in the marine/boating industry, our demand is extremely seasonal. We have a 4 month window (April-July) in which we capture 80% of our total year sales. The seasonality of our business puts intense pressure on our procurement department for an accurate and adamant supply of key goods during our narrow sales window.
I am listing your article summaries here for the Mula, Poler, et al. article:
In Article 2, "Models for production planning under uncertainty", the author’s main purpose was to illustrate many theories and concepts available to maximize efficiency and accuracy of a production planning model. Uncertainty is categorized by Environmental and System uncertainty. Environmental uncertainty includes influences from outside of the production process such as demand and supply uncertainty. System uncertainly includes influences inside the production process such as production lead time, yield uncertainty, and production system failure.The production and inventory planning approaches described in the article vary by use and application, but share many similar characteristics. These common characteristics used in many of the production planning models described in the article are the use of yield factors, safety stock, use ratios, “fuzzy” variables, and lead-time variability. Determinants of a successful production planning model are to minimize excess inventory costs, minimize backordered costs, and increase product turns.
The purpose of this article is..."to gain a better understanding
of the ways of managing uncertainty in production planning, and to provide
a basis for future research.." Uncertainty is defined as ..."the
difference between the amount of information required to perform a task and
the amount
of information already possessed." In order to be more efficient, there
has been research on uncertainty in order to become more productive. Even with
the best of planning, the uncertainty of projects and situations is always
yet to be determined and can cause major problems.
In the article, there are different proposals as how to handle uncertainty
in production models. There are many and diverse topics discussed by each economist
each laid out in a table. Fuzzy modeling is defined as ..."an approach
based on the fuzzy set theory (Bellman and Zadeh, 1970). In this approach a
distinction is made between randomness and imprecision." Fuzziness is
discussed at length in this article. What does it mean? According to the article,
The authors question the use of the probabilistic approach, as in their view
imprecision does not equate to randomness in many situations." Imprecision
is not random in their thoughts. The definition is easy, but the solution is
challenging. Statistics are used heavily in the approach, with topics also
relating to methodologies, logistics and planning under uncertainty. While
uncertainty is impossible to remove (as stated by Mula et al., 2005)., the
research continues on how to improve its inconveniences.
This article is based upon different views and different tactics to plan under uncertainty. There are many, many, many, different processes to determine what to plan for. Uncertainty is the difference between the amount of information required to perform a task and the amount of information already possessed. The text state that there are many forms of uncertainty. It reviews 87 citations from 1987 until 2004. It goes over many models for different approaches and even shows graphs of these methods. In general this paper entails there are many ways to try and beat uncertainty, but there are no for sure ways to get around that little percentage of what’s going on. Although all of this research has been done thee will need to be further research and investigation of new approaches to uncertainty. There are five main points the author recommends further research. These are, investigations to new approaches to modeling of uncertainty, development of new models that contain additional sources and types of uncertainty, investigations incorporating all types of uncertainty in an integrated manner, development of works that compare the different modeling approaches with real case studies, and development of a comparative evaluation of the existent models for the different manufacturing systems. This is a basic sum up of this long article.
The paper looks at uncertainty in production planning models to aid in creating better planning decisions. Uncertainty has been categorized into two groups: environmental uncertainty and system uncertainty. This paper looks at system uncertainty which includes yield uncertainty, production lead time uncertainty and quality uncertainty all of which are a part of the production process. 87 citations were used from 1983 to 2004, which the reader can use in order to provide them with a “starting point” for modeling uncertainty in production planning. The two main criteria was used for selecting papers which include (1) models that focus on a 1-2 year planning horizon that incorporate items from both the strategic and operational models and (2) models that are applied to real-world problems with an emphasis on manufacturing systems. The paper concludes that uncertainty can never be completely removed and further research needs to be done in the areas of: (1)developing new approaches to modeling of uncertainty, (2) development of new models that contain additional sources and types of uncertainty, (3) investigation of incorporating all types of uncertainty in an integrated manner, (4) development of empirical works that compare the different modeling approaches with real case studies, and (5) comparative evaluation of existing models for different manufacturing systems.
Article 2 is all about uncertainty in the production process, whether it be environmental or systematic. The text breaks down examples of both such as: Environmental uncertainty is the uncertainty beyond the limits of the actual production process(ex. supply and demand); System uncertainty is the uncertainty within the production process(ex. operations yield, lead time, and quality). The article then discusses methods to review models of production planning in order to manage both environmental and systematic uncertainty. The classification scheme for review of production planning models can be broken down into two categories: Production planning and a modeling approach. Throughout the article there are 7 production planning categories mentioned and theses are aggregate planning, hierarchical planning, MRP, capacity planning, manufacturing resource planning, inventory management, and supply chain management. Along with the production planning methods, there are 4 different modeling approaches and these are conceptual, analytical, artificial intelligence, and simulation.Now that all of that is out of the way, the bulk of the article is basically a complex compilation of the studies and theories proposed by experts, and the testing of them in order to determine the best method for adapting and accounting for uncertainty in the planning process. Upon research and testing, the article concluded that analytical models only addressed only one type of uncertainty, and also assumed a simple structure of the production process. For more complex processes, those containing a variety of final products and more than one type of uncertainty involved, the best approach for determining the uncertainty factor is the use of artificial intelligence and/or process simulation. It is impossible to completely remove uncertainty from a supply chain and production processes, but simulation can allow managers to plan accordingly and thus mold their production processes in to more agile systems. To make a long story short, managers that develop production process models that do not recognize uncertainty can be expected to make poor planning decisions, in contrast to production models that account for the always present degree of uncertainty in the production process.
This article makes the statement that production planning models that do not
account for uncertainty are inferior to those that do account for uncertainty.
The article reviews literature already published on the topic of production
planning under uncertainty from journals, proceedings, conferences, books and
published PhD dissertations.
There is a large amount of research about production planning under uncertainty.
The article reviews and classifies the literature in order to provide a starting
point for investigating the massive amount of research that already exists
on this subject. The authors selected research that used 1-2 year planning
horizons in scope, and that were applied to mainly manufacturing systems. The
article introduces a classification scheme for production planning models based
on two aspects – the production planning area and the modeling approach.
Seven major categories of production planning are defined in the article, and
these are broken down further by four different modeling approaches, listed
below.
One author, Galbraith (1973), defines uncertainty as “the difference
between the amount of information required to perform a task and the amount
of information already possessed.” Ho (1989), categorizes uncertainty
into two groups – environmental, including uncertainties that are beyond
the production process such as supply and demand, and system uncertainty, related
to uncertainty in the production process such as yield, lead time, quality
and changes to products. These two classifications are referred to throughout
the article.
The authors present varying research on the different models, but conclude
that fuzzy set theory, used with artificial intelligence models, is an appropriate
method that can offer great advantages to accounting for uncertainty in current
production planning systems. The authors also identify a need for additional
research, development of new and more accurate models, a way of integrating
all types of uncertainty, a look at real case studies, and a comparison of
the models for different manufacturing systems.
Production Planning Research Briefed in Article:
Conceptual models:
•
Supply chain planning
•
Material requirement planning
Analytical models:
•
Hierarchical production planning
•
Material requirement planning
•
Capacity planning
•
Manufacturing resource planning
•
Supply chain planning
Artificial intelligence models:
• Aggregate planning
• Material requirement planning
• Manufacturing resource planning
• Inventory management
• Supply chain planning
Simulation models:
• Aggregate planning
• Material requirement planning
• Capacity planning
• Manufacturing resource planning
The authors of the article are using about 87 resources to give a better view of how to manage uncertainty in product planning and to provide a base for future research. After defining uncertainty, the authors are taking a set of supply chain concepts like material requirement planning, supply chain planning, hierarchical production planning, capacity planning, manufacturing resource planning, aggregate planning, and inventory management, and classify them for the purpose of review. The categories of models for production planning under uncertainty that the authors use as the base of the analysis are: conceptual models, analytical models, artificial intelligence models, and simulation models. The conclusion of the paper is that uncertainty is impossible to be completely removed from supply chains, and also from each section of the supply chain. It also concludes that optimization of productions under uncertainty are very complex. Even though it can be complex, implementation of such studies can help supply chains to be more agile.